From cfa2db86414b5271bf5ed5814ac267b5276f9805 Mon Sep 17 00:00:00 2001 From: Manushi Majumdar Date: Tue, 13 Aug 2024 23:43:04 -0400 Subject: [PATCH] deleting samples that use geoanalytics --- .../analyze_new_york_city_taxi_data.ipynb | 1143 ------ ..._hurricane_tracks_using_geoanalytics.ipynb | 758 ---- ...ering_using_geoanalytics_and_pyspark.ipynb | 3368 ----------------- ...asting_pm2.5_using_big_data_analysis.ipynb | 1 - .../part1_prepare_hurricane_data.ipynb | 1 - ...re_analysis_using_sentinel-2_imagery.ipynb | 858 ----- 6 files changed, 6129 deletions(-) delete mode 100644 samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb delete mode 100644 samples/04_gis_analysts_data_scientists/creating_hurricane_tracks_using_geoanalytics.ipynb delete mode 100644 samples/04_gis_analysts_data_scientists/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.ipynb delete mode 100644 samples/04_gis_analysts_data_scientists/forecasting_pm2.5_using_big_data_analysis.ipynb delete mode 100644 samples/04_gis_analysts_data_scientists/part1_prepare_hurricane_data.ipynb delete mode 100644 samples/04_gis_analysts_data_scientists/wildfire_analysis_using_sentinel-2_imagery.ipynb diff --git a/samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb b/samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb deleted file mode 100644 index 688d10f963..0000000000 --- a/samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb +++ /dev/null @@ -1,1143 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analyzing New York City taxi data using big data tools\n", - "\n", - "At 10.5 and later releases, ArcGIS Enterprise introduces [ArcGIS GeoAnalytics Server](http://server.arcgis.com/en/server/latest/get-started/windows/what-is-arcgis-geoanalytics-server-.htm) which provides you the ability to perform big data analysis on your infrastructure. This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python.\n", - "\n", - "The data used in this sample can be downloaded from [NYC Taxi & Limousine Commission website](http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml). For this sample, data for the months January & Febuary of 2015 were used, each averaging 12 million records.\n", - "\n", - "**Note**: The ability to perform big data analysis is only available on ArcGIS Enterprise 10.5 licensed with a GeoAnalytics server and not yet available on ArcGIS Online." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "toc": true - }, - "source": [ - "

Table of Contents

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" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The NYC taxi data\n", - "\n", - "To give you an overview, let us take a look at a subset with 2000 points published as a feature service." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - " NYC taxi subset\n", - " \n", - "
Feature Layer Collection by api_data_owner\n", - "
Last Modified: May 17, 2019\n", - "
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\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import arcgis\n", - "from arcgis.gis import GIS\n", - "\n", - "ago_gis = GIS() # Connect to ArcGIS Online as an anonymous user\n", - "search_subset = ago_gis.content.search(\"NYC_taxi_subset\", item_type = \"Feature Layer\")\n", - "subset_item = search_subset[0]\n", - "subset_item" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us bring up a map to display the data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subset_map = ago_gis.map(\"New York, NY\", zoomlevel=11)\n", - "subset_map" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "subset_map.add_layer(subset_item)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us access the feature layers using the `layers` property. We can select a specific layer from the laters list and explore its attribute table to understand the structure of our data. In the cell below, we use the feature layer's [`query()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method to return the layer attribute information. The `query()` method returns a [`FeatureSet`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#featureset) object, which is a collection of individual [`Feature`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#feature) objects.\n", - "\n", - "You can mine through the `FeatureSet` to inspect each individual `Feature`, read its attribute information and then compose a table of all features and their attributes. However, the `FeatureSet` object provides a much easier and more direct way to get that information. Using the [`df`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureSet.df) property of a `FeatureSet`, you can load the attribute information as a [`pandas`](https://pandas.pydata.org/) [`dataframe`](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) object.\n", - "\n", - "If you installed the ArcGIS API for Python through ArcGIS Pro or with the `conda install` command, you have the api and its dependencies, including the `pandas` package. The [`df`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureSet.df) property will return a `dataframe`. If you [installed without dependences](https://developers.arcgis.com/python/guide/install-and-set-up/#Install-without-Dependencies), you need to install the `pandas` Python package for the `df` property to return a dataframe. If you get an error that pandas cannot be found, you can install it by typing the following in your terminal that is running the jupyter notebook:\n", - "\n", - " conda install pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Field1OBJECTIDRateCodeIDVendorIDdropoff_latitudedropoff_longitudeextrafare_amountimprovement_surchargemta_tax...payment_typepickup_latitudepickup_longitudestore_and_fwd_flagtip_amounttolls_amounttotal_amounttpep_dropoff_datetimetpep_pickup_datetimetrip_distance
0347932011240.782318-73.9804920.09.50.30.5...140.778149-73.956291N2.1012.41970-01-01 00:23:42.2689431970-01-01 00:23:42.2682181.76
1847334221240.769756-73.9506000.513.50.30.5...240.729458-73.983864N0.0014.81970-01-01 00:23:42.1375771970-01-01 00:23:42.1368923.73
21086437431240.753040-73.9856800.014.50.30.5...240.743740-73.987617N0.0015.31970-01-01 00:23:42.7199061970-01-01 00:23:42.7187112.84
3735009441240.765743-73.9549940.011.50.30.5...240.757507-73.981682N0.0012.31970-01-01 00:23:40.9075581970-01-01 00:23:40.9066012.18
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4 rows × 21 columns

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" - ], - "text/plain": [ - " Field1 OBJECTID RateCodeID VendorID dropoff_latitude \\\n", - "0 3479320 1 1 2 40.782318 \n", - "1 8473342 2 1 2 40.769756 \n", - "2 10864374 3 1 2 40.753040 \n", - "3 7350094 4 1 2 40.765743 \n", - "\n", - " dropoff_longitude extra fare_amount improvement_surcharge mta_tax \\\n", - "0 -73.980492 0.0 9.5 0.3 0.5 \n", - "1 -73.950600 0.5 13.5 0.3 0.5 \n", - "2 -73.985680 0.0 14.5 0.3 0.5 \n", - "3 -73.954994 0.0 11.5 0.3 0.5 \n", - "\n", - " ... payment_type pickup_latitude pickup_longitude \\\n", - "0 ... 1 40.778149 -73.956291 \n", - "1 ... 2 40.729458 -73.983864 \n", - "2 ... 2 40.743740 -73.987617 \n", - "3 ... 2 40.757507 -73.981682 \n", - "\n", - " store_and_fwd_flag tip_amount tolls_amount total_amount \\\n", - "0 N 2.1 0 12.4 \n", - "1 N 0.0 0 14.8 \n", - "2 N 0.0 0 15.3 \n", - "3 N 0.0 0 12.3 \n", - "\n", - " tpep_dropoff_datetime tpep_pickup_datetime trip_distance \n", - "0 1970-01-01 00:23:42.268943 1970-01-01 00:23:42.268218 1.76 \n", - "1 1970-01-01 00:23:42.137577 1970-01-01 00:23:42.136892 3.73 \n", - "2 1970-01-01 00:23:42.719906 1970-01-01 00:23:42.718711 2.84 \n", - "3 1970-01-01 00:23:40.907558 1970-01-01 00:23:40.906601 2.18 \n", - "\n", - "[4 rows x 21 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subset_feature_layer = subset_item.layers[0]\n", - "\n", - "# query the attribute information. Limit to first 5 rows.\n", - "query_result = subset_feature_layer.query(where = 'OBJECTID < 5',\n", - " out_fields = \"*\", \n", - " returnGeometry = False)\n", - "\n", - "att_data_frame = query_result.sdf # get as a Pandas dataframe\n", - "att_data_frame" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The table above represents the attribute information available from the NYC dataset. Columns provide a wealth of infomation such as pickup and dropoff_locations, fares, tips, tolls, and trip distances which you can analyze to observe many interesting patterns. The full data dataset contains over 24 million points. To discern patterns out of it, let us aggregate the points into blocks of 1 square kilometer." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Searching for big data file shares" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To process data using GeoAnalytics Server, you need to have registered the data with your Geoanalytics Server. In this sample the data is in multiple csv files, which have been previously registered as a big data file share.\n", - "\n", - "Let us connect to an ArcGIS Enterprise." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "gis = GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ensure that the Geoanalytics [is supported](https://developers.arcgis.com/python/api-reference/arcgis.geoanalytics.toc.html#arcgis.geoanalytics.is_supported) with our GIS." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arcgis.geoanalytics.is_supported()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the geoanalytics datastores and search it for the registered datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "datastores = arcgis.geoanalytics.get_datastores()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bigdata_fileshares = datastores.search(id='0e7a861d-c1c5-4acc-869d-05d2cebbdbee')\n", - "bigdata_fileshares" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "GA_Data is registered as a `big data file share` with the Geoanalytics datastore, so we can reference it:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "data_item = bigdata_fileshares[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Registering big data file shares\n", - "\n", - "The code below shows how a big data file share can be registered with the geoanalytics datastores, in case it's not already registered." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Big Data file share exists for NYCdata\n" - ] - } - ], - "source": [ - "# data_item = datastores.add_bigdata(\"NYCdata\", r\"\\\\pathway\\to\\data\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once a big data file share is created, the GeoAnalytics server processes all the valid file types to discern the schema of the data. This process can take a few minutes depending on the size of your data. Once processed, querying the [`manifest`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html?highlight=manifest#arcgis.gis.Datastore.manifest) property returns the schema. As you can see from below, the schema is similar to the subset we observed earlier in this sample." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'datasets': [{'name': 'air_quality',\n", - " 'format': {'quoteChar': '\"',\n", - " 'fieldDelimiter': ',',\n", - " 'hasHeaderRow': True,\n", - " 'encoding': 'UTF-8',\n", - " 'escapeChar': '\"',\n", - " 'recordTerminator': '\\n',\n", - " 'type': 'delimited',\n", - " 'extension': 'csv'},\n", - " 'schema': {'fields': [{'name': 'State Code',\n", - " 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'County Code', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Site Num', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Parameter Code', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'POC', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Latitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'Longitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'Datum', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Parameter Name', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Date Local', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Time Local', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Date GMT', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Time GMT', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Sample Measurement', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'Units of Measure', 'type': 'esriFieldTypeString'},\n", - " {'name': 'MDL', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'Uncertainty', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Qualifier', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Method Type', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Method Code', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Method Name', 'type': 'esriFieldTypeString'},\n", - " {'name': 'State Name', 'type': 'esriFieldTypeString'},\n", - " {'name': 'County Name', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Date of Last Change', 'type': 'esriFieldTypeString'}]},\n", - " 'geometry': {'geometryType': 'esriGeometryPoint',\n", - " 'spatialReference': {'wkid': 4326},\n", - " 'fields': [{'name': 'Longitude', 'formats': ['x']},\n", - " {'name': 'Latitude', 'formats': ['y']}]},\n", - " 'time': {'timeType': 'instant',\n", - " 'timeReference': {'timeZone': 'UTC'},\n", - " 'fields': [{'name': 'Date Local', 'formats': ['yyyy-MM-dd']}]}},\n", - " {'name': 'crime',\n", - " 'format': {'quoteChar': '\"',\n", - " 'fieldDelimiter': ',',\n", - " 'hasHeaderRow': True,\n", - " 'encoding': 'UTF-8',\n", - " 'escapeChar': '\"',\n", - " 'recordTerminator': '\\n',\n", - " 'type': 'delimited',\n", - " 'extension': 'csv'},\n", - " 'schema': {'fields': [{'name': 'ID', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Case Number', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Date', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Block', 'type': 'esriFieldTypeString'},\n", - " {'name': 'IUCR', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Primary Type', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Description', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Location Description', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Arrest', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Domestic', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Beat', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'District', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Ward', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Community Area', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'FBI Code', 'type': 'esriFieldTypeString'},\n", - " {'name': 'X Coordinate', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Y Coordinate', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Year', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Updated On', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Latitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'Longitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'Location', 'type': 'esriFieldTypeString'}]},\n", - " 'geometry': {'geometryType': 'esriGeometryPoint',\n", - " 'spatialReference': {'wkid': 4326},\n", - " 'fields': [{'name': 'Location', 'formats': ['({y},{x})']}]},\n", - " 'time': {'timeType': 'instant',\n", - " 'timeReference': {'timeZone': 'UTC'},\n", - " 'fields': [{'name': 'Date', 'formats': ['MM/dd/yyyy hh:mm:ss a']}]}},\n", - " {'name': 'calls',\n", - " 'format': {'quoteChar': '\"',\n", - " 'fieldDelimiter': ',',\n", - " 'hasHeaderRow': True,\n", - " 'encoding': 'UTF-8',\n", - " 'escapeChar': '\"',\n", - " 'recordTerminator': '\\n',\n", - " 'type': 'delimited',\n", - " 'extension': 'csv'},\n", - " 'schema': {'fields': [{'name': 'NOPD_Item', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Type_', 'type': 'esriFieldTypeString'},\n", - " {'name': 'TypeText', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Priority', 'type': 'esriFieldTypeString'},\n", - " {'name': 'MapX', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'MapY', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'TimeCreate', 'type': 'esriFieldTypeString'},\n", - " {'name': 'TimeDispatch', 'type': 'esriFieldTypeString'},\n", - " {'name': 'TimeArrive', 'type': 'esriFieldTypeString'},\n", - " {'name': 'TimeClosed', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Disposition', 'type': 'esriFieldTypeString'},\n", - " {'name': 'DispositionText', 'type': 'esriFieldTypeString'},\n", - " {'name': 'BLOCK_ADDRESS', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Zip', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'PoliceDistrict', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Location', 'type': 'esriFieldTypeString'}]},\n", - " 'time': {'timeType': 'instant',\n", - " 'timeReference': {'timeZone': 'UTC'},\n", - " 'fields': [{'name': 'TimeDispatch', 'formats': ['epoch_millis']}]}},\n", - " {'name': 'analyze_new_york_city_taxi_data',\n", - " 'format': {'quoteChar': '\"',\n", - " 'fieldDelimiter': ',',\n", - " 'hasHeaderRow': True,\n", - " 'encoding': 'UTF-8',\n", - " 'escapeChar': '\"',\n", - " 'recordTerminator': '\\n',\n", - " 'type': 'delimited',\n", - " 'extension': 'csv'},\n", - " 'schema': {'fields': [{'name': 'VendorID',\n", - " 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'tpep_pickup_datetime', 'type': 'esriFieldTypeString'},\n", - " {'name': 'tpep_dropoff_datetime', 'type': 'esriFieldTypeString'},\n", - " {'name': 'passenger_count', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'trip_distance', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'pickup_longitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'pickup_latitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'RateCodeID', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'store_and_fwd_flag', 'type': 'esriFieldTypeString'},\n", - " {'name': 'dropoff_longitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'dropoff_latitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'payment_type', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'fare_amount', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'extra', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'mta_tax', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'tip_amount', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'tolls_amount', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'improvement_surcharge', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'total_amount', 'type': 'esriFieldTypeDouble'}]},\n", - " 'geometry': {'geometryType': 'esriGeometryPoint',\n", - " 'spatialReference': {'wkid': 4326},\n", - " 'fields': [{'name': 'pickup_longitude', 'formats': ['x']},\n", - " {'name': 'pickup_latitude', 'formats': ['y']}]},\n", - " 'time': {'timeType': 'instant',\n", - " 'timeReference': {'timeZone': 'UTC'},\n", - " 'fields': [{'name': 'tpep_pickup_datetime',\n", - " 'formats': ['yyyy-MM-dd HH:mm:ss']}]}}]}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_item.manifest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since this big data file share has multiple datasets, let's check the manifest for the taxi dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'analyze_new_york_city_taxi_data',\n", - " 'format': {'quoteChar': '\"',\n", - " 'fieldDelimiter': ',',\n", - " 'hasHeaderRow': True,\n", - " 'encoding': 'UTF-8',\n", - " 'escapeChar': '\"',\n", - " 'recordTerminator': '\\n',\n", - " 'type': 'delimited',\n", - " 'extension': 'csv'},\n", - " 'schema': {'fields': [{'name': 'VendorID', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'tpep_pickup_datetime', 'type': 'esriFieldTypeString'},\n", - " {'name': 'tpep_dropoff_datetime', 'type': 'esriFieldTypeString'},\n", - " {'name': 'passenger_count', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'trip_distance', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'pickup_longitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'pickup_latitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'RateCodeID', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'store_and_fwd_flag', 'type': 'esriFieldTypeString'},\n", - " {'name': 'dropoff_longitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'dropoff_latitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'payment_type', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'fare_amount', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'extra', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'mta_tax', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'tip_amount', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'tolls_amount', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'improvement_surcharge', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'total_amount', 'type': 'esriFieldTypeDouble'}]},\n", - " 'geometry': {'geometryType': 'esriGeometryPoint',\n", - " 'spatialReference': {'wkid': 4326},\n", - " 'fields': [{'name': 'pickup_longitude', 'formats': ['x']},\n", - " {'name': 'pickup_latitude', 'formats': ['y']}]},\n", - " 'time': {'timeType': 'instant',\n", - " 'timeReference': {'timeZone': 'UTC'},\n", - " 'fields': [{'name': 'tpep_pickup_datetime',\n", - " 'formats': ['yyyy-MM-dd HH:mm:ss']}]}}" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_item.manifest['datasets'][3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Performing data aggregation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you add a big data file share datastore, a corresponding item gets created on your portal. You can search for it like a regular item and query its layers." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "search_result = gis.content.search(\"bigDataFileShares_GA_Data\", item_type = \"big data file share\")\n", - "search_result" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Big Data File Share by arcgis_python\n", - "
Last Modified: May 27, 2021\n", - "
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\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_item = search_result[0]\n", - "data_item" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_item.layers" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "year_2015 = data_item.layers[3]\n", - "year_2015" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Aggregate points tool\n", - "You access the [`aggregate_points()`](https://developers.arcgis.com/python/api-reference/arcgis.geoanalytics.summarize_data.html#aggregate-points) tool in the [`summarize_data`](https://developers.arcgis.com/python/api-reference/arcgis.geoanalytics.summarize_data.html#) submodule of the [`geoanalytics`](https://developers.arcgis.com/python/api-reference/arcgis.geoanalytics.toc.html) module. In this example, we are using this tool to aggregate the numerous points into 1 kilometer square blocks. The tool creates a polygon feature layer in which each polygon contains aggregated attribute information from all the points in the input dataset that fall within that polygon. The output feature layer contains only polygons that contain at least one point from the input dataset. See [Aggregate Points](https://enterprise.arcgis.com/en/portal/latest/use/geoanalyticstool-aggregatepoints.htm) for details on using this tool." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "from arcgis.geoanalytics.summarize_data import aggregate_points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The aggregate points tool requires that either:\n", - " * the point layer is projected, or\n", - " * the output or processing coordinate system is set to a Projected Coordinate System\n", - "\n", - "We can query the layer `properties` to investigate the coordinate system of the point layer:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'wkid': 4326}" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "year_2015.properties['spatialReference']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since WGS84 (the coordinate system referred to by wkid 4326) is unprojected, we can use the [`arcgis.env`]() module to set the environment used in the tool processing. The [`process_spatial_reference`](https://developers.arcgis.com/python/api-reference/arcgis.env.html#arcgis.env.process_spatial_reference) environment setting controls the geometry processing of tools used by the API for Python. We can set this parameter to a projected coordinate system for tool processing:\n", - "\n", - "> **NOTE:** Aggregate Points requires that your area layer is in a projected coordinate system. See the `Usage notes` section of the [`help`](https://enterprise.arcgis.com/en/portal/latest/use/geoanalyticstool-aggregatepoints.htm#Usage-notes) for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "arcgis.env.process_spatial_reference=3857" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the [`arcgis.env`](https://developers.arcgis.com/python/api-reference/arcgis.env.html) module to modify environment settings that geoprocessing and geoanalytics tools use during execution. Set [`verbose`](https://developers.arcgis.com/python/api-reference/arcgis.env.html#verbose) to `True` to return detailed messaging when running tools:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "arcgis.env.verbose = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's run the tool, specifying 1 kilometer squares as the polygons for which we want to aggregate information about all the NYC taxi information in each of those polygons:" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Submitted.\n", - "Executing...\n", - "Executing (AggregatePoints): AggregatePoints \"Feature Set\" Square 1 Kilometers # # # # # # # \"{\"serviceProperties\": {\"name\": \"Aggregate_Points_Analysis_S22I46\", \"serviceUrl\": \"https://pythonapi.playground.esri.com/server/rest/services/Hosted/Aggregate_Points_Analysis_S22I46/FeatureServer\"}, \"itemProperties\": {\"itemId\": \"b3b25331e78d4da487da7e38e208655d\"}}\" \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 3857}}\"\n", - "Start Time: Thu May 27 04:31:18 2021\n", - "Using URL based GPRecordSet param: https://pythonapi.playground.esri.com/ga/rest/services/DataStoreCatalogs/bigDataFileShares_GA_Data/BigDataCatalogServer/analyze_new_york_city_taxi_data\n", - "{\"messageCode\":\"BD_101033\",\"message\":\"'pointLayer' will be projected into the processing spatial reference.\",\"params\":{\"paramName\":\"pointLayer\"}}\n", - "{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 280 tasks.\",\"params\":{\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"0/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"1/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"13/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"13\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"25/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"25\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"33/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"33\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"43/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"43\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"53/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"53\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"61/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"61\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"71/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"71\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"79/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"79\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"90/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"90\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"99/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"99\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"109/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"109\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"119/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"119\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"127/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"127\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"134/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"134\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"162/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"162\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"268/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"268\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101029\",\"message\":\"280/280 distributed tasks completed.\",\"params\":{\"completedTasks\":\"280\",\"totalTasks\":\"280\"}}\n", - "{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}\n", - "{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 4855\",\"params\":{\"resultCount\":\"4855\"}}\n", - "{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = {\\\"xmin\\\":-140.5863419647051,\\\"ymin\\\":-27.777893572271292,\\\"xmax\\\":78.66546943034649,\\\"ymax\\\":82.49801821869467}\",\"params\":{\"extent\":\"{\\\"xmin\\\":-140.5863419647051,\\\"ymin\\\":-27.777893572271292,\\\"xmax\\\":78.66546943034649,\\\"ymax\\\":82.49801821869467}\"}}\n", - "{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = None\",\"params\":{\"extent\":\"None\"}}\n", - "{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://pythonapi.playground.esri.com/server/rest/services/Hosted/Aggregate_Points_Analysis_S22I46/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://pythonapi.playground.esri.com/server/rest/services/Hosted/Aggregate_Points_Analysis_S22I46/FeatureServer/0\"}}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "{\"messageCode\":\"BD_101054\",\"message\":\"Some records have either missing or invalid geometries.\"}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"messageCode\":\"BD_101054\",\"message\":\"Some records have either missing or invalid geometries.\"}\n", - "Succeeded at Thu May 27 04:33:07 2021 (Elapsed Time: 1 minutes 49 seconds)\n", - "AggregatePoints GP Job: j7fb3186c1a174df5abd5fb1a806c0e76 finished successfully.\n" - ] - } - ], - "source": [ - "agg_result = aggregate_points(year_2015, \n", - " bin_type='square',\n", - " bin_size=1, \n", - " polygon_layer='', \n", - " bin_size_unit='Kilometers')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Inspect the results\n", - "\n", - "Let us create a map and load the processed result which is a feature layer item." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "processed_map = gis.map('New York, NY', 11)\n", - "processed_map" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "processed_map.add_layer(agg_result)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default the item we just created is not shared, so additinal processing requires login credentials. Let's [`share()`] the item to avoid this." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'notSharedWith': [], 'itemId': '5cf0d7a3676f496fbcb9f9d460242583'}" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agg_result.share(org=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us inspect the analysis result using smart mapping. To learn more about this visualization capability, refer to the guide on [Smart Mapping](https://developers.arcgis.com/python/guide/smart-mapping/) under the 'Mapping and Visualization' section." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "map2 = gis.map(\"New York, NY\", 11)\n", - "map2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "map2.add_layer(agg_result, {\n", - " \"renderer\":\"ClassedColorRenderer\",\n", - " \"field_name\":\"MAX_tip_amount\", \n", - " \"normalizationField\":'MAX_trip_distance',\n", - " \"classificationMethod\":'natural-breaks',\n", - " \"opacity\":0.75\n", - " })" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now start seeing patterns, such as which pickup areas resulted in higher tips for the cab drivers." - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "esriNotebookRuntime": { - "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", - "notebookRuntimeVersion": "9.0" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": true, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/samples/04_gis_analysts_data_scientists/creating_hurricane_tracks_using_geoanalytics.ipynb b/samples/04_gis_analysts_data_scientists/creating_hurricane_tracks_using_geoanalytics.ipynb deleted file mode 100644 index d38108ff82..0000000000 --- a/samples/04_gis_analysts_data_scientists/creating_hurricane_tracks_using_geoanalytics.ipynb +++ /dev/null @@ -1,758 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating hurricane tracks using Geoanalytics" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "toc": true - }, - "source": [ - "

Table of Contents

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" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The sample code below uses big data analytics (GeoAnalytics) to reconstruct hurricane tracks using data registered on a big data file share in the GIS. Note that this functionality is currently available on ArcGIS Enterprise 10.5 and not yet with ArcGIS Online.\n", - "\n", - "## Reconstruct tracks\n", - "Reconstruct tracks is a type of data aggregation tool available in the `arcgis.geoanalytics` module. This tool works with a layer of point features or polygon features that are time enabled. It first determines which points belong to a track using an identification number or identification string. Using the time at each location, the tracks are ordered sequentially and transformed into a line representing the path of movement.\n", - "\n", - "## Data used\n", - "For this sample, hurricane data from over a period of 50 years, totalling about 150,000 points split into 5 shape files was used. The [National Hurricane Center](http://www.nhc.noaa.gov/gis/) provides similar datasets that can be used for exploratory purposes.\n", - "\n", - "To illustrate the nature of the data a subset was published as a feature service and can be visualized as below:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Hurricane_tracks_pointsFeature Layer Collection by arcgis_python\n", - "
Last Modified: May 27, 2021\n", - "
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\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from arcgis.gis import GIS\n", - "\n", - "#Let us connect to an ArcGIS Enterprise\n", - "gis = GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')\n", - "hurricane_pts = gis.content.get('ebdb876ca1a74cc89a81c3f8ee481e94')\n", - "hurricane_pts" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subset_map = gis.map(\"USA\")\n", - "subset_map" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "subset_map.add_layer(hurricane_pts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inspect the data attributes\n", - "Let us query the first layer in hurricane_pts and view its attribute table as a Pandas dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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serial_numseasonnumbasinsub_basinnameiso_timenaturelatitudelongitude...centerwind_wmo1pres_wmo1track_typesizeWindINSTANT_DATETIMEglobalidOBJECTIDSHAPE
01927265N1032519273NAMMNOT NAMED9/26/1927 0:00TS16.8-43.6...atcf32.263-100.000main35000350001927-09-26 00:00:00{2360A7E6-07CE-C8E9-B05C-1BE4CC0466AC}1{\"x\": -43.6, \"y\": 16.8, \"spatialReference\": {\"...
11978347S2004119792SIMM02S:ANGELE12/24/1978 12:00NR-22.036.4...reunion-100.000-100.000main001978-12-24 12:00:00{5263FA06-0416-E055-F3A2-7CF1F9F3CD21}2{\"x\": 36.4, \"y\": -22, \"spatialReference\": {\"wk...
21994362S1105419954SIMMCHRISTELLE12/31/1994 0:00TS-13.851.9...reunion15.93816.458main25000250001994-12-31 00:00:00{357134CE-40B1-75F9-C82C-5DE32098E161}3{\"x\": 51.9, \"y\": -13.8, \"spatialReference\": {\"...
32006093S15115200614SIWAHUBERT4/3/2006 12:00NR-14.4114.4...bom8.49023.063main25300253002006-04-03 12:00:00{4BA29993-3C4B-831A-AC79-4E940FD4D00D}4{\"x\": 114.4, \"y\": -14.4, \"spatialReference\": {...
41951009S1614019513SPEA09P1/23/1951 23:00NR-21.9143.0...bom-100.0004.181main001951-01-23 23:00:00{4BB38CC9-7C65-EDEB-9CE8-61CA9D948743}5{\"x\": 143, \"y\": -21.9, \"spatialReference\": {\"w...
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5 rows × 22 columns

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" - ], - "text/plain": [ - " serial_num season num basin sub_basin name iso_time \\\n", - "0 1927265N10325 1927 3 NA MM NOT NAMED 9/26/1927 0:00 \n", - "1 1978347S20041 1979 2 SI MM 02S:ANGELE 12/24/1978 12:00 \n", - "2 1994362S11054 1995 4 SI MM CHRISTELLE 12/31/1994 0:00 \n", - "3 2006093S15115 2006 14 SI WA HUBERT 4/3/2006 12:00 \n", - "4 1951009S16140 1951 3 SP EA 09P 1/23/1951 23:00 \n", - "\n", - " nature latitude longitude ... center wind_wmo1 pres_wmo1 track_type \\\n", - "0 TS 16.8 -43.6 ... atcf 32.263 -100.000 main \n", - "1 NR -22.0 36.4 ... reunion -100.000 -100.000 main \n", - "2 TS -13.8 51.9 ... reunion 15.938 16.458 main \n", - "3 NR -14.4 114.4 ... bom 8.490 23.063 main \n", - "4 NR -21.9 143.0 ... bom -100.000 4.181 main \n", - "\n", - " size Wind INSTANT_DATETIME globalid \\\n", - "0 35000 35000 1927-09-26 00:00:00 {2360A7E6-07CE-C8E9-B05C-1BE4CC0466AC} \n", - "1 0 0 1978-12-24 12:00:00 {5263FA06-0416-E055-F3A2-7CF1F9F3CD21} \n", - "2 25000 25000 1994-12-31 00:00:00 {357134CE-40B1-75F9-C82C-5DE32098E161} \n", - "3 25300 25300 2006-04-03 12:00:00 {4BA29993-3C4B-831A-AC79-4E940FD4D00D} \n", - "4 0 0 1951-01-23 23:00:00 {4BB38CC9-7C65-EDEB-9CE8-61CA9D948743} \n", - "\n", - " OBJECTID SHAPE \n", - "0 1 {\"x\": -43.6, \"y\": 16.8, \"spatialReference\": {\"... \n", - "1 2 {\"x\": 36.4, \"y\": -22, \"spatialReference\": {\"wk... \n", - "2 3 {\"x\": 51.9, \"y\": -13.8, \"spatialReference\": {\"... \n", - "3 4 {\"x\": 114.4, \"y\": -14.4, \"spatialReference\": {... \n", - "4 5 {\"x\": 143, \"y\": -21.9, \"spatialReference\": {\"w... \n", - "\n", - "[5 rows x 22 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hurricane_pts.layers[0].query(as_df=True).head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a data store\n", - "For the GeoAnalytics server to process your big data, it needs the data to be registered as a data store. In our case, the data is in multiple shape files and we will register the folder containing the files as a data store of type `bigDataFileShare`.\n", - "\n", - "Let us connect to an ArcGIS Enterprise" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "gis = GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the geoanalytics datastores and search it for the registered datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Query the data stores available\n", - "import arcgis\n", - "datastores = arcgis.geoanalytics.get_datastores()\n", - "\n", - "bigdata_fileshares = datastores.search(id='a215eebc-1bab-42d5-9aa0-45fe2549ba55')\n", - "bigdata_fileshares" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset `hurricanes_all` data is registered as a big data file share with the Geoanalytics datastore, so we can reference it:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "data_item = bigdata_fileshares[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If there is no big data file share for hurricane track data registered on the server, we can register one that points to the shared folder containing the shape files." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Big Data file share exists for Hurricane_tracks\n" - ] - } - ], - "source": [ - "# data_item = datastores.add_bigdata(\"Hurricane_tracks\", r\"\\\\path_to_hurricane_data\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once a big data file share is registered, the GeoAnalytics server processes all the valid file types to discern the schema of the data, including information about the geometry in a dataset. If the dataset is time-enabled, as is required to use some GeoAnalytics Tools, the manifest reports the necessary metadata about how time information is stored as well.\n", - "\n", - "This process can take a few minutes depending on the size of your data. Once processed, querying the manifest property returns the schema. As you can see from below, the schema is similar to the subset we observed earlier in this sample." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'hurricanes',\n", - " 'format': {'type': 'shapefile', 'extension': 'shp'},\n", - " 'schema': {'fields': [{'name': 'serial_num', 'type': 'esriFieldTypeString'},\n", - " {'name': 'season', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'num', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'basin', 'type': 'esriFieldTypeString'},\n", - " {'name': 'sub_basin', 'type': 'esriFieldTypeString'},\n", - " {'name': 'name', 'type': 'esriFieldTypeString'},\n", - " {'name': 'iso_time', 'type': 'esriFieldTypeString'},\n", - " {'name': 'nature', 'type': 'esriFieldTypeString'},\n", - " {'name': 'latitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'longitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'wind_wmo_', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'pres_wmo_', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'center', 'type': 'esriFieldTypeString'},\n", - " {'name': 'wind_wmo1', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'pres_wmo1', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'track_type', 'type': 'esriFieldTypeString'},\n", - " {'name': 'size', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Wind', 'type': 'esriFieldTypeBigInteger'}]},\n", - " 'geometry': {'geometryType': 'esriGeometryPoint',\n", - " 'spatialReference': {'wkid': 102682, 'latestWkid': 3452}},\n", - " 'time': {'timeType': 'instant',\n", - " 'timeReference': {'timeZone': 'UTC'},\n", - " 'fields': [{'name': 'iso_time',\n", - " 'formats': ['yyyy-MM-dd HH:mm:ss', 'MM/dd/yyyy HH:mm']}]}}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_item.manifest['datasets'][0] #for brevity only a portion is printed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Perform data aggregation using reconstruct tracks tool\n", - "\n", - "When you add a big data file share, a corresponding item gets created in your GIS. You can search for it like a regular item and query its layers." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "search_result = gis.content.search(\"bigDataFileShares_hurricanes_all\", item_type = \"big data file share\")\n", - "search_result" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - " bigDataFileShares_all_hurricanes\n", - " \n", - "
Big Data File Share by api_data_owner\n", - "
Last Modified: May 02, 2018\n", - "
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\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_item = search_result[0]\n", - "data_item" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "years_50 = data_item.layers[0]\n", - "years_50" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reconstruct tracks tool\n", - "\n", - "The `reconstruct_tracks()` function is available in the `arcgis.geoanalytics.summarize_data` module. In this example, we are using this tool to aggregate the numerous points into line segments showing the tracks followed by the hurricanes. The tool creates a feature layer item as an output which can be accessed once the processing is complete." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from arcgis.geoanalytics.summarize_data import reconstruct_tracks\n", - "from datetime import datetime as dt" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "{\"messageCode\":\"BD_101024\",\"message\":\"Using geodesic method for geographic coordinate system.\"}\n" - ] - } - ], - "source": [ - "agg_result = reconstruct_tracks(years_50, \n", - " track_fields='Serial_Num',\n", - " output_name='construct tracks test' + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inspect the results\n", - "Let us create a map and load the processed result which is a feature service" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "processed_map = gis.map(\"USA\")\n", - "processed_map" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "processed_map.add_layer(agg_result)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus we transformed a bunch of ponints into tracks that represents paths taken by the hurricanes over a period of 50 years. We can pull up another map and inspect the results a bit more closely" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our input data and the map widget is time enabled. Thus we can filter the data to represent the tracks from only the years 1860 to 1870" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "processed_map.set_time_extent('1860', '1870')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What can geoanalytics do for you?\n", - "\n", - "With this sample we just scratched the surface of what big data analysis can do for you. ArcGIS Enterprise at 10.5 packs a powerful set of tools that let you derive a lot of value from your data. You can do so by asking the right questions, for instance, a weather dataset such as this could be used to answer a few interesting questions such as\n", - " \n", - " - did the number of hurricanes per season increase over the years?\n", - " - give me the hurricanes that travelled longest distance\n", - " - give me the ones that stayed for longest time. Do we see a trend?\n", - " - how are wind speed and distance travelled correlated?\n", - " - my assets are located in a tornado corridor. How many times in the past century, was there a hurricane within 50 miles from my assets?\n", - " - my industry is dependent on tourism, which is heavily impacted by the vagaries of weather. From historical weather data, can I correlate my profits with major weather events? How well is my business insulated from freak weather events?\n", - " - over the years do we see any shifts in major weather events - do we notice a shift in when the hurricane season starts?\n", - " \n", - "The ArcGIS API for Python gives you a gateway to easily access the big data tools from your ArcGIS Enterprise. By combining it with other powerful libraries from the pandas and scipy stack and the rich visualization capabilities of the Jupyter notebook, you can extract a lot of value from your data, big or small." - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "esriNotebookRuntime": { - "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", - "notebookRuntimeVersion": "9.0" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": true, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "293.594px" - }, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/samples/04_gis_analysts_data_scientists/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.ipynb b/samples/04_gis_analysts_data_scientists/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.ipynb deleted file mode 100644 index e055add3fc..0000000000 --- a/samples/04_gis_analysts_data_scientists/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.ipynb +++ /dev/null @@ -1,3368 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Crime analysis and clustering using geoanalytics and pyspark.ml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* [Introduction](#Introduction)\n", - "* [Necessary Imports](#Necessary-Imports)\n", - "* [Connect to your ArcGIS Enterprise Organization](#Connect-to-your-ArcGIS-Enterprise-Organization)\n", - "* [Ensure your GIS supports GeoAnalytics](#Ensure-your-GIS-supports-GeoAnalytics)\n", - "* [Prepare the data](#Prepare-the-data)\n", - " * [Register a big data file share](#Register-a-big-data-file-share)\n", - "* [Get data for analysis](#Get-data-for-analysis)\n", - "* [Describe data](#Describe-data)\n", - "* [Analyze patterns](#Analyze-patterns)\n", - " * [Aggregate points](#Aggregate-points)\n", - " * [Calculate density](#Calculate-density)\n", - " * [Find hot spots](#Find-hot-spots)\n", - "* [Use Spark Dataframe and Run Python Script](#Use-Spark-Dataframe-and-Run-Python-Script)\n", - " * [Location of crime](#Location-of-crime)\n", - " * [Type of crime](#Type-of-crime)\n", - " * [Location of theft](#Location-of-theft)\n", - " * [Count of crime incidents by block group](#Count-of-crime-incidents-by-block-group)\n", - " * [Get crime types for a particular block group](#Get-crime-types-for-a-particular-block-group)\n", - " * [Crime distribution by the hour](#Crime-distribution-by-the-hour)\n", - " * [Big data machine learning using pyspark.ml](#Big-data-machine-learning-using-pyspark.ml)\n", - " * [Find the optimal number of clusters](#Find-the-optimal-number-of-clusters)\n", - " * [K-Means clustering](#K-Means-Clustering)\n", - "* [Conclusion](#Conclusion)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many of the poorest neighborhoods in the City of Chicago face violent crimes. With rapid increase in crime, amount of crime data is also increasing. Thus, there is a strong need to identify crime patterns in order to reduce its occurrence. Data mining using some of the most powerful tools available in ArcGIS API for Python is an effective way to analyze and detect patterns in data. Through this sample, we will demonstrate the utility of a number of geoanalytics tools including ``find_hot_spots``, ``aggregate_points`` and ``calculate_density`` to visually understand geographical patterns. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `pyspark module` available through ``run_python_script`` tool provides a collection of distributed analysis tools for data management, clustering, regression, and more. The ``run_python_script`` task automatically imports the `pyspark module` so you can directly interact with it. By calling this implementation of k-means in the ``run_python_script`` tool, we will cluster crime data into a predefined number of clusters. Such clusters are also useful in identifying crime patterns. \n", - "\n", - "Further, based on the results of the analysis, the segmented crime map can be used to help efficiently dispatch officers throughout a city." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Necessary Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "from datetime import datetime as dt\n", - "\n", - "import arcgis\n", - "import arcgis.geoanalytics\n", - "from arcgis.gis import GIS\n", - "from arcgis.geoanalytics.summarize_data import describe_dataset, aggregate_points\n", - "from arcgis.geoanalytics.analyze_patterns import calculate_density, find_hot_spots\n", - "from arcgis.geoanalytics.manage_data import clip_layer, run_python_script" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Connect to your ArcGIS Enterprise Organization" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "agol_gis = GIS('home')\n", - "gis = GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ensure your GIS supports GeoAnalytics" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before executing a tool, we need to ensure an ArcGIS Enterprise GIS is set up with a licensed GeoAnalytics server. To do so, call the [is_supported()](https://developers.arcgis.com/python/api-reference/arcgis.geoanalytics.toc.html#is-supported) method after connecting to your Enterprise portal. See the [Components of ArcGIS URLs](http://enterprise.arcgis.com/en/portal/latest/administer/linux/components-of-arcgis-urls.htm) documentation for details on the urls to enter in the [GIS](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html#gis) parameters based on your particular Enterprise configuration." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arcgis.geoanalytics.is_supported()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To register a file share or an HDFS, we need to format datasets as subfolders within a single parent folder and register the parent folder. This parent folder becomes a datastore, and each subfolder becomes a dataset. Our folder hierarchy would look like below:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn more about preparing your big data file share datasets [here](https://enterprise.arcgis.com/en/server/latest/get-started/windows/what-is-a-big-data-file-share.htm)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Register a big data file share" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `get_datastores()` method of the geoanalytics module returns a `DatastoreManager` object that lets you search for and manage the big data file share items as Python API `Datastore` objects on your GeoAnalytics server." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bigdata_datastore_manager = arcgis.geoanalytics.get_datastores()\n", - "bigdata_datastore_manager" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will register chicago crime data as a big data file share using the `add_bigdata()` function on a `DatastoreManager` object. \n", - "\n", - "When we register a directory, all subdirectories under the specified folder are also registered with the server. Always register the parent folder (for example, \\\\machinename\\mydatashare) that contains one or more individual dataset folders as the big data file share item. To learn more, see [register a big data file share](https://enterprise.arcgis.com/en/server/latest/manage-data/windows/registering-your-data-with-arcgis-server-using-manager.htm#ESRI_SECTION1_0D55682C9D6E48E7857852A9E2D5D189).\n", - "\n", - "Note: \n", - "You cannot browse directories in ArcGIS Server Manager. You must provide the full path to the folder you want to register, for example, \\\\myserver\\share\\bigdata. Avoid using local paths, such as C:\\bigdata, unless the same data folder is available on all nodes of the server site." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created Big Data file share for Chicago_Crime_2001_2020\n" - ] - } - ], - "source": [ - "# data_item = bigdata_datastore_manager.add_bigdata(\"Chicago_Crime_2001_2020\", r\"\\\\machine_name\\data\\chicago\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bigdata_fileshares = bigdata_datastore_manager.search(id='0e7a861d-c1c5-4acc-869d-05d2cebbdbee')\n", - "bigdata_fileshares" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "file_share_folder = bigdata_fileshares[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once a big data file share is created, the GeoAnalytics server samples the datasets to generate a [manifest](https://enterprise.arcgis.com/en/server/latest/get-started/windows/understanding-the-big-data-file-share-manifest.htm), which outlines the data schema and specifies any time and geometry fields. A query of the resulting manifest returns each dataset's schema. This process can take a few minutes depending on the size of your data. Once processed, querying the manifest property returns the schema of the datasets in your big data file share." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'crime',\n", - " 'format': {'quoteChar': '\"',\n", - " 'fieldDelimiter': ',',\n", - " 'hasHeaderRow': True,\n", - " 'encoding': 'UTF-8',\n", - " 'escapeChar': '\"',\n", - " 'recordTerminator': '\\n',\n", - " 'type': 'delimited',\n", - " 'extension': 'csv'},\n", - " 'schema': {'fields': [{'name': 'ID', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Case Number', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Date', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Block', 'type': 'esriFieldTypeString'},\n", - " {'name': 'IUCR', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Primary Type', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Description', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Location Description', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Arrest', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Domestic', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Beat', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'District', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Ward', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Community Area', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'FBI Code', 'type': 'esriFieldTypeString'},\n", - " {'name': 'X Coordinate', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Y Coordinate', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Year', 'type': 'esriFieldTypeBigInteger'},\n", - " {'name': 'Updated On', 'type': 'esriFieldTypeString'},\n", - " {'name': 'Latitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'Longitude', 'type': 'esriFieldTypeDouble'},\n", - " {'name': 'Location', 'type': 'esriFieldTypeString'}]},\n", - " 'geometry': {'geometryType': 'esriGeometryPoint',\n", - " 'spatialReference': {'wkid': 4326},\n", - " 'fields': [{'name': 'Location', 'formats': ['({y},{x})']}]},\n", - " 'time': {'timeType': 'instant',\n", - " 'timeReference': {'timeZone': 'UTC'},\n", - " 'fields': [{'name': 'Date', 'formats': ['MM/dd/yyyy hh:mm:ss a']}]}}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "manifest = file_share_folder.manifest['datasets'][1]\n", - "manifest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get data for analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adding a big data file share to the Geoanalytics server adds a corresponding big data file share item on the portal. We can search for these types of items using the ``item_type`` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "search_result = gis.content.search(\"bigDataFileShares_GA_Data\", item_type = \"big data file share\")\n", - "search_result" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "ga_item = search_result[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Big Data File Share by arcgis_python\n", - "
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block_groups_illinoisFeature Layer Collection by api_data_owner\n", - "
Last Modified: May 27, 2021\n", - "
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" - ], - "text/plain": [ - " ID Case_Number Date Block IUCR \\\n", - "0 8196694 HT430829 08/04/2011 02:10:00 AM 079XX S MERRILL AVE 520.0 \n", - "1 5139385 HM736684 11/22/2006 09:00:00 PM 019XX N MOHAWK ST 1310.0 \n", - "2 6257174 HP338636 05/16/2008 05:30:00 AM 108XX S LOWE AVE 915.0 \n", - "3 8518985 HV195817 01/20/2012 09:00:00 AM 047XX S KNOX AVE 840.0 \n", - "4 3930218 HL301854 04/17/2005 11:40:00 PM 039XX W ARMITAGE AVE 1220.0 \n", - "\n", - " Primary_Type Description Location_Description \\\n", - "0 ASSAULT AGGRAVATED:KNIFE/CUTTING INSTR RESIDENCE \n", - "1 CRIMINAL DAMAGE TO PROPERTY OTHER \n", - "2 MOTOR VEHICLE THEFT TRUCK, BUS, MOTOR HOME STREET \n", - "3 THEFT FINANCIAL ID THEFT: OVER $300 RESIDENCE \n", - "4 DECEPTIVE PRACTICE THEFT OF LOST/MISLAID PROP ALLEY \n", - "\n", - " Arrest Domestic ... Y_Coordinate Year Updated_On Latitude \\\n", - "0 true false ... 1852704.0 2011 02/10/2018 03:50:01 PM 41.750809 \n", - "1 false false ... 1913191.0 2006 02/10/2018 03:50:01 PM 41.917244 \n", - "2 false false ... 1832936.0 2008 02/28/2018 03:56:25 PM 41.696981 \n", - "3 false false ... 1872783.0 2012 02/10/2018 03:50:01 PM 41.806897 \n", - "4 true false ... 1912994.0 2005 02/28/2018 03:56:25 PM 41.917175 \n", - "\n", - " Longitude Location INSTANT_DATETIME \\\n", - "0 -87.572309 (41.750808511, -87.572308641) 2011-08-04 02:10:00 \n", - "1 -87.642423 (41.917243909, -87.642422501) 2006-11-22 21:00:00 \n", - "2 -87.638886 (41.696980545, -87.638886196) 2008-05-16 05:30:00 \n", - "3 -87.739467 (41.806896849, -87.739466549) 2012-01-20 09:00:00 \n", - "4 -87.725912 (41.917175309, -87.725912468) 2005-04-17 23:40:00 \n", - "\n", - " globalid OBJECTID \\\n", - "0 {25BA0BFD-A32B-802A-72C5-D8A698A3C06F} 1 \n", - "1 {A67F0D22-7EED-03EE-511A-49458AB189C7} 2 \n", - "2 {5FE25286-201F-EF1D-3D6F-ECF7AC8DA402} 3 \n", - "3 {F475734C-7CC7-06DC-75F3-B1D9D6D91D8E} 4 \n", - "4 {862B9571-2761-454E-56E4-F19124DCC584} 5 \n", - "\n", - " SHAPE \n", - "0 {'x': -87.572308641, 'y': 41.750808511, 'spati... \n", - "1 {'x': -87.642422501, 'y': 41.917243909, 'spati... \n", - "2 {'x': -87.638886196, 'y': 41.696980545, 'spati... \n", - "3 {'x': -87.739466549, 'y': 41.806896849, 'spati... \n", - "4 {'x': -87.725912468, 'y': 41.917175309, 'spati... \n", - "\n", - "[5 rows x 26 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sdf_slyr = description.sample_layer.query(as_df=True)\n", - "sdf_slyr.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m1 = gis.map('chicago')\n", - "m1" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "m1.add_layer(description.sample_layer)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "m1.legend = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze patterns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The GeoAnalytics Tools use a process spatial reference during execution. Analyses with square or hexagon bins require a projected coordinate system. We'll use 26771 as seen from http://epsg.io/?q=illinois%20kind%3APROJCRS." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "arcgis.env.process_spatial_reference = 26771 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Aggregate points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the `aggregate_points` method in the `arcgis.geoanalytics.summarize_data` submodule to group call features into individual block group features. The output polygon feature layer summarizes attribute information for all calls that fall within each block group. If no calls fall within a block group, that block group will not appear in the output.\n", - "\n", - "The GeoAnalytics Tools use a [process spatial reference](https://developers.arcgis.com/rest/services-reference/process-spatial-reference.htm) during execution. Analyses with square or hexagon bins require a projected coordinate system. We'll use the World Cylindrical Equal Area projection (WKID 54034) below. All results are stored in the spatiotemporal datastore of the Enterprise in the WGS 84 Spatial Reference.\n", - "\n", - "See the GeoAnalytics Documentation for a full explanation of [analysis environment settings](https://enterprise.arcgis.com/en/portal/latest/use/geoanalyticstool-useenvironmentsettings.htm)." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "{\"messageCode\":\"BD_101189\",\"message\":\"The GeoAnalytics job is waiting for resources and has not started yet. The job will automatically cancel after 10 minutes.\",\"params\":{\"minutes\":\"10\"}}\n", - "{\"messageCode\":\"BD_101189\",\"message\":\"The GeoAnalytics job is waiting for resources and has not started yet. The job will automatically cancel after 10 minutes.\",\"params\":{\"minutes\":\"10\"}}\n", - "{\"messageCode\":\"BD_101051\",\"message\":\"Possible issues were found while reading 'pointLayer'.\",\"params\":{\"paramName\":\"pointLayer\"}}\n", - "{\"messageCode\":\"BD_101054\",\"message\":\"Some records have either missing or invalid geometries.\"}\n" - ] - } - ], - "source": [ - "agg_result = aggregate_points(crime_lyr, \n", - " polygon_layer=blk_lyr,\n", - " output_name=\"aggregate results of crime\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - " aggregate_results_of_crime653441\n", - " \n", - "
aggregate_results_of_crime653441Feature Layer Collection by admin\n", - "
Last Modified: April 09, 2020\n", - "
0 comments, 0 views\n", - "
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The result is a layer of areas classified from least dense to most dense. In this example, we will create density map by aggregating points within a bin of 1 kilometer. To learn more. please see [here](https://developers.arcgis.com/rest/services-reference/calculate-density-geoanalytics.htm)." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "{\"messageCode\":\"BD_101051\",\"message\":\"Possible issues were found while reading 'inputLayer'.\",\"params\":{\"paramName\":\"inputLayer\"}}\n", - "{\"messageCode\":\"BD_101054\",\"message\":\"Some records have either missing or invalid geometries.\"}\n" - ] - } - ], - "source": [ - "cal_density = calculate_density(crime_lyr,\n", - " weight='Uniform',\n", - " bin_type='Square',\n", - " bin_size=1,\n", - " bin_size_unit=\"Kilometers\",\n", - " time_step_interval=1,\n", - " time_step_interval_unit=\"Years\",\n", - " time_step_repeat_interval=1,\n", - " time_step_repeat_interval_unit=\"Months\",\n", - " time_step_reference=dt(2001, 1, 1),\n", - " radius=1000,\n", - " radius_unit=\"Meters\",\n", - " area_units='SquareKilometers',\n", - " output_name=\"calculate density of crime\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m4 = gis.map('chicago')\n", - "m4" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "m4.add_layer(cal_density)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "m4.legend = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `find_hot_spots` tool analyzes point data and finds statistically significant spatial clustering of high (hot spots) and low (cold spots) numbers of incidents relative to the overall distribution of the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Find hot spots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `find_hot_spots` tool analyzes point data and finds statistically significant spatial clustering of high (hot spots) and low (cold spots) numbers of incidents relative to the overall distribution of the data." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "{\"messageCode\":\"BD_101051\",\"message\":\"Possible issues were found while reading 'pointLayer'.\",\"params\":{\"paramName\":\"pointLayer\"}}\n", - "{\"messageCode\":\"BD_101054\",\"message\":\"Some records have either missing or invalid geometries.\"}\n" - ] - } - ], - "source": [ - "hot_spots = find_hot_spots(crime_lyr, \n", - " bin_size=100,\n", - " bin_size_unit='Meters',\n", - " neighborhood_distance=250,\n", - " neighborhood_distance_unit='Meters',\n", - " output_name=\"get hot spot areas of crime\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m5 = gis.map('chicago')\n", - "m5" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "m5.add_layer(hot_spots)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "m5.legend = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The darkest red features indicate areas where you can state with 99 percent confidence that the clustering of crime features is not the result of random chance but rather of some other variable that might be worth investigating. Similarly, the darkest blue features indicate that the lack of crime incidents is most likely not just random, but with 90% certainty you can state it is because of some variable in those locations. Features that are beige do not represent statistically significant clustering; the number of crimes could very likely be the result of random processes and random chance in those areas." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Spark Dataframe and Run Python Script" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `run_python_script` method executes a Python script directly in an ArcGIS GeoAnalytics server site . The script can create an analysis pipeline by chaining together multiple GeoAnalytics tools without writing intermediate results to a data store. The tool can also distribute Python functionality across the GeoAnalytics server site.\n", - "\n", - "Geoanalytics Server installs a Python 3.6 environment that this tool uses. The environment includes `Spark 2.2.0`, the compute platform that distributes analysis across multiple cores of one or more machines in your GeoAnalytics Server site. The environment includes the `pyspark module` which provides a collection of distributed analysis tools for data management, clustering, regression, and more. The `run_python_script` task automatically imports the `pyspark module` so you can directly interact with it.\n", - "\n", - "When using the `geoanalytics` and pyspark packages, most functions return analysis results as Spark DataFrame memory structures. You can write these data frames to a data store or process them in a script. This lets you chain multiple geoanalytics and pyspark tools while only writing out the final result, eliminating the need to create any bulky intermediate result layers. For more details, click [here](https://developers.arcgis.com/rest/services-reference/using-webgis-layers-in-pyspark.htm)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The **Location Description** field represents areas with the most common crime locations. We will write a function to group our data by location description. This will help us count the number of crimes occurring at each location type." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "def groupby_description():\n", - " from datetime import datetime as dt\n", - " # crime data is stored in a feature service and accessed as a DataFrame via the layers object\n", - " df = layers[0]\n", - " # group the dataframe by Location Description field and count the number of crimes for each Location Description. \n", - " out = df.groupBy('Location Description').count()\n", - " # Write the final result to our datastore.\n", - " out.write.format(\"webgis\").save(\"groupby_location_description\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Executing (RunPythonScript): RunPythonScript \"def groupby_description():\\\\n from datetime import datetime as dt\\\\n # crime data is stored in a feature service and accessed as a DataFrame via the layers object\\\\n df = layers[0]\\\\n # group the dataframe by Location Description field and count the number of crimes for each Location Description. \\\\n out = df.groupBy(\\'Location Description\\').count()\\\\n # Write the final result to our datastore.\\\\n out.write.format(\"webgis\").save(\"groupby_location_description\" + str(dt.now().microsecond))\\\\n\\\\ngroupby_description()\" https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 26771}}\"'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Start Time: Thu Apr 9 18:21:15 2020'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Using URL based GPRecordSet param: https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 259 tasks.\",\"params\":{\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"18/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"18\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"41/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"41\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"60/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"60\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"259/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"259\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 181\",\"params\":{\"resultCount\":\"181\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://ndhagsb01.esri.com/gis/rest/services/Hosted/groupby_location_description595817/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://ndhagsb01.esri.com/gis/rest/services/Hosted/groupby_location_description595817/FeatureServer/0\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Succeeded at Thu Apr 9 18:22:03 2020 (Elapsed Time: 48.18 seconds)'}]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_python_script(code=groupby_description, layers=[crime_lyr])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The result is saved as a feature layer. We can Search for the saved item using the `search()` method. Providing the search keyword same as the name we used for writing the result will retrieve the layer." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "groupby_description = gis.content.search('groupby_location_description')[0]\n", - "groupby_description_lyr = groupby_description.tables[0] #retrieve table from the item\n", - "groupby_description_df = groupby_description_lyr.query(as_df=True) #read layer as dataframe\n", - "groupby_description_df.sort_values(by='count', ascending=False, inplace=True) #sort count field in decreasing order" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Location of crime" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "groupby_description_df[:10].plot(x='Location_Description', \n", - " y='count', kind='barh')\n", - "plt.xticks(\n", - " rotation=45,\n", - " horizontalalignment='center',\n", - " fontweight='light',\n", - " fontsize='medium',\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Street is the most frequent location for crime occurrance. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The **Primary Type** field contains the type for the crime. Let's investigate the most frequent type of crime in the Chicago by writing our own function:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "def groupby_texttype():\n", - " from datetime import datetime as dt\n", - " # crime data is stored in a feature service and accessed as a DataFrame via the layers object\n", - " df = layers[0]\n", - " # group the dataframe by TextType field and count the crime incidents for each crime type. \n", - " out = df.groupBy('Primary Type').count()\n", - " # Write the final result to our datastore.\n", - " out.write.format(\"webgis\").save(\"groupby_type_of_crime\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Executing (RunPythonScript): RunPythonScript \"def groupby_texttype():\\\\n from datetime import datetime as dt\\\\n # Calls data is stored in a feature service and accessed as a DataFrame via the layers object\\\\n df = layers[0]\\\\n # group the dataframe by TextType field and count the number of calls for each call type. \\\\n out = df.groupBy(\\'Primary Type\\').count()\\\\n # Write the final result to our datastore.\\\\n out.write.format(\"webgis\").save(\"groupby_type_of_crime\" + str(dt.now().microsecond))\\\\n\\\\ngroupby_texttype()\" https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 26771}}\"'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Start Time: Thu Apr 9 18:55:46 2020'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Using URL based GPRecordSet param: https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 259 tasks.\",\"params\":{\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"22/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"22\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"44/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"44\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"259/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"259\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 35\",\"params\":{\"resultCount\":\"35\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://ndhagsb01.esri.com/gis/rest/services/Hosted/groupby_type_of_crime538317/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://ndhagsb01.esri.com/gis/rest/services/Hosted/groupby_type_of_crime538317/FeatureServer/0\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Succeeded at Thu Apr 9 18:56:26 2020 (Elapsed Time: 39.68 seconds)'}]" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_python_script(code=groupby_texttype, layers=[crime_lyr])" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "groupby_texttype = gis.content.search('groupby_type_of_crime')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - " groupby_type_of_crime538317\n", - " \n", - "
Table Layer by admin\n", - "
Last Modified: April 09, 2020\n", - "
0 comments, 0 views\n", - "
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Primary_TypecountglobalidOBJECTID
0OFFENSE INVOLVING CHILDREN48412{4120ABC0-FE3A-BBE0-ABC7-B885EEB2D5D2}9
1STALKING3644{52CC61CF-D8DE-D67B-4FC3-C8DD5DB175DE}20
2PUBLIC PEACE VIOLATION49583{4E902E77-D398-72D5-1E3C-EECF4A77B90E}26
3OBSCENITY650{A304388C-A37A-505E-D403-A90F83B04A77}34
4ARSON11603{E65FA2C6-9678-F283-A7B3-E61A40B12674}52
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" - ], - "text/plain": [ - " Primary_Type count globalid \\\n", - "0 OFFENSE INVOLVING CHILDREN 48412 {4120ABC0-FE3A-BBE0-ABC7-B885EEB2D5D2} \n", - "1 STALKING 3644 {52CC61CF-D8DE-D67B-4FC3-C8DD5DB175DE} \n", - "2 PUBLIC PEACE VIOLATION 49583 {4E902E77-D398-72D5-1E3C-EECF4A77B90E} \n", - "3 OBSCENITY 650 {A304388C-A37A-505E-D403-A90F83B04A77} \n", - "4 ARSON 11603 {E65FA2C6-9678-F283-A7B3-E61A40B12674} \n", - "\n", - " OBJECTID \n", - "0 9 \n", - "1 20 \n", - "2 26 \n", - "3 34 \n", - "4 52 " - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "groupby_texttype_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "groupby_texttype_df.sort_values(by='count', ascending=False, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Type of crime" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ], - "text/plain": [ - " Primary_Type count globalid OBJECTID\n", - "12 THEFT 1493302 {0CBB34E2-58C8-7D0B-01B4-D3E9CE832DC9} 102" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "theft" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "def theft_description():\n", - " from datetime import datetime as dt\n", - " # crime data is stored in a feature service and accessed as a DataFrame via the layers object\n", - " df = layers[0]\n", - " df[df['Primary Type'] == 'THEFT']\n", - " out = df.groupBy('Location Description').count()\n", - " # Write the final result to our datastore.\n", - " out.write.format(\"webgis\").save(\"theft_description\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Executing (RunPythonScript): RunPythonScript \"def theft_description():\\\\n from datetime import datetime as dt\\\\n # Calls data is stored in a feature service and accessed as a DataFrame via the layers object\\\\n df = layers[0]\\\\n df[df[\\'Primary Type\\'] == \\'THEFT\\']\\\\n out = df.groupBy(\\'Location Description\\').count()\\\\n # Write the final result to our datastore.\\\\n out.write.format(\"webgis\").save(\"theft_description\" + str(dt.now().microsecond))\\\\n\\\\ntheft_description()\" https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 26771}}\"'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Start Time: Thu Apr 9 18:56:30 2020'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Using URL based GPRecordSet param: https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 259 tasks.\",\"params\":{\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"24/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"24\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"45/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"45\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"101/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"101\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"259/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"259\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 181\",\"params\":{\"resultCount\":\"181\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://ndhagsb01.esri.com/gis/rest/services/Hosted/theft_description406470/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://ndhagsb01.esri.com/gis/rest/services/Hosted/theft_description406470/FeatureServer/0\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Succeeded at Thu Apr 9 18:57:11 2020 (Elapsed Time: 41.21 seconds)'}]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_python_script(code=theft_description, layers=[crime_lyr])" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "theft_description = gis.content.search('theft_description')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "theft_description_df = theft_description.tables[0].query(as_df=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "theft_description_df.sort_values(by='count', ascending=False, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Location of theft" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "theft_description_df[:10].plot(x='Location_Description', y='count', kind='barh')\n", - "plt.xticks(\n", - " rotation=45,\n", - " horizontalalignment='center',\n", - " fontweight='light',\n", - " fontsize='medium',\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This plot shows the relation between crime type and crime location. It indicates that most of the theft activities occur on streets." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "def grpby_type_blkgrp():\n", - " from datetime import datetime as dt\n", - " # Load the big data file share layer into a DataFrame\n", - " df = layers[0]\n", - " out = df.groupBy('Primary Type', 'Block').count()\n", - " out.write.format(\"webgis\").save(\"grpby_type_blkgrp\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Executing (RunPythonScript): RunPythonScript \"def grpby_type_blkgrp():\\\\n from datetime import datetime as dt\\\\n # Load the big data file share layer into a DataFrame\\\\n df = layers[0]\\\\n out = df.groupBy(\\'Primary Type\\', \\'Block\\').count()\\\\n out.write.format(\"webgis\").save(\"grpby_type_blkgrp\" + str(dt.now().microsecond))\\\\n\\\\ngrpby_type_blkgrp()\" https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 26771}}\"'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Start Time: Thu Apr 9 18:57:14 2020'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Using URL based GPRecordSet param: https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 259 tasks.\",\"params\":{\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"25/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"25\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"46/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"46\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"60/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"60\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"118/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"118\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"163/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"163\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"206/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"206\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"245/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"245\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"259/259 distributed tasks completed.\",\"params\":{\"completedTasks\":\"259\",\"totalTasks\":\"259\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 571108\",\"params\":{\"resultCount\":\"571108\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://ndhagsb01.esri.com/gis/rest/services/Hosted/grpby_type_blkgrp322476/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://ndhagsb01.esri.com/gis/rest/services/Hosted/grpby_type_blkgrp322476/FeatureServer/0\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Succeeded at Thu Apr 9 18:58:17 2020 (Elapsed Time: 1 minutes 3 seconds)'}]" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_python_script(code=grpby_type_blkgrp, layers=[crime_lyr])" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "grpby_cat_blk = gis.content.search('grpby_type_blkgrp')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - " grpby_type_blkgrp322476\n", - " \n", - "
Table Layer by admin\n", - "
Last Modified: April 09, 2020\n", - "
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BlockOBJECTIDPrimary_Typecountglobalid
0096XX S MICHIGAN AV1BATTERY34{AC1470E0-614D-BB2A-901E-034720D62910}
1070XX S LAFAYETTE ST2ASSAULT1{5876F9DE-F728-22A9-270C-710E48FA17FB}
2061XX S COTTAGE GROVE3CRIMINAL TRESPASS75{3D7FC5D0-F670-53B9-7135-5DB84B1048E4}
3014XX W MONTROSE AV4OTHER OFFENSE1{478D77DC-DAFA-C59D-08B0-29911B4F50FD}
4055XX N LAKE SHORE DR5OTHER OFFENSE1{69281C9B-5158-8A73-7AEF-1EB4102276C2}
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" - ], - "text/plain": [ - " Block OBJECTID Primary_Type count \\\n", - "0 096XX S MICHIGAN AV 1 BATTERY 34 \n", - "1 070XX S LAFAYETTE ST 2 ASSAULT 1 \n", - "2 061XX S COTTAGE GROVE 3 CRIMINAL TRESPASS 75 \n", - "3 014XX W MONTROSE AV 4 OTHER OFFENSE 1 \n", - "4 055XX N LAKE SHORE DR 5 OTHER OFFENSE 1 \n", - "\n", - " globalid \n", - "0 {AC1470E0-614D-BB2A-901E-034720D62910} \n", - "1 {5876F9DE-F728-22A9-270C-710E48FA17FB} \n", - "2 {3D7FC5D0-F670-53B9-7135-5DB84B1048E4} \n", - "3 {478D77DC-DAFA-C59D-08B0-29911B4F50FD} \n", - "4 {69281C9B-5158-8A73-7AEF-1EB4102276C2} " - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grpby_cat_blk_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Count of crime incidents by block group" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "grpby_cat_blk_df.sort_values(by='count', ascending=False, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "grpby_cat_blk_df.head(10).plot(x='Block', y='count', kind='barh')\n", - "plt.xticks(\n", - " rotation=45,\n", - " horizontalalignment='center',\n", - " fontweight='light',\n", - " fontsize='medium',\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get crime types for a particular block group" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "blk_addr_high = grpby_cat_blk_df[grpby_cat_blk_df['Block'] == '001XX N STATE ST']" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "143115 WEAPONS VIOLATION\n", - "766 THEFT\n", - "122685 STALKING\n", - "94954 SEX OFFENSE\n", - "28868 ROBBERY\n", - "Name: Primary_Type, dtype: object" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "blk_addr_high.Primary_Type.sort_values(ascending=False).head()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "def crime_by_datetime():\n", - " from datetime import datetime as dt\n", - " # Load the big data file share layer into a DataFrame\n", - " from pyspark.sql import functions as F\n", - " df = layers[0]\n", - " out = df.withColumn('datetime', F.unix_timestamp('Date', 'dd/MM/yyyy hh:mm:ss a').cast('timestamp'))\n", - " out.write.format(\"webgis\").save(\"crime_by_datetime\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Executing (RunPythonScript): RunPythonScript \"def crime_by_datetime():\\\\n from datetime import datetime as dt\\\\n # Load the big data file share layer into a DataFrame\\\\n from pyspark.sql import functions as F\\\\n df = layers[0]\\\\n out = df.withColumn(\\'datetime\\', F.unix_timestamp(\\'Date\\', \\'dd/MM/yyyy hh:mm:ss a\\').cast(\\'timestamp\\'))\\\\n out.write.format(\"webgis\").save(\"crime_by_datetime\" + str(dt.now().microsecond))\\\\n\\\\ncrime_by_datetime()\" https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 26771}}\"'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Start Time: Thu Apr 9 19:39:44 2020'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Using URL based GPRecordSet param: https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 59 tasks.\",\"params\":{\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"5/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"5\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"6/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"6\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"13/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"13\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"19/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"19\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"24/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"24\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"25/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"25\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"26/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"26\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"29/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"29\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"33/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"33\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"37/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"37\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"40/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"40\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"43/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"43\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"46/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"46\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"59/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"59\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 7061128\",\"params\":{\"resultCount\":\"7061128\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = {\\\\\"xmin\\\\\":-91.686565684,\\\\\"ymin\\\\\":36.619446395,\\\\\"xmax\\\\\":-87.524529378,\\\\\"ymax\\\\\":42.022910333}\",\"params\":{\"extent\":\"{\\\\\"xmin\\\\\":-91.686565684,\\\\\"ymin\\\\\":36.619446395,\\\\\"xmax\\\\\":-87.524529378,\\\\\"ymax\\\\\":42.022910333}\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = Interval(MutableInstant(2001-01-01 00:00:00.000),MutableInstant(2020-01-26 23:40:00.000))\",\"params\":{\"extent\":\"Interval(MutableInstant(2001-01-01 00:00:00.000),MutableInstant(2020-01-26 23:40:00.000))\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://ndhagsb01.esri.com/gis/rest/services/Hosted/crime_by_datetime650380/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://ndhagsb01.esri.com/gis/rest/services/Hosted/crime_by_datetime650380/FeatureServer/0\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Succeeded at Thu Apr 9 19:42:31 2020 (Elapsed Time: 2 minutes 46 seconds)'}]" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_python_script(code=crime_by_datetime, layers=[crime_lyr])" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "calls_with_datetime = gis.content.search('crime_by_datetime')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "calls_with_datetime_lyr = calls_with_datetime.layers[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "def crime_with_added_date_time_cols():\n", - " from datetime import datetime as dt\n", - " # Load the big data file share layer into a DataFrame\n", - " from pyspark.sql.functions import year, month, hour\n", - " df = layers[0]\n", - " df = df.withColumn('month', month(df['datetime']))\n", - " out = df.withColumn('hour', hour(df['datetime']))\n", - " out.write.format(\"webgis\").save(\"crime_with_added_date_time_cols\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Executing (RunPythonScript): RunPythonScript \"def crime_with_added_date_time_cols():\\\\n from datetime import datetime as dt\\\\n # Load the big data file share layer into a DataFrame\\\\n from pyspark.sql.functions import year, month, hour\\\\n df = layers[0]\\\\n df = df.withColumn(\\'month\\', month(df[\\'datetime\\']))\\\\n out = df.withColumn(\\'hour\\', hour(df[\\'datetime\\']))\\\\n out.write.format(\"webgis\").save(\"crime_with_added_date_time_cols\" + str(dt.now().microsecond))\\\\n\\\\ncrime_with_added_date_time_cols()\" https://ndhagsb01.esri.com/gis/rest/services/Hosted/crime_by_datetime650380/FeatureServer/0 \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 26771}}\"'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Start Time: Thu Apr 9 19:42:34 2020'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Using URL based GPRecordSet param: https://ndhagsb01.esri.com/gis/rest/services/Hosted/crime_by_datetime650380/FeatureServer/0'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 66 tasks.\",\"params\":{\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"3/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"3\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"10/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"10\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"15/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"15\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"19/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"19\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"22/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"22\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"24/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"24\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"29/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"29\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"32/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"32\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"36/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"36\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"37/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"37\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"38/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"38\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"42/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"42\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"43/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"43\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"46/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"46\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"51/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"51\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"55/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"55\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"56/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"56\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"60/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"60\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"66/66 distributed tasks completed.\",\"params\":{\"completedTasks\":\"66\",\"totalTasks\":\"66\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 7061128\",\"params\":{\"resultCount\":\"7061128\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = {\\\\\"xmin\\\\\":-91.686565684,\\\\\"ymin\\\\\":36.619446395,\\\\\"xmax\\\\\":-87.524529378,\\\\\"ymax\\\\\":42.022910333}\",\"params\":{\"extent\":\"{\\\\\"xmin\\\\\":-91.686565684,\\\\\"ymin\\\\\":36.619446395,\\\\\"xmax\\\\\":-87.524529378,\\\\\"ymax\\\\\":42.022910333}\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = Interval(MutableInstant(2001-01-01 00:00:00.000),MutableInstant(2020-01-26 23:40:00.000))\",\"params\":{\"extent\":\"Interval(MutableInstant(2001-01-01 00:00:00.000),MutableInstant(2020-01-26 23:40:00.000))\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://ndhagsb01.esri.com/gis/rest/services/Hosted/crime_with_added_date_time_cols749239/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://ndhagsb01.esri.com/gis/rest/services/Hosted/crime_with_added_date_time_cols749239/FeatureServer/0\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Succeeded at Thu Apr 9 19:47:06 2020 (Elapsed Time: 4 minutes 32 seconds)'}]" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_python_script(code=crime_with_added_date_time_cols, layers=[calls_with_datetime_lyr])" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "date_time_added_item = gis.content.search('crime_with_added_date_time_cols')" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "date_time_added_lyr = date_time_added_item[0].layers[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "def grp_crime_by_hour():\n", - " from datetime import datetime as dt\n", - " # Load the big data file share layer into a DataFrame\n", - " df = layers[0]\n", - " out = df.groupBy('hour').count()\n", - " out.write.format(\"webgis\").save(\"grp_crime_by_hour\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Executing (RunPythonScript): RunPythonScript \"def grp_crime_by_hour():\\\\n from datetime import datetime as dt\\\\n # Load the big data file share layer into a DataFrame\\\\n df = layers[0]\\\\n out = df.groupBy(\\'hour\\').count()\\\\n out.write.format(\"webgis\").save(\"grp_crime_by_hour\" + str(dt.now().microsecond))\\\\n\\\\ngrp_crime_by_hour()\" https://ndhagsb01.esri.com/gis/rest/services/Hosted/crime_with_added_date_time_cols749239/FeatureServer/0 \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 26771}}\"'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Start Time: Thu Apr 9 19:47:09 2020'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Using URL based GPRecordSet param: https://ndhagsb01.esri.com/gis/rest/services/Hosted/crime_with_added_date_time_cols749239/FeatureServer/0'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 266 tasks.\",\"params\":{\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"7/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"7\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"11/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"11\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"19/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"19\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"25/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"25\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"27/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"27\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"32/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"32\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"36/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"36\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"41/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"41\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"45/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"45\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"52/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"52\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"58/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"58\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"62/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"62\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"266/266 distributed tasks completed.\",\"params\":{\"completedTasks\":\"266\",\"totalTasks\":\"266\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 25\",\"params\":{\"resultCount\":\"25\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = None\",\"params\":{\"extent\":\"None\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://ndhagsb01.esri.com/gis/rest/services/Hosted/grp_crime_by_hour391644/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://ndhagsb01.esri.com/gis/rest/services/Hosted/grp_crime_by_hour391644/FeatureServer/0\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Succeeded at Thu Apr 9 19:49:15 2020 (Elapsed Time: 2 minutes 5 seconds)'}]" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_python_script(code=grp_crime_by_hour, layers=[date_time_added_lyr])" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "hour = gis.content.search('grp_crime_by_hour')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "grp_hour = hour.tables[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "df_hour = grp_hour.query(as_df=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Crime distribution by the hour" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "(df_hour\n", - " .dropna()\n", - " .sort_values(by='hour')\n", - " .astype({'hour' : int})\n", - " .plot(x='hour', y='count', kind='bar'))\n", - "plt.xticks(\n", - " rotation=45,\n", - " horizontalalignment='center',\n", - " fontweight='light',\n", - " fontsize='medium',\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph shows that the crime activities are more common at the peak hours 12 A.M. and 12 P.M." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Big data machine learning using pyspark.ml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find the optimal number of clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The average silhouette approach measures the quality of a clustering. That is, it determines how well each object lies within its cluster. A high average silhouette width indicates a good clustering. To learn more about silhouette analysis, click [here](https://runawayhorse001.github.io/LearningApacheSpark/clustering.html). " - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "def optimal_k():\n", - " import time\n", - " import numpy as np\n", - " import pandas as pd\n", - " from pyspark.ml.feature import VectorAssembler\n", - " from pyspark.ml.clustering import KMeans\n", - " from datetime import datetime as dt\n", - " from pyspark.ml.evaluation import ClusteringEvaluator\n", - " from pyspark.sql.context import SQLContext\n", - " from pyspark.sql.types import StructType, StructField, DoubleType, IntegerType, FloatType\n", - "\n", - " silh_lst = []\n", - " k_lst = np.arange(3, 70)\n", - "\n", - " crime_locations = layers[0]\n", - " assembler = VectorAssembler(inputCols=[\"X Coordinate\", \"Y Coordinate\"], outputCol=\"features\")\n", - " crime_locations = assembler.setHandleInvalid(\"skip\").transform(crime_locations)\n", - " \n", - " for k in k_lst:\n", - " silh_val = []\n", - " for run in np.arange(1, 3):\n", - " # Trains a k-means model.\n", - " kmeans = KMeans().setK(int(k)).setSeed(int(np.random.randint(100, size=1)))\n", - " model = kmeans.fit(crime_locations.select(\"features\"))\n", - "\n", - " # Make predictions\n", - " predictions = model.transform(crime_locations)\n", - "\n", - " # Evaluate clustering by computing Silhouette score\n", - " evaluator = ClusteringEvaluator()\n", - " silhouette = evaluator.evaluate(predictions)\n", - " silh_val.append(silhouette)\n", - "\n", - " silh_array=np.asanyarray(silh_val)\n", - " silh_lst.append(silh_array.mean()) \n", - "\n", - " silhouette = pd.DataFrame(list(zip(k_lst,silh_lst)),columns = ['k', 'silhouette'])\n", - " schema = StructType([StructField('k',IntegerType(),True), StructField('silhouette',FloatType(),True)])\n", - " out = SQLContext(sparkContext=spark.sparkContext, sparkSession=spark).createDataFrame(silhouette, schema)\n", - " # Write the result DataFrame to the relational data store\n", - " out.write.format(\"webgis\").option(\"dataStore\",\"relational\").save(\"optimalKmeans\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "run_python_script(code=optimal_k, layers=[crime_lyr])" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "optimal_k = gis.content.search('optimalKmeans')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "optimal_k_tbl = optimal_k.tables[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "k_df = optimal_k_tbl.query().sdf" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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objectidksilhouette
5458150.556612
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............
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67 rows × 3 columns

\n", - "
" - ], - "text/plain": [ - " objectid k silhouette\n", - "54 58 15 0.556612\n", - "22 23 19 0.556012\n", - "2 3 9 0.555995\n", - "39 40 14 0.552853\n", - "38 39 11 0.551726\n", - ".. ... .. ...\n", - "24 25 25 0.527496\n", - "19 20 7 0.527266\n", - "26 27 34 0.525585\n", - "37 38 8 0.507064\n", - "36 37 5 0.492071\n", - "\n", - "[67 rows x 3 columns]" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "k_df.sort_values(by='silhouette', ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "15" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "num_clusters = k_df.sort_values(by='silhouette', ascending=False).loc[0]['k']\n", - "num_clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### K-Means Clustering" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "def cluster_crimes():\n", - " \n", - " from pyspark.ml.feature import VectorAssembler\n", - " from pyspark.ml.clustering import KMeans\n", - " from datetime import datetime as dt\n", - " # Crime data is stored in a feature service and accessed as a DataFrame via the layers object\n", - " crime_locations = layers[0]\n", - " \n", - " # Combine the x and y columns in the DataFrame into a single column called \"features\"\n", - " assembler = VectorAssembler(inputCols=[\"X Coordinate\", \"Y Coordinate\"], outputCol=\"features\")\n", - " crime_locations = assembler.setHandleInvalid(\"skip\").transform(crime_locations)\n", - "\n", - " # Fit a k-means model with 15 clusters using the \"features\" column of the crime locations\n", - " kmeans = KMeans(k=15)\n", - " model = kmeans.fit(crime_locations.select(\"features\"))\n", - " \n", - " cost = model.computeCost(crime_locations)\n", - " # Add the cluster labels from the k-means model to the original DataFrame\n", - " crime_locations_clusters = model.transform(crime_locations)\n", - " # Write the result DataFrame to the relational data store\n", - " crime_locations_clusters.write.format(\"webgis\").save(\"Crime_Clusters_KMeans\" + str(dt.now().microsecond))" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "{\"messageCode\":\"BD_101231\",\"message\":\"The following fields are not supported and will be dropped: features\",\"params\":{\"fields\":\"features\"}}\n" - ] - }, - { - "data": { - "text/plain": [ - "[{'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Executing (RunPythonScript): RunPythonScript \"def cluster_crimes():\\\\n \\\\n from pyspark.ml.feature import VectorAssembler\\\\n from pyspark.ml.clustering import KMeans\\\\n from datetime import datetime as dt\\\\n # Crime data is stored in a feature service and accessed as a DataFrame via the layers object\\\\n crime_locations = layers[0]\\\\n \\\\n # Combine the x and y columns in the DataFrame into a single column called \"features\"\\\\n assembler = VectorAssembler(inputCols=[\"X Coordinate\", \"Y Coordinate\"], outputCol=\"features\")\\\\n crime_locations = assembler.setHandleInvalid(\"skip\").transform(crime_locations)\\\\n\\\\n # Fit a k-means model with 50 clusters using the \"features\" column of the crime locations\\\\n kmeans = KMeans(k=15)\\\\n model = kmeans.fit(crime_locations.select(\"features\"))\\\\n \\\\n cost = model.computeCost(crime_locations)\\\\n print(\\'cost\\', cost)\\\\n # Add the cluster labels from the k-means model to the original DataFrame\\\\n crime_locations_clusters = model.transform(crime_locations)\\\\n # Write the result DataFrame to the relational data store\\\\n crime_locations_clusters.write.format(\"webgis\").save(\"Crime_Clusters_KMeans\" + str(dt.now().microsecond))\\\\n\\\\ncluster_crimes()\" https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime \"{\"defaultAggregationStyles\": false, \"processSR\": {\"wkid\": 26771}}\"'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Start Time: Fri Apr 10 08:14:38 2020'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Using URL based GPRecordSet param: https://ndhga01.esri.com/gis/rest/services/DataStoreCatalogs/bigDataFileShares_Chicago_Crime_2001_2020/BigDataCatalogServer/crime'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 59 tasks.\",\"params\":{\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"23/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"23\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"52/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"52\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"59/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"59\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 59 tasks.\",\"params\":{\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"59/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"59\",\"totalTasks\":\"59\"}}'},\n", - 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" {'type': 'esriJobMessageTypeWarning',\n", - " 'description': '{\"messageCode\":\"BD_101231\",\"message\":\"The following fields are not supported and will be dropped: features\",\"params\":{\"fields\":\"features\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 59 tasks.\",\"params\":{\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"0/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"1/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"1\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"5/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"5\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"6/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"6\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"12/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"12\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"18/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"18\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"22/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"22\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"25/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"25\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"26/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"26\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"28/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"28\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"31/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"31\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"35/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"35\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"38/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"38\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"40/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"40\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"43/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"43\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"45/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"45\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"46/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"46\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"53/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"53\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101029\",\"message\":\"59/59 distributed tasks completed.\",\"params\":{\"completedTasks\":\"59\",\"totalTasks\":\"59\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 6993512\",\"params\":{\"resultCount\":\"6993512\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = {\\\\\"xmin\\\\\":-91.686565684,\\\\\"ymin\\\\\":36.619446395,\\\\\"xmax\\\\\":-87.524529378,\\\\\"ymax\\\\\":42.022910333}\",\"params\":{\"extent\":\"{\\\\\"xmin\\\\\":-91.686565684,\\\\\"ymin\\\\\":36.619446395,\\\\\"xmax\\\\\":-87.524529378,\\\\\"ymax\\\\\":42.022910333}\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = Interval(MutableInstant(2001-01-01 00:00:00.000),MutableInstant(2020-01-26 23:40:00.000))\",\"params\":{\"extent\":\"Interval(MutableInstant(2001-01-01 00:00:00.000),MutableInstant(2020-01-26 23:40:00.000))\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': '{\"messageCode\":\"BD_101226\",\"message\":\"Feature service layer created: https://ndhagsb01.esri.com/gis/rest/services/Hosted/Crime_Clusters_KMeans540499/FeatureServer/0\",\"params\":{\"serviceUrl\":\"https://ndhagsb01.esri.com/gis/rest/services/Hosted/Crime_Clusters_KMeans540499/FeatureServer/0\"}}'},\n", - " {'type': 'esriJobMessageTypeInformative',\n", - " 'description': 'Succeeded at Fri Apr 10 08:19:30 2020 (Elapsed Time: 4 minutes 52 seconds)'}]" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_python_script(code=cluster_crimes, layers=[crime_lyr])" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "clusters = gis.content.search('Crime_Clusters_KMeans')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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We have really gained a lot of knowledge about the use of data mining and clustering to help manage huge amount of data and deduce useful information from criminal data. " - ] - } - ], - "metadata": { - "esriNotebookRuntime": { - "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", - "notebookRuntimeVersion": "9.0" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/samples/04_gis_analysts_data_scientists/forecasting_pm2.5_using_big_data_analysis.ipynb b/samples/04_gis_analysts_data_scientists/forecasting_pm2.5_using_big_data_analysis.ipynb deleted file mode 100644 index c133eafee6..0000000000 --- a/samples/04_gis_analysts_data_scientists/forecasting_pm2.5_using_big_data_analysis.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Forecasting PM2.5 using big data analysis"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Table of Contents\n", "* [Prerequisites](#Prerequisites)\n", "* [Introduction](#Introduction)\n", "* [Necessary imports](#Necessary-imports)\n", "* [Connect to your ArcGIS Enterprise organization](#Connect-to-your-ArcGIS-Enterprise-organization)\n", "* [Ensure your GIS supports GeoAnalytics](#Ensure-your-GIS-supports-GeoAnalytics)\n", "* [Prepare data](#Prepare_data)\n", " * [Create a big data file share](#Create-a-big-data-file-share)\n", "* [Get data for analysis](#Get-data-for-analysis)\n", " * [Search for big data file shares](#Search-for-big-data-file-shares)\n", " * [Search for feature layers](#Search-for-feature-layers)\n", "* [Uncover patterns in data](#Uncover-patterns-in-data)\n", " * [Describe data](#Describe-data)\n", " * [Commonly used methods of measurement](#Commonly-used-methods-of-measurement)\n", " * [Average PM 2.5 value by county](#Average-PM-2.5-value-by-county)\n", "* [Prepare time series data](#Prepare-time-series-data) \n", "* [Predict PM2.5 using Facebook's Prophet model](#Predict-PM2.5-using-Facebook's-Prophet-model)\n", "* [Visualize result on Dashboard](#Visualize-result-on-Dashboard)\n", "* [Conclusion](#Conclusion)\n", "* [References](#References)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Prerequisites"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- The tools available in the ArcGIS API for Python geoanalytics module require an ArcGIS Enterprise licensed and configured with the linux based [ArcGIS GeoAnalytics server](http://enterprise.arcgis.com/en/server/latest/get-started/windows/configure-the-portal-with-arcgis-geoanalytics-server.htm).\n", "- When ArcGIS GeoAnalytics Server is installed on Linux, additional configuration steps are required before using the RunPythonScript operation. Install and configure Python 3.6 for Linux on each machine in your GeoAnalytics Server site, ensuring that Python is installed into the same directory on each machine. For this analysis, you also need to install FB Prophet library in the same environment. Then, update the [ArcGIS Server Properties](https://developers.arcgis.com/rest/enterprise-administration/server/serverproperties.htm) on your GeoAnalytics Server site with the pysparkPython property. The value of this property should be the path to the Python executable on your GeoAnalytics Server machines, for example, {\"pysparkPython\":\"path/to/environment\"}. \n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Introduction"]}, {"cell_type": "markdown", "metadata": {}, "source": ["While the `arcgis.geoanalytics` module offers powerful spatial analysis tools, the pyspark package includes dozens of non-spatial distributed tools for classification, prediction, clustering, and more.\n", "Using the power of distributed compute, we can analyze large datasets much faster than other non-distributed systems.\n", "\n", "This notebook demonstrates the capability of spark-powered geoanalytics server to forecast hourly [PM2.5](https://www.epa.gov/pm-pollution/particulate-matter-pm-basics) given the historic time series data for more than one time-dependent variable. The most common factors in the weather environment used in this analysis are PM2.5, PM10, wind speed, wind direction, and relative humidity. The levels of these pollutants are measured by the US Environmental Protection Agency (EPA), which controls overall air quality. We have used the [dataset](https://aqs.epa.gov/aqsweb/airdata/download_files.html#Raw) provided by EPA. \n", "With Spark we will learn how to customize and extend our analysis capabilities by:\n", "\n", "- Querying and summarizing your data using SQL\n", "- Turning analysis workflows into pipelines of GeoAnalytics tools\n", "- Modeling data with included machine learning libraries\n", "\n", "The forecasted result will then be displayed on a dashboard using the recently introduced `arcgis.dashboard` module in ArcGIS API for Python. \n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Note:\n", "- The ability to perform big data analysis is only available on ArcGIS Enterprise licensed with a GeoAnalytics server and not yet available on ArcGIS Online."]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Necessary imports"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["from datetime import datetime as dt\n", "import pandas as pd\n", "\n", "import arcgis\n", "from arcgis.gis import GIS\n", "from arcgis.geoanalytics.manage_data import run_python_script"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Connect to your ArcGIS Enterprise organization"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": ["gis = GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Ensure your GIS supports GeoAnalytics\n", "After connecting to the Enterprise organization, we need to ensure an ArcGIS Enterprise GIS is set up with a licensed GeoAnalytics server. To do so, we will call the `is_supported()` method."]}, {"cell_type": "code", "execution_count": 4, "metadata": {"scrolled": true}, "outputs": [{"data": {"text/plain": ["True"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["arcgis.geoanalytics.is_supported()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Prepare data\n", "To register a file share or an HDFS, we need to format datasets as subfolders within a single parent folder and register the parent folder. This parent folder becomes a datastore, and each subfolder becomes a dataset. Our folder hierarchy would look like below:"]}, {"cell_type": "markdown", "metadata": {}, "source": [""]}, {"cell_type": "markdown", "metadata": {}, "source": ["Learn more about preparing your big data file share datasets [here](https://enterprise.arcgis.com/en/server/latest/get-started/windows/what-is-a-big-data-file-share.htm)."]}, {"cell_type": "markdown", "metadata": {}, "source": ["The [`get_datastores()`](https://developers.arcgis.com/python/api-reference/arcgis.geoanalytics.toc.html?highlight=get_datastores#arcgis.geoanalytics.get_datastores) method of the geoanalytics module returns a [`DatastoreManager`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html#datastoremanager) object that lets you search for and manage the big data file share items as Python API [`Datastore`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html#datastore) objects on your GeoAnalytics server."]}, {"cell_type": "code", "execution_count": 5, "metadata": {"scrolled": true}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["bigdata_datastore_manager = arcgis.geoanalytics.get_datastores()\n", "bigdata_datastore_manager"]}, {"cell_type": "markdown", "metadata": {}, "source": ["We will register air quality data as a big data file share using the [`add_bigdata()`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html#arcgis.gis.DatastoreManager.add_bigdata) function on a `DatastoreManager` object. \n", "\n", "When we register a directory, all subdirectories under the specified folder are also registered with the server. Always register the parent folder (for example, \\\\machinename\\mydatashare) that contains one or more individual dataset folders as the big data file share item. To learn more, see [register a big data file share](https://enterprise.arcgis.com/en/server/latest/manage-data/windows/registering-your-data-with-arcgis-server-using-manager.htm#ESRI_SECTION1_0D55682C9D6E48E7857852A9E2D5D189)\n", "\n", "Note: \n", "You cannot browse directories in ArcGIS Server Manager. You must provide the full path to the folder you want to register, for example, \\\\myserver\\share\\bigdata. Avoid using local paths, such as C:\\bigdata, unless the same data folder is available on all nodes of the server site."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["# data_item = bigdata_datastore_manager.add_bigdata(\"air_quality_17_18_19\", r\"/mnt/network/data\")"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"text/plain": ["[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["bigdata_fileshares = bigdata_datastore_manager.search()\n", "bigdata_fileshares"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Get data for analysis"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Adding a big data file share to the Geoanalytics server adds a corresponding [big data file share item](https://enterprise.arcgis.com/en/portal/latest/use/what-is-a-big-data-file-share.htm) on the portal. We can search for these types of items using the item_type parameter."]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/html": ["
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\n", " "], "text/plain": [""]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["aqs_data = gis.content.search(\"bigDataFileShares_GA_Data\", item_type = \"big data file share\")[0]\n", "aqs_data"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Querying the layers property of the [item](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html#item) returns a featureLayer representing the data. "]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": ["air_lyr = aqs_data.layers[0]"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"data": {"text/plain": ["{\n", " \"dataStoreID\": \"4f498adb-8e5a-4b7e-96d1-9accacb26a50\",\n", " \"fields\": [\n", " {\n", " \"name\": \"State Code\",\n", " \"type\": \"esriFieldTypeInteger\"\n", " },\n", " {\n", " \"name\": \"County Code\",\n", " \"type\": \"esriFieldTypeInteger\"\n", " },\n", " {\n", " \"name\": \"Site Num\",\n", " \"type\": \"esriFieldTypeInteger\"\n", " },\n", " {\n", " \"name\": \"Parameter Code\",\n", " \"type\": \"esriFieldTypeInteger\"\n", " },\n", " {\n", " \"name\": \"POC\",\n", " \"type\": \"esriFieldTypeInteger\"\n", " },\n", " {\n", " \"name\": \"Latitude\",\n", " \"type\": \"esriFieldTypeDouble\"\n", " },\n", " {\n", " \"name\": \"Longitude\",\n", " \"type\": \"esriFieldTypeDouble\"\n", " },\n", " {\n", " \"name\": \"Datum\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Parameter Name\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Date Local\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Time Local\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Date GMT\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Time GMT\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Sample Measurement\",\n", " \"type\": \"esriFieldTypeDouble\"\n", " },\n", " {\n", " \"name\": \"Units of Measure\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"MDL\",\n", " \"type\": \"esriFieldTypeDouble\"\n", " },\n", " {\n", " \"name\": \"Uncertainty\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Qualifier\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Method Type\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Method Code\",\n", " \"type\": \"esriFieldTypeInteger\"\n", " },\n", " {\n", " \"name\": \"Method Name\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"State Name\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"County Name\",\n", " \"type\": \"esriFieldTypeString\"\n", " },\n", " {\n", " \"name\": \"Date of Last Change\",\n", " \"type\": \"esriFieldTypeString\"\n", " }\n", " ],\n", " \"name\": \"air_quality\",\n", " \"geometryType\": \"esriGeometryPoint\",\n", " \"type\": \"featureClass\",\n", " \"spatialReference\": {\n", " \"wkid\": 102682,\n", " \"latestWkid\": 3452\n", " },\n", " \"geometry\": {\n", " \"fields\": [\n", " {\n", " \"name\": \"Longitude\",\n", " \"formats\": [\n", " \"x\"\n", " ]\n", " },\n", " {\n", " \"name\": \"Latitude\",\n", " \"formats\": [\n", " \"y\"\n", " ]\n", " }\n", " ]\n", " },\n", " \"time\": {\n", " \"timeType\": \"instant\",\n", " \"timeReference\": {\n", " \"timeZone\": \"UTC\"\n", " },\n", " \"fields\": [\n", " {\n", " \"name\": \"Date Local\",\n", " \"formats\": [\n", " \"yyyy-MM-dd\"\n", " ]\n", " }\n", " ]\n", " },\n", " \"currentVersion\": 10.81,\n", " \"children\": []\n", "}"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["air_lyr.properties"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now we'll search for a feature layer depicting all the counties in the United States. We'll use the county polygons later in the notebook."]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": ["usa_counties = gis.content.get('5c6ef8ef57934990b543708f815d606e')"]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/html": ["
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State_CodeCounty_CodeSite_NumParameter_CodePOCLatitudeLongitudeDatumParameter_NameDate_Local...Method_TypeMethod_CodeMethod_NameState_NameCounty_NameDate_of_Last_ChangeINSTANT_DATETIMEglobalidOBJECTIDSHAPE
0563570062201142.486361-110.098861WGS84Relative Humidity2017-07-05...Non-FRM60Instrumental - Vaisala 435C RH/AT SensorWyomingSublette2017-11-202017-07-05{4CB48700-32CB-6126-E342-B783681A14AF}1{\"x\": -110.098861, \"y\": 42.486360999999995, \"s...
1992788313141.301400-72.902871WGS84Black Carbon PM2.5 at 880 nm2017-06-08...Non-FRM894Magee AE33/ TAPI M633 Aethalometer - Optical a...ConnecticutNew Haven2018-01-092017-06-08{3D99EDC1-EC50-6B1A-508F-A0034CD9DB02}2{\"x\": -72.90287099999999, \"y\": 41.3014, \"spati...
24579742601234.093959-80.962304WGS84Nitric oxide (NO)2018-06-28...Non-FRM674Instrumental - Chemiluminescence Thermo Electr...South CarolinaRichland2019-02-182018-06-28{04ED8FBB-2ADF-AA44-BE30-F61E7A939657}3{\"x\": -80.962304, \"y\": 34.093959, \"spatialRefe...
348183162101132.378682-94.711811WGS84Outdoor Temperature2018-10-18...Non-FRM40INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.TexasGregg2019-02-182018-10-18{60E09F2E-891F-EEBD-1253-0FFA407BDEA4}4{\"x\": -94.711811, \"y\": 32.378682, \"spatialRefe...
4191533044201141.603159-93.643118WGS84Ozone2017-12-23...FEM47INSTRUMENTAL - ULTRA VIOLETIowaPolk2018-01-122017-12-23{73E73030-0883-7A32-ED9C-BD59ACC00A2F}5{\"x\": -93.643118, \"y\": 41.603159, \"spatialRefe...
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995275396261104144.965242-93.254759NAD83Wind Direction - Resultant2018-07-15...Non-FRM63Instrumental - ClimatronicsMinnesotaHennepin2019-01-172018-07-15{61736B2D-7C20-DF9E-37BA-21CF788F8FDD}1003{\"x\": -93.25475899999999, \"y\": 44.965241999999...
996484310144201129.302650-103.177810WGS84Ozone2018-02-21...FEM47INSTRUMENTAL - ULTRA VIOLETTexasBrewster2018-04-192018-02-21{E739DA0C-7A97-8059-72A3-6505490F990D}1004{\"x\": -103.17781, \"y\": 29.30265, \"spatialRefer...
997191631762101141.467236-90.688451WGS84Outdoor Temperature2017-07-16...Non-FRM40INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.IowaScott2017-08-102017-07-16{1B805C6B-7621-B9B2-9C25-E35DC16D466E}1006{\"x\": -90.688451, \"y\": 41.467236, \"spatialRefe...
998391452162101138.600611-82.829782NAD83Outdoor Temperature2017-12-20...Non-FRM40INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.OhioScioto2018-01-122017-12-20{965A3811-00DB-88A4-A734-959041D2CF67}1007{\"x\": -82.829782, \"y\": 38.600611, \"spatialRefe...
999631461103136.102244-119.565650NAD83Wind Speed - Resultant2017-09-28...Non-FRM20INSTRUMENTAL - VECTOR SUMMATIONCaliforniaKings2018-03-052017-09-28{85F9DBB0-C17B-A74A-2717-29AACFCDB63B}1008{\"x\": -119.56565, \"y\": 36.102244, \"spatialRefe...
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1000 rows \u00d7 28 columns

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"], "text/plain": [" State_Code County_Code Site_Num Parameter_Code POC Latitude \\\n", "0 56 35 700 62201 1 42.486361 \n", "1 9 9 27 88313 1 41.301400 \n", "2 45 79 7 42601 2 34.093959 \n", "3 48 183 1 62101 1 32.378682 \n", "4 19 153 30 44201 1 41.603159 \n", ".. ... ... ... ... ... ... \n", "995 27 53 962 61104 1 44.965242 \n", "996 48 43 101 44201 1 29.302650 \n", "997 19 163 17 62101 1 41.467236 \n", "998 39 145 21 62101 1 38.600611 \n", "999 6 31 4 61103 1 36.102244 \n", "\n", " Longitude Datum Parameter_Name Date_Local ... \\\n", "0 -110.098861 WGS84 Relative Humidity 2017-07-05 ... \n", "1 -72.902871 WGS84 Black Carbon PM2.5 at 880 nm 2017-06-08 ... \n", "2 -80.962304 WGS84 Nitric oxide (NO) 2018-06-28 ... \n", "3 -94.711811 WGS84 Outdoor Temperature 2018-10-18 ... \n", "4 -93.643118 WGS84 Ozone 2017-12-23 ... \n", ".. ... ... ... ... ... \n", "995 -93.254759 NAD83 Wind Direction - Resultant 2018-07-15 ... \n", "996 -103.177810 WGS84 Ozone 2018-02-21 ... \n", "997 -90.688451 WGS84 Outdoor Temperature 2017-07-16 ... \n", "998 -82.829782 NAD83 Outdoor Temperature 2017-12-20 ... \n", "999 -119.565650 NAD83 Wind Speed - Resultant 2017-09-28 ... \n", "\n", " Method_Type Method_Code \\\n", "0 Non-FRM 60 \n", "1 Non-FRM 894 \n", "2 Non-FRM 674 \n", "3 Non-FRM 40 \n", "4 FEM 47 \n", ".. ... ... \n", "995 Non-FRM 63 \n", "996 FEM 47 \n", "997 Non-FRM 40 \n", "998 Non-FRM 40 \n", "999 Non-FRM 20 \n", "\n", " Method_Name State_Name \\\n", "0 Instrumental - Vaisala 435C RH/AT Sensor Wyoming \n", "1 Magee AE33/ TAPI M633 Aethalometer - Optical a... Connecticut \n", "2 Instrumental - Chemiluminescence Thermo Electr... South Carolina \n", "3 INSTRUMENTAL - ELECTRONIC OR MACHINE AVG. Texas \n", "4 INSTRUMENTAL - ULTRA VIOLET Iowa \n", ".. ... ... \n", "995 Instrumental - Climatronics Minnesota \n", "996 INSTRUMENTAL - ULTRA VIOLET Texas \n", "997 INSTRUMENTAL - ELECTRONIC OR MACHINE AVG. Iowa \n", "998 INSTRUMENTAL - ELECTRONIC OR MACHINE AVG. Ohio \n", "999 INSTRUMENTAL - VECTOR SUMMATION California \n", "\n", " County_Name Date_of_Last_Change INSTANT_DATETIME \\\n", "0 Sublette 2017-11-20 2017-07-05 \n", "1 New Haven 2018-01-09 2017-06-08 \n", "2 Richland 2019-02-18 2018-06-28 \n", "3 Gregg 2019-02-18 2018-10-18 \n", "4 Polk 2018-01-12 2017-12-23 \n", ".. ... ... ... \n", "995 Hennepin 2019-01-17 2018-07-15 \n", "996 Brewster 2018-04-19 2018-02-21 \n", "997 Scott 2017-08-10 2017-07-16 \n", "998 Scioto 2018-01-12 2017-12-20 \n", "999 Kings 2018-03-05 2017-09-28 \n", "\n", " globalid OBJECTID \\\n", "0 {4CB48700-32CB-6126-E342-B783681A14AF} 1 \n", "1 {3D99EDC1-EC50-6B1A-508F-A0034CD9DB02} 2 \n", "2 {04ED8FBB-2ADF-AA44-BE30-F61E7A939657} 3 \n", "3 {60E09F2E-891F-EEBD-1253-0FFA407BDEA4} 4 \n", "4 {73E73030-0883-7A32-ED9C-BD59ACC00A2F} 5 \n", ".. ... ... \n", "995 {61736B2D-7C20-DF9E-37BA-21CF788F8FDD} 1003 \n", "996 {E739DA0C-7A97-8059-72A3-6505490F990D} 1004 \n", "997 {1B805C6B-7621-B9B2-9C25-E35DC16D466E} 1006 \n", "998 {965A3811-00DB-88A4-A734-959041D2CF67} 1007 \n", "999 {85F9DBB0-C17B-A74A-2717-29AACFCDB63B} 1008 \n", "\n", " SHAPE \n", "0 {\"x\": -110.098861, \"y\": 42.486360999999995, \"s... \n", "1 {\"x\": -72.90287099999999, \"y\": 41.3014, \"spati... \n", "2 {\"x\": -80.962304, \"y\": 34.093959, \"spatialRefe... \n", "3 {\"x\": -94.711811, \"y\": 32.378682, \"spatialRefe... \n", "4 {\"x\": -93.643118, \"y\": 41.603159, \"spatialRefe... \n", ".. ... \n", "995 {\"x\": -93.25475899999999, \"y\": 44.965241999999... \n", "996 {\"x\": -103.17781, \"y\": 29.30265, \"spatialRefer... \n", "997 {\"x\": -90.688451, \"y\": 41.467236, \"spatialRefe... \n", "998 {\"x\": -82.829782, \"y\": 38.600611, \"spatialRefe... \n", "999 {\"x\": -119.56565, \"y\": 36.102244, \"spatialRefe... \n", "\n", "[1000 rows x 28 columns]"]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["description.sample_layer.query().sdf"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"text/html": [""], "text/plain": [""]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["m1 = gis.map('USA')\n", "m1"]}, {"cell_type": "markdown", "metadata": {}, "source": ["
Locations of the Air pollution monitors"]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": ["m1.add_layer(description.sample_layer)"]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": ["m1.zoom_to_layer(description.sample_layer)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Commonly used methods of measurement"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The 'Method Name' attribute contains information about the type of instrument used for measurement. 'Parameter Name' attribute tells about the name or description assigned in AQS to the parameter measured by the monitor. For more details, read [here](https://aqs.epa.gov/aqsweb/airdata/FileFormats.html#_hourly_data_files).\n", "\n", "The function below groups data by both these parameters. This will help us know the most common type of instrument used to measure each type of parameter."]}, {"cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": ["def measurement_type():\n", " from datetime import datetime as dt\n", " # Load the big data file share layer into a DataFrame\n", " df = layers[0]\n", " out = df.groupBy('Method Name','Parameter Name').count()\n", " out.write.format(\"webgis\").save(\"common_method_type\" + str(dt.now().microsecond)) # Write the final result to our datastore."]}, {"cell_type": "markdown", "metadata": {}, "source": ["The [`run_python_script`](https://developers.arcgis.com/python/api-reference/arcgis.geoanalytics.manage_data.html?highlight=run%20python%20script#arcgis.geoanalytics.manage_data.run_python_script) method executes a Python script directly in an ArcGIS GeoAnalytics server site . The script can create an analysis pipeline by chaining together multiple GeoAnalytics tools without writing intermediate results to a data store. The tool can also distribute Python functionality across the GeoAnalytics server site.\n", "\n", "When using the geoanalytics and pyspark packages, most functions return analysis results as Spark DataFrame memory structures. You can write these data frames to a data store or process them in a script. This lets you chain multiple geoanalytics and pyspark tools while only writing out the final result, eliminating the need to create any bulky intermediate result layers.\n", "\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["run_python_script(code=measurement_type, layers=[air_lyr])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The result is saved as a feature layer. We can Search for the saved item using the search() method. Providing the search keyword same as the name we used for writing the result will retrieve the layer."]}, {"cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": ["method_item = gis.content.search('common_method_type')[0]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Accessing the `tables` property of the item will give us the tables object. We will then use `query()` method to read the table as spatially enabled dataframe."]}, {"cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": ["method_df = method_item.tables[0].query(as_df=True)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Sort the values in the decreasing order of the count field."]}, {"cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [{"data": {"text/html": ["
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Method_NameParameter_NamecountglobalidOBJECTID
89INSTRUMENTAL - ULTRA VIOLET ABSORPTIONOzone10650047{1C333EB9-AB7D-9ACF-3116-09924757FD90}112
6INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.Outdoor Temperature9808818{17DCDDD0-079F-5BF2-45C3-0BF485355EB0}8
158INSTRUMENTAL - ULTRA VIOLETOzone8634142{EDA2D065-CE9E-2175-F094-8D9C2F6DCEBD}198
290INSTRUMENTAL - VECTOR SUMMATIONWind Direction - Resultant7348868{7154F73E-775C-4C1A-8775-FB99B0074348}503
277INSTRUMENTAL - VECTOR SUMMATIONWind Speed - Resultant7267098{4CCC027A-BABE-DF05-FC6B-9B04F013AF14}455
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100Cooper Environmental Services model Xact 620 -...Vanadium PM10 LC105{ABFA136A-BE6E-79A7-FCA4-608CFABCB77F}126
234Cooper Environmental Services model Xact 620 -...Tin PM10 LC105{A5B11059-6FD5-EC9A-65E4-6D4B06B25FFF}339
245Cooper Environmental Services model Xact 620 -...Silver PM10 LC105{E56280AE-5F42-AD31-2D95-C3418CB40985}356
12Cooper Environmental Services model Xact 620 -...Calcium PM10 LC105{6FC4B1BA-A7E4-A69C-BB44-CC26A6FD935B}17
38ThePM2.5 - Local Conditions1{E955A26F-4FC1-E8A3-40F1-D85F48D15ED9}49
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338 rows \u00d7 5 columns

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"], "text/plain": [" Method_Name \\\n", "89 INSTRUMENTAL - ULTRA VIOLET ABSORPTION \n", "6 INSTRUMENTAL - ELECTRONIC OR MACHINE AVG. \n", "158 INSTRUMENTAL - ULTRA VIOLET \n", "290 INSTRUMENTAL - VECTOR SUMMATION \n", "277 INSTRUMENTAL - VECTOR SUMMATION \n", ".. ... \n", "100 Cooper Environmental Services model Xact 620 -... \n", "234 Cooper Environmental Services model Xact 620 -... \n", "245 Cooper Environmental Services model Xact 620 -... \n", "12 Cooper Environmental Services model Xact 620 -... \n", "38 The \n", "\n", " Parameter_Name count \\\n", "89 Ozone 10650047 \n", "6 Outdoor Temperature 9808818 \n", "158 Ozone 8634142 \n", "290 Wind Direction - Resultant 7348868 \n", "277 Wind Speed - Resultant 7267098 \n", ".. ... ... \n", "100 Vanadium PM10 LC 105 \n", "234 Tin PM10 LC 105 \n", "245 Silver PM10 LC 105 \n", "12 Calcium PM10 LC 105 \n", "38 PM2.5 - Local Conditions 1 \n", "\n", " globalid OBJECTID \n", "89 {1C333EB9-AB7D-9ACF-3116-09924757FD90} 112 \n", "6 {17DCDDD0-079F-5BF2-45C3-0BF485355EB0} 8 \n", "158 {EDA2D065-CE9E-2175-F094-8D9C2F6DCEBD} 198 \n", "290 {7154F73E-775C-4C1A-8775-FB99B0074348} 503 \n", "277 {4CCC027A-BABE-DF05-FC6B-9B04F013AF14} 455 \n", ".. ... ... \n", "100 {ABFA136A-BE6E-79A7-FCA4-608CFABCB77F} 126 \n", "234 {A5B11059-6FD5-EC9A-65E4-6D4B06B25FFF} 339 \n", "245 {E56280AE-5F42-AD31-2D95-C3418CB40985} 356 \n", "12 {6FC4B1BA-A7E4-A69C-BB44-CC26A6FD935B} 17 \n", "38 {E955A26F-4FC1-E8A3-40F1-D85F48D15ED9} 49 \n", "\n", "[338 rows x 5 columns]"]}, "execution_count": 35, "metadata": {}, "output_type": "execute_result"}], "source": ["method_df.sort_values(by='count', ascending=False)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The table above shows that Ozone is measure using ULTRA VIOLET ABSORPTION method. We can filter the dataframe and search for the ones we are interested in."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Average PM 2.5 value by county"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The function below filters the data by rows that give information about PM2.5 pollutant. To find the average PM2.5 value of each county, we will use [`join_features`](https://developers.arcgis.com/rest/services-reference/join-features.htm) tool. Finally, we will write the output to the datastore."]}, {"cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": ["def average():\n", " from datetime import datetime as dt\n", " df = layers[0]\n", " df = df.filter(df['Parameter Name'] == 'PM2.5 - Local Conditions')\n", " res = geoanalytics.join_features(target_layer=layers[1], \n", " join_layer=df, \n", " join_operation=\"JoinOneToOne\",\n", " summary_fields=[{'statisticType' : 'mean', 'onStatisticField' : 'Sample Measurement'}],\n", " spatial_relationship='Contains')\n", " res.write.format(\"webgis\").save(\"average_pm_by_county\" + str(dt.now().microsecond))"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["run_python_script(average, [air_lyr, usa_counties_lyr])"]}, {"cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": ["average_pm_by_county = gis.content.search('average_pm_by_county')[0]"]}, {"cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [{"data": {"text/html": ["
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Barometric_pressureCarbon_monoxideNitric_oxide__NO_Nitrogen_dioxide__NO2_OBJECTIDOutdoor_TemperatureOxides_of_nitrogen__NOx_OzonePM10_Total_0_10um_STPPM2_5___Local_ConditionsReactive_oxides_of_nitrogen__NOy_Relative_HumiditySulfur_dioxideWind_Direction___ResultantWind_Speed___Resultantdatetimeglobalidunique_id
01004.30.1330.801.8154.32.60.039NaN3.62.535.00.373.04.42017-02-13 10:00{35E5A035-34CC-85FB-4955-22F1325C9A40}010730023
1996.3NaN0.1512.3246.412.60.018NaN13.213.059.00.673.03.52017-12-14 22:00{B078A8A2-237E-4732-4C33-62B9D9F39D57}010730023
2995.8NaN31.4021.5366.255.90.00750.019.048.569.06.4150.02.02017-05-26 06:00{F45C8958-8061-FA8D-DDC8-9325DB224B77}010730023
3992.6NaN-0.052.7457.42.50.035NaNNaN2.956.00.5280.05.92018-01-22 22:00{85796BC1-2FE1-86C6-23BE-E70D9DABD1F7}010730023
41007.00.1480.156.9546.27.00.035NaN1.76.541.00.0119.03.32017-02-09 17:00{61606AF2-0DC2-52DF-3B01-B979C11CB37E}010730023
\n", "
"], "text/plain": [" Barometric_pressure Carbon_monoxide Nitric_oxide__NO_ \\\n", "0 1004.3 0.133 0.80 \n", "1 996.3 NaN 0.15 \n", "2 995.8 NaN 31.40 \n", "3 992.6 NaN -0.05 \n", "4 1007.0 0.148 0.15 \n", "\n", " Nitrogen_dioxide__NO2_ OBJECTID Outdoor_Temperature \\\n", "0 1.8 1 54.3 \n", "1 12.3 2 46.4 \n", "2 21.5 3 66.2 \n", "3 2.7 4 57.4 \n", "4 6.9 5 46.2 \n", "\n", " Oxides_of_nitrogen__NOx_ Ozone PM10_Total_0_10um_STP \\\n", "0 2.6 0.039 NaN \n", "1 12.6 0.018 NaN \n", "2 55.9 0.007 50.0 \n", "3 2.5 0.035 NaN \n", "4 7.0 0.035 NaN \n", "\n", " PM2_5___Local_Conditions Reactive_oxides_of_nitrogen__NOy_ \\\n", "0 3.6 2.5 \n", "1 13.2 13.0 \n", "2 19.0 48.5 \n", "3 NaN 2.9 \n", "4 1.7 6.5 \n", "\n", " Relative_Humidity Sulfur_dioxide Wind_Direction___Resultant \\\n", "0 35.0 0.3 73.0 \n", "1 59.0 0.6 73.0 \n", "2 69.0 6.4 150.0 \n", "3 56.0 0.5 280.0 \n", "4 41.0 0.0 119.0 \n", "\n", " Wind_Speed___Resultant datetime \\\n", "0 4.4 2017-02-13 10:00 \n", "1 3.5 2017-12-14 22:00 \n", "2 2.0 2017-05-26 06:00 \n", "3 5.9 2018-01-22 22:00 \n", "4 3.3 2017-02-09 17:00 \n", "\n", " globalid unique_id \n", "0 {35E5A035-34CC-85FB-4955-22F1325C9A40} 010730023 \n", "1 {B078A8A2-237E-4732-4C33-62B9D9F39D57} 010730023 \n", "2 {F45C8958-8061-FA8D-DDC8-9325DB224B77} 010730023 \n", "3 {85796BC1-2FE1-86C6-23BE-E70D9DABD1F7} 010730023 \n", "4 {61606AF2-0DC2-52DF-3B01-B979C11CB37E} 010730023 "]}, "execution_count": 42, "metadata": {}, "output_type": "execute_result"}], "source": ["series_data.query(as_df=True)[:5]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Predict PM2.5 using Facebook's Prophet model"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The functon below uses [pyspark pandas UDF](https://docs.microsoft.com/en-us/azure/databricks/spark/latest/spark-sql/udf-python-pandas#:~:text=A%20pandas%20user%2Ddefined%20function,%2Da%2Dtime%20Python%20UDFs.) function [fb-prophet model](https://facebook.github.io/prophet/) to foreast pm2.5 hourly value for the month of 2019 January."]}, {"cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": ["def predict_pm25():\n", " #imports\n", " from arcgis.gis import GIS\n", " gis = GIS(profile=\"your_enterprise_portal\")\n", " from datetime import datetime as dt\n", " from pyspark.sql.functions import concat, col, lit\n", " import pandas as pd\n", " import numpy as np\n", " from fbprophet import Prophet\n", " from pyspark.sql.functions import pandas_udf, PandasUDFType\n", " from pyspark.sql.types import StructType, StructField, DoubleType, IntegerType, FloatType, TimestampType\n", " import warnings\n", " warnings.filterwarnings('ignore')\n", " \n", " df1 = layers[0] #converts laayer into spark dataframe\n", " cols = ['Outdoor_Temperature', 'Ozone', 'PM10_Total_0_10um_STP',\n", " 'PM2_5___Local_Conditions',\n", " 'Wind_Direction___Resultant',\n", " 'Wind_Speed___Resultant', 'datetime']\n", " df1 = df1.select(cols) #filter data by columns needed\n", " df1 = df1.withColumn('flag', lit(1))\n", " schema = StructType([StructField('ds', TimestampType(), True), #schema of the resulting dataframe\n", " StructField('yhat_lower', FloatType(), True),\n", " StructField('yhat_upper', FloatType(), True),\n", " StructField('yhat', FloatType(), True),\n", " StructField('y', FloatType(), True)])\n", " \n", " @pandas_udf(schema, PandasUDFType.GROUPED_MAP)\n", " def forecast_pm25(df):\n", " #prepare data \n", " df['Date'] = df['datetime'].astype('datetime64[ns]')\n", " df['year'] = df['Date'].dt.year\n", " df.set_index('Date', inplace=True) \n", " df.sort_index(inplace=True)\n", " v = pd.date_range(start='2016-12-31 23:00:00', periods=18265, freq='H', closed='right') #get date range\n", " newdf = pd.DataFrame(index=v) \n", " # Fill missing dates \n", " historical=pd.merge(newdf, df, how='left', left_index=True, right_index=True)\n", " historical.interpolate(method='time', inplace=True)\n", " historical.reset_index(inplace=True)\n", " historical.rename(columns={'index': 'ds', 'PM2_5___Local_Conditions': 'y'}, inplace=True)\n", " historical.fillna(0, inplace=True)\n", " # handle zero and negative values for pm\n", " for i,item in enumerate(historical['y']):\n", " if item<=0:\n", " historical['y'].iloc[i]=historical['y'].iloc[i-1]\n", " else:\n", " historical['y'].iloc[i]=item\n", " \n", " for i,item in enumerate(historical['PM10_Total_0_10um_STP']):\n", " if item<=0:\n", " historical['PM10_Total_0_10um_STP'].iloc[i]=historical['PM10_Total_0_10um_STP'].iloc[i-1]\n", " else:\n", " historical['PM10_Total_0_10um_STP'].iloc[i]=item \n", " \n", " for i,item in enumerate(historical['Wind_Speed___Resultant']):\n", " if item<=0:\n", " historical['Wind_Speed___Resultant'].iloc[i]=historical['Wind_Speed___Resultant'].iloc[i-1]\n", " else:\n", " historical['Wind_Speed___Resultant'].iloc[i]=item\n", " \n", " for i,item in enumerate(historical['Wind_Direction___Resultant']):\n", " if item<=0:\n", " historical['Wind_Direction___Resultant'].iloc[i]=historical['Wind_Direction___Resultant'].iloc[i-1]\n", " else:\n", " historical['Wind_Direction___Resultant'].iloc[i]=item\n", " # split data into train and test \n", " train_df = historical[historical.year != 2019]\n", " test_df = historical[historical.year == 2019]\n", " test_df.drop(columns='y', inplace=True) \n", " # train model \n", " m = Prophet(daily_seasonality=True,\n", " weekly_seasonality=True)\n", " m.add_regressor('PM10_Total_0_10um_STP')\n", " m.add_regressor('Wind_Speed___Resultant')\n", " m.add_regressor('Wind_Direction___Resultant')\n", " m.fit(train_df);\n", " # predict on test data\n", " forecast = m.predict(test_df)\n", " # save plots locally\n", " plot1 = m.plot(forecast);\n", " plot2 = m.plot_components(forecast);\n", " plot1.savefig(r'/home/ags/localdatastore/fbdata/forecast.png')\n", " plot2.savefig(r'/home/ags/localdatastore/fbdata/cmponents.png')\n", " # Uncomment the following lines if you want to publish and visualize your graphs as as item.\n", "# gis = GIS('https://machinename/portal', 'username', 'password')\n", "# gis.content.add(item_properties={\"type\": \"Image\", \"title\": \"Forecast Plot\"}, data=r\"/home/ags/localdatastore/fbdata/forecast.png\")\n", "# gis.content.add(item_properties={\"type\": \"Image\", \"title\": \"Forecase Components2\"}, data=r\"/home/ags/localdatastore/fbdata/cmponents.png\")\n", " # create df with actual and predicted fields\n", " cmp_df = forecast.set_index('ds')[['yhat', 'yhat_lower', 'yhat_upper']].join(historical.set_index('ds'))\n", " cmp_df.reset_index(inplace=True)\n", " cmp_df = cmp_df[['ds', 'yhat_lower', 'yhat_upper', 'yhat', 'y']]\n", " return cmp_df\n", " res = df1.groupby(['flag']).apply(forecast_pm25)\n", "\n", " res.write.format(\"webgis\").save(\"predicted_results_on_test_data\" + str(dt.now().microsecond))"]}, {"cell_type": "code", "execution_count": null, "metadata": {"scrolled": true}, "outputs": [], "source": ["run_python_script(code=predict_pm25, layers=[series_data])"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["predicted_item = gis.content.search('predicted_results_on_test_data854829')[0]"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/html": ["
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\n", " predicted_results_on_test_data854829\n", " \n", "
Table Layer by admin\n", "
Last Modified: June 24, 2020\n", "
0 comments, 366 views\n", "
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\n", " "], "text/plain": [""]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["predicted_item"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": ["predicted_df = predicted_item[0].tables[0].query().sdf"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/plain": ["Index(['ds', 'yhat_lower', 'yhat_upper', 'yhat', 'y', 'globalid', 'OBJECTID'], dtype='object')"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["predicted_df.columns"]}, {"cell_type": "code", "execution_count": 9, "metadata": {"scrolled": true}, "outputs": [{"data": {"text/html": ["
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dsyhat_loweryhat_upperyhatyglobalidOBJECTID
02018-12-31 18:30:001.35776915.9411108.6910765.5{7BCD0AF8-F5B1-4A7E-7118-88589E0A4DAD}44
12018-12-31 19:30:001.14151216.2653528.7738485.7{747EF178-8A36-5CCC-74A3-4BAD71DB0553}244
22018-12-31 20:30:000.38937415.9279577.6410615.4{7D6C9381-7279-AB10-C5F7-AE138BFD6AE0}444
32018-12-31 21:30:00-0.16414115.0330847.1110854.4{B9BD44CF-F794-87F3-E389-E654CDCE8844}644
42018-12-31 22:30:00-0.58777614.4896347.0024204.4{B7C8C5C7-1C9E-E66A-CA41-39E5F0D601FA}844
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"], "text/plain": [" ds yhat_lower yhat_upper yhat y \\\n", "0 2018-12-31 18:30:00 1.357769 15.941110 8.691076 5.5 \n", "1 2018-12-31 19:30:00 1.141512 16.265352 8.773848 5.7 \n", "2 2018-12-31 20:30:00 0.389374 15.927957 7.641061 5.4 \n", "3 2018-12-31 21:30:00 -0.164141 15.033084 7.111085 4.4 \n", "4 2018-12-31 22:30:00 -0.587776 14.489634 7.002420 4.4 \n", "\n", " globalid OBJECTID \n", "0 {7BCD0AF8-F5B1-4A7E-7118-88589E0A4DAD} 44 \n", "1 {747EF178-8A36-5CCC-74A3-4BAD71DB0553} 244 \n", "2 {7D6C9381-7279-AB10-C5F7-AE138BFD6AE0} 444 \n", "3 {B9BD44CF-F794-87F3-E389-E654CDCE8844} 644 \n", "4 {B7C8C5C7-1C9E-E66A-CA41-39E5F0D601FA} 844 "]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["predicted_df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In the above table, y attribute shows the actual value of PM2.5 and yhat shows the values predicted by the trained model."]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Visualize result on Dashboard"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The `arcgis.apps` module includes [Dashboard](https://developers.arcgis.com/python/api-reference/arcgis.apps.dashboard.html) submodule to create dashboards programmatically. The dashboard submodule contains classes for different widgets which can be configured and be used to publish a dashboard.\n", "\n", "\n", "We want to visualize our predicted results on a dashboard. To learn more about Dashboards module, visit guide [here](../guide/authoring-arcgis-dashboards/)."]}, {"cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": ["from arcgis.apps.dashboard import SerialChart, add_column, add_row\n", "from arcgis.apps.dashboard import Dashboard\n", "from arcgis.apps.dashboard import SerialChart"]}, {"cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [{"data": {"text/html": [""], "text/plain": [""]}, "execution_count": 50, "metadata": {}, "output_type": "execute_result"}], "source": ["chart = SerialChart(predicted_item, #Create a serial chart\n", " categories_from=\"features\", \n", " title=\"Forecast of PM2.5 for Janumary 2019\") \n", "\n", "chart.data.category_field = \"ds\" #set category field\n", "\n", "chart.category_axis.title = \"datetime\" #set title for x axis\n", "\n", "chart.value_axis.title = \"pm2.5\" #set title for y axis\n", "\n", "#set fields to visualize on y axis\n", "chart.data.add_value_field('y', line_color='#CB4335') \n", "chart.data.add_value_field('yhat', line_color='#2980B9')\n", "chart.data.add_value_field('yhat_upper', line_color='#CACFD2')\n", "chart.data.add_value_field('yhat_lower', line_color='#CACFD2')\n", "\n", "chart.legend.visibility = True \n", "chart.legend.placement = \"side\"\n", "chart.category_axis.minimum_period = 'hours'\n", "chart.data.labels = False\n", "chart.data.hover_text = True\n", "chart.data.hover_text = True\n", "chart"]}, {"cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": ["agol_gis = GIS('home')"]}, {"cell_type": "code", "execution_count": 48, "metadata": {"scrolled": true}, "outputs": [{"data": {"text/html": ["
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Dashboard by api_data_owner\n", "
Last Modified: December 02, 2020\n", "
0 comments, 0 views\n", "
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\n", " "], "text/plain": [""]}, "execution_count": 48, "metadata": {}, "output_type": "execute_result"}], "source": ["dashboard = Dashboard() #creates a Dashboard object\n", "dashboard.layout = add_row([a_chart]) #adds one chat to the Dashboard\n", "dashboard.save('pm2.5_dashboard_2019_jan', #publishes the dashboard to the portal\n", " gis=agol_gis)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Conclusion"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In this notebook, we learnt how to use spark with geoanalytics server in order to carry out distributed computation of big data analysis. "]}, {"cell_type": "markdown", "metadata": {}, "source": ["## References"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- https://towardsdatascience.com/pyspark-forecasting-with-pandas-udf-and-fb-prophet-e9d70f86d802\n", "- https://stackoverflow.com/questions/61509033/forecasting-with-facebook-prophet-using-pandas-udf-in-spark\n", "- https://databricks.com/blog/2020/01/27/time-series-forecasting-prophet-spark.html"]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10"}}, "nbformat": 4, "nbformat_minor": 2} \ No newline at end of file diff --git a/samples/04_gis_analysts_data_scientists/part1_prepare_hurricane_data.ipynb b/samples/04_gis_analysts_data_scientists/part1_prepare_hurricane_data.ipynb deleted file mode 100644 index 2a2e7ff28f..0000000000 --- a/samples/04_gis_analysts_data_scientists/part1_prepare_hurricane_data.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Data Preparation - Hurricane analysis, part 1/3"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Table of Contents\n", "* [Introduction](#Introduction)\n", "* [Download hurricane data from NCEI FTP portal](#Download-hurricane-data-from-NCEI-FTP-portal)\n", " * [Download each file into the hurricanes_raw directory](#Download-each-file-into-the-hurricanes_raw-directory)\n", "* [Process CSV files by removing header rows](#Process-CSV-files-by-removing-header-rows)\n", " * [Automate across all files](#Automate-across-all-files)\n", "* [Cleaning hurricane observations with Dask](#Cleaning-hurricane-observations-with-Dask)\n", " * [Read input CSV data](#Read-input-CSV-data)\n", " * [Merge all location columns](#Merge-all-location-columns)\n", " * [Merge similar columns](#Merge-similar-columns)\n", " * [Merge wind columns](#Merge-wind-columns)\n", " * [Merge pressure columns](#Merge-pressure-columns)\n", " * [Merge grade columns](#Merge-grade-columns)\n", " * [Merge eye diameter columns](#Merge-eye-diameter-columns)\n", " * [Identify remaining redundant columns](#Identify-remaining-redundant-columns)\n", " * [Drop all redundant columns](#Drop-all-redundant-columns)\n", " * [Perform delayed computation](#Perform-delayed-computation)\n", " * [Preview results](#Preview-results)\n", "* [Creating hurricane tracks using Geoanalytics](#Creating-hurricane-tracks-using-Geoanalytics)\n", " * [Create a data store](#Create-a-data-store)\n", " * [Perform data aggregation using reconstruct tracks tool](#Perform-data-aggregation-using-reconstruct-tracks-tool)\n", " * [Execute reconstruct tracks tool](#Execute-reconstruct-tracks-tool)\n", " * [Analyze the result of aggregation](#Analyze-the-result-of-aggregation)\n", "* [Conclusion](#Conclusion)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Introduction"]}, {"cell_type": "markdown", "metadata": {}, "source": ["\n", "Hurricanes are large swirling storms that produce winds of speeds `74` miles per hour (`119` kmph) or higher. When hurricanes make a landfall, they produce heavy rainfall, cause storm surges and intense flooding. Often hurricanes strike places that are dense in population, causing devastating amounts of death and destruction throughout the world."]}, {"cell_type": "markdown", "metadata": {}, "source": [""]}, {"cell_type": "markdown", "metadata": {}, "source": ["Since the recent past, agencies such as the [National Hurricane Center](https://www.nhc.noaa.gov/aboutintro.shtml) have been collecting quantitative data about hurricanes. In this study we use meteorological data of hurricanes recorded in the past `169` years to analyze their location, intensity and investigate if there are any statistically significant trends. We also analyze the places most affected by hurricanes and what their demographic make up is. We conclude by citing relevant articles that draw similar conclusions."]}, {"cell_type": "markdown", "metadata": {}, "source": ["This notebook covers part 1 of this study. In this notebook, we:\n", "\n", " - download data from NCEI portal\n", " - do extensive pre-processing in the form of clearing headers, merging redundant columns\n", " - aggregate the observations into hurricane tracks."]}, {"cell_type": "markdown", "metadata": {}, "source": ["**Note**: To run this sample, you need a few extra libraries in your conda environment. If you don't have the libraries, install them by running the following commands from cmd.exe or your shell."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["pip install dask==2.14.0\n", "pip install toolz\n", "pip install fsspec==0.3.1"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Download hurricane data from NCEI FTP portal\n", "The [National Centers for Environmental Information](https://www.ncdc.noaa.gov/), formerly [National Climatic Data Center](https://www.ncdc.noaa.gov/) shares the historic hurricane track datasets at [ftp://eclipse.ncdc.noaa.gov/pub/ibtracs/v03r09/all/csv/](ftp://eclipse.ncdc.noaa.gov/pub/ibtracs/v03r09/all/csv/). We use the `ftplib` Python library to login in and download these datasets."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["# imports for downloading data from FTP site\n", "import os\n", "from ftplib import FTP\n", "\n", "# imports to process data using DASK\n", "from dask import delayed\n", "import dask.dataframe as ddf\n", "\n", "# imports for data analysis and visualization\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# imports to perform spatial aggregation using ArcGIS GeoAnalytics server\n", "from arcgis.gis import GIS\n", "from arcgis.geoanalytics import get_datastores\n", "from arcgis.geoanalytics.summarize_data import reconstruct_tracks\n", "import arcgis\n", "\n", "# miscellaneous imports\n", "from pprint import pprint\n", "from copy import deepcopy"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Establish an anonymous connection to FTP site."]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/plain": ["'230 Anonymous access granted, restrictions apply'"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["conn = FTP(host='eclipse.ncdc.noaa.gov')\n", "conn.login()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Change directory to folder containing the hurricane files. List the files."]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/plain": ["176"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["conn.cwd('/pub/ibtracs/v03r10/all/csv/year/')\n", "file_list = conn.nlst()\n", "len(file_list)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Print the top 10 items."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"text/plain": ["['Year.1842.ibtracs_all.v03r10.csv',\n", " 'Year.1843.ibtracs_all.v03r10.csv',\n", " 'Year.1844.ibtracs_all.v03r10.csv',\n", " 'Year.1845.ibtracs_all.v03r10.csv',\n", " 'Year.1846.ibtracs_all.v03r10.csv',\n", " 'Year.1847.ibtracs_all.v03r10.csv',\n", " 'Year.1848.ibtracs_all.v03r10.csv',\n", " 'Year.1849.ibtracs_all.v03r10.csv',\n", " 'Year.1850.ibtracs_all.v03r10.csv',\n", " 'Year.1851.ibtracs_all.v03r10.csv']"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["file_list[:10]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Download each file into the `hurricanes_raw` directory"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["data_dir = r'data/hurricanes_data/'"]}, {"cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [{"data": {"text/plain": ["['Allstorms.ibtracs_all.v03r09.csv',\n", " 'hurricanes_raw',\n", " '.nb_auth_file',\n", " 'Allstorms.ibtracs_all.v03r09.csv.gz']"]}, "execution_count": 31, "metadata": {}, "output_type": "execute_result"}], "source": ["if 'hurricanes_raw' not in os.listdir(data_dir):\n", " os.mkdir(os.path.join(data_dir,'hurricanes_raw'))\n", "\n", "hurricane_raw_dir = os.path.join(data_dir,'hurricanes_raw')\n", "os.listdir(data_dir)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now we are going to download data from 1842-2017, whih might take around 15 mins."]}, {"cell_type": "code", "execution_count": 11, "metadata": {"scrolled": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloaded Year.1842.ibtracs_all.v03r10.csv\n", "Downloaded Year.1843.ibtracs_all.v03r10.csv\n", "Downloaded Year.1844.ibtracs_all.v03r10.csv\n", "Downloaded Year.1845.ibtracs_all.v03r10.csv\n", "....Downloaded Year.2015.ibtracs_all.v03r10.csv\n", "Downloaded Year.2016.ibtracs_all.v03r10.csv\n", "Downloaded Year.2017.ibtracs_all.v03r10.csv\n", "CPU times: user 8.63 s, sys: 12.1 s, total: 20.8 s\n", "Wall time: 12min 5s\n"]}], "source": ["file_path = hurricane_raw_dir\n", "for file in file_list:\n", " with open(os.path.join(file_path, file), 'wb') as file_handle:\n", " try:\n", " conn.retrbinary('RETR ' + file, file_handle.write, 1024)\n", " print(f'Downloaded {file}')\n", " \n", " except Exception as download_ex:\n", " print(f'Error downloading {file} + {str(download_ex)}')"]}, {"cell_type": "markdown", "metadata": {"heading_collapsed": true}, "source": ["## Process CSV files by removing header rows\n", "The CSV files have multiple header rows. Let us start by processing one of the files as an example."]}, {"cell_type": "code", "execution_count": 2, "metadata": {"hidden": true}, "outputs": [], "source": ["csv_path = os.path.join(hurricane_raw_dir,'Year.2017.ibtracs_all.v03r10.csv')"]}, {"cell_type": "code", "execution_count": 18, "metadata": {"hidden": true}, "outputs": [{"data": {"text/html": ["
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IBTrACS -- Version: v03r10
Serial_NumSeasonNumBasinSub_basinNameISO_timeNatureLatitudeLongitudeWind(WMO)Pres(WMO)CenterWind(WMO) PercentilePres(WMO) PercentileTrack_typeLatitude_for_mappingLongitude_for_mappingCurrent Basinhurdat_atl_lathurdat_atl_lonhurdat_atl_gradehurdat_atl_windhurdat_atl_prestd9636_lattd9636_lontd9636_gradetd9636_windtd9636_presreunion_latreunion_lonreunion_gradereunion_windreunion_presatcf_latatcf_lonatcf_gradeatcf_windatcf_presmlc_natl_latmlc_natl_lonmlc_natl_grademlc_natl_windmlc_natl_presds824_sh_latds824_sh_londs824_sh_gradeds824_sh_windds824_sh_presds824_ni_latds824_ni_londs824_ni_gradeds824_ni_windds824_ni_presbom_latbom_lonbom_gradebom_windbom_presds824_au_latds824_au_londs824_au_gradeds824_au_windds824_au_presjtwc_sh_latjtwc_sh_lonjtwc_sh_gradejtwc_sh_windjtwc_sh_presjtwc_wp_latjtwc_wp_lonjtwc_wp_gradejtwc_wp_windjtwc_wp_prestd9635_lattd9635_lontd9635_gradetd9635_windtd9635_presds824_wp_latds824_wp_londs824_wp_gradeds824_wp_windds824_wp_presjtwc_io_latjtwc_io_lonjtwc_io_gradejtwc_io_windjtwc_io_prescma_latcma_loncma_gradecma_windcma_preshurdat_epa_lathurdat_epa_lonhurdat_epa_gradehurdat_epa_windhurdat_epa_presjtwc_ep_latjtwc_ep_lonjtwc_ep_gradejtwc_ep_windjtwc_ep_presds824_ep_latds824_ep_londs824_ep_gradeds824_ep_windds824_ep_presjtwc_cp_latjtwc_cp_lonjtwc_cp_gradejtwc_cp_windjtwc_cp_prestokyo_lattokyo_lontokyo_gradetokyo_windtokyo_presneumann_latneumann_lonneumann_gradeneumann_windneumann_preshko_lathko_lonhko_gradehko_windhko_prescphc_latcphc_loncphc_gradecphc_windcphc_preswellington_latwellington_lonwellington_gradewellington_windwellington_presnewdelhi_latnewdelhi_lonnewdelhi_gradenewdelhi_windnewdelhi_presnadi_latnadi_lonnadi_gradenadi_windnadi_presreunion_rmwreunion_wind_radii_1_nereunion_wind_radii_1_sereunion_wind_radii_1_swreunion_wind_radii_1_nwreunion_wind_radii_2_nereunion_wind_radii_2_sereunion_wind_radii_2_swreunion_wind_radii_2_nwbom_mn_hurr_xtntbom_mn_gale_xtntbom_mn_eye_diambom_rociatcf_rmwatcf_pociatcf_rociatcf_eyeatcf_wrad34_rad1atcf_wrad34_rad2atcf_wrad34_rad3atcf_wrad34_rad4atcf_wrad50_rad1atcf_wrad50_rad2atcf_wrad50_rad3atcf_wrad50_rad4atcf_wrad64_rad1atcf_wrad64_rad2atcf_wrad64_rad3atcf_wrad64_rad4tokyo_dir50tokyo_long50tokyo_short50tokyo_dir30tokyo_long30tokyo_short30jtwc_??_rmwjtwc_??_pocijtwc_??_rocijtwc_??_eyejtwc_??_wrad34_rad1jtwc_??_wrad34_rad2jtwc_??_wrad34_rad3jtwc_??_wrad34_rad4jtwc_??_wrad50_rad1jtwc_??_wrad50_rad2jtwc_??_wrad50_rad3jtwc_??_wrad50_rad4jtwc_??_wrad64_rad1jtwc_??_wrad64_rad2jtwc_??_wrad64_rad3jtwc_??_wrad64_rad4
NaNYear#BBBBNaNYYYY-MM-DD HH:MM:SSNaNdeg_northdeg_eastktmbNaN%%NaNdegrees_northdegrees_eastNaNdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbdeg_northdeg_eastktmbnmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilembnmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmileQuadnmilenmileQuadnmilenmilenmilembnmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmilenmile
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\n", "
"], "text/plain": [" IBTrACS -- Version: v03r10\n", "Serial_Num Season Num Basin Sub_basin Name ISO_time Nature Latitude Longitude Wind(WMO) Pres(WMO) Center Wind(WMO) Percentile Pres(WMO) Percentile Track_type Latitude_for_mapping Longitude_for_mapping Current Basin hurdat_atl_lat hurdat_atl_lon hurdat_atl_grade hurdat_atl_wind hurdat_atl_pres td9636_lat td9636_lon td9636_grade td9636_wind td9636_pres reunion_lat reunion_lon reunion_grade reunion_wind reunion_pres atcf_lat atcf_lon atcf_grade atcf_wind atcf_pres mlc_natl_lat mlc_natl_lon mlc_natl_grade mlc_natl_wind mlc_natl_pres ds824_sh_lat ds824_sh_lon ds824_sh_grade ds824_sh_wind ds824_sh_pres ds824_ni_lat ds824_ni_lon ds824_ni_grade ds824_ni_wind ds824_ni_pres bom_lat bom_lon bom_grade bom_wind bom_pres ds824_au_lat ds824_au_lon ds824_au_grade ds824_au_wind ds824_au_pres jtwc_sh_lat jtwc_sh_lon jtwc_sh_grade jtwc_sh_wind jtwc_sh_pres jtwc_wp_lat jtwc_wp_lon jtwc_wp_grade jtwc_wp_wind jtwc_wp_pres td9635_lat td9635_lon td9635_grade td9635_wind td9635_pres ds824_wp_lat ds824_wp_lon ds824_wp_grade ds824_wp_wind ds824_wp_pres jtwc_io_lat jtwc_io_lon jtwc_io_grade jtwc_io_wind jtwc_io_pres cma_lat cma_lon cma_grade cma_wind cma_pres hurdat_epa_lat hurdat_epa_lon hurdat_epa_grade hurdat_epa_wind hurdat_epa_pres jtwc_ep_lat jtwc_ep_lon jtwc_ep_grade jtwc_ep_wind jtwc_ep_pres ds824_ep_lat ds824_ep_lon ds824_ep_grade ds824_ep_wind ds824_ep_pres jtwc_cp_lat jtwc_cp_lon jtwc_cp_grade jtwc_cp_wind jtwc_cp_pres tokyo_lat tokyo_lon tokyo_grade tokyo_wind tokyo_pres neumann_lat neumann_lon neumann_grade neumann_wind neumann_pres hko_lat hko_lon hko_grade hko_wind hko_pres cphc_lat cphc_lon cphc_grade cphc_wind cphc_pres wellington_lat wellington_lon wellington_grade wellington_wind wellington_pres newdelhi_lat newdelhi_lon newdelhi_grade newdelhi_wind newdelhi_pres nadi_lat nadi_lon nadi_grade nadi_wind nadi_pres reunion_rmw reunion_wind_radii_1_ne reunion_wind_radii_1_se reunion_wind_radii_1_sw reunion_wind_radii_1_nw reunion_wind_radii_2_ne reunion_wind_radii_2_se reunion_wind_radii_2_sw reunion_wind_radii_2_nw bom_mn_hurr_xtnt bom_mn_gale_xtnt bom_mn_eye_diam bom_roci atcf_rmw atcf_poci atcf_roci atcf_eye atcf_wrad34_rad1 atcf_wrad34_rad2 atcf_wrad34_rad3 atcf_wrad34_rad4 atcf_wrad50_rad1 atcf_wrad50_rad2 atcf_wrad50_rad3 atcf_wrad50_rad4 atcf_wrad64_rad1 atcf_wrad64_rad2 atcf_wrad64_rad3 atcf_wrad64_rad4 tokyo_dir50 tokyo_long50 tokyo_short50 tokyo_dir30 tokyo_long30 tokyo_short30 jtwc_??_rmw jtwc_??_poci jtwc_??_roci jtwc_??_eye jtwc_??_wrad34_rad1 jtwc_??_wrad34_rad2 jtwc_??_wrad34_rad3 jtwc_??_wrad34_rad4 jtwc_??_wrad50_rad1 jtwc_??_wrad50_rad2 jtwc_??_wrad50_rad3 jtwc_??_wrad50_rad4 jtwc_??_wrad64_rad1 jtwc_??_wrad64_rad2 jtwc_??_wrad64_rad3 jtwc_??_wrad64_rad4\n", "NaN Year # BB BB NaN YYYY-MM-DD HH:MM:SS NaN deg_north deg_east kt mb NaN % % NaN degrees_north degrees_east NaN deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb deg_north deg_east kt mb nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile mb nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile Quad nmile nmile Quad nmile nmile nmile mb nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile nmile\n", "1874011S14064 1874 01 SI MM XXXX874148 1874-01-11 06:00:00 NR -13.70 63.90 0.0 0.0 reunion -100.000 -100.000 main -13.70 63.90 SI -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -13.7 63.9 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000\n", " 1874-01-11 12:00:00 NR -999. -999. -999. -999. NaN -999. -999. main -13.75 63.86 SI -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -13.7 63.9 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000\n", " 1874-01-11 18:00:00 NR -999. -999. -999. -999. NaN -999. -999. main -13.88 63.77 SI -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -13.9 63.8 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000 -999.000"]}, "execution_count": 18, "metadata": {}, "output_type": "execute_result"}], "source": ["df = pd.read_csv(csv_path)\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["The input looks mangled. This is because the file's row `1` has a header that pandas fails to read. So let us skip that row."]}, {"cell_type": "code", "execution_count": 19, "metadata": {"hidden": true}, "outputs": [{"data": {"text/html": ["
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Serial_NumSeasonNumBasinSub_basinNameISO_timeNatureLatitudeLongitude...jtwc_??_wrad34_rad3jtwc_??_wrad34_rad4jtwc_??_wrad50_rad1jtwc_??_wrad50_rad2jtwc_??_wrad50_rad3jtwc_??_wrad50_rad4jtwc_??_wrad64_rad1jtwc_??_wrad64_rad2jtwc_??_wrad64_rad3jtwc_??_wrad64_rad4
0NaNYear#BBBBNaNYYYY-MM-DD HH:MM:SSNaNdeg_northdeg_east...nmilenmilenmilenmilenmilenmilenmilenmilenmilenmile
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41874011S14064187401SIMMXXXX8741481874-01-12 00:00:00NR-999.-999....-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000
\n", "

5 rows \u00d7 200 columns

\n", "
"], "text/plain": [" Serial_Num Season Num Basin Sub_basin Name ISO_time \\\n", "0 NaN Year # BB BB NaN YYYY-MM-DD HH:MM:SS \n", "1 1874011S14064 1874 01 SI MM XXXX874148 1874-01-11 06:00:00 \n", "2 1874011S14064 1874 01 SI MM XXXX874148 1874-01-11 12:00:00 \n", "3 1874011S14064 1874 01 SI MM XXXX874148 1874-01-11 18:00:00 \n", "4 1874011S14064 1874 01 SI MM XXXX874148 1874-01-12 00:00:00 \n", "\n", " Nature Latitude Longitude ... jtwc_??_wrad34_rad3 \\\n", "0 NaN deg_north deg_east ... nmile \n", "1 NR -13.70 63.90 ... -999.000 \n", "2 NR -999. -999. ... -999.000 \n", "3 NR -999. -999. ... -999.000 \n", "4 NR -999. -999. ... -999.000 \n", "\n", " jtwc_??_wrad34_rad4 jtwc_??_wrad50_rad1 jtwc_??_wrad50_rad2 \\\n", "0 nmile nmile nmile \n", "1 -999.000 -999.000 -999.000 \n", "2 -999.000 -999.000 -999.000 \n", "3 -999.000 -999.000 -999.000 \n", "4 -999.000 -999.000 -999.000 \n", "\n", " jtwc_??_wrad50_rad3 jtwc_??_wrad50_rad4 jtwc_??_wrad64_rad1 \\\n", "0 nmile nmile nmile \n", "1 -999.000 -999.000 -999.000 \n", "2 -999.000 -999.000 -999.000 \n", "3 -999.000 -999.000 -999.000 \n", "4 -999.000 -999.000 -999.000 \n", "\n", " jtwc_??_wrad64_rad2 jtwc_??_wrad64_rad3 jtwc_??_wrad64_rad4 \n", "0 nmile nmile nmile \n", "1 -999.000 -999.000 -999.000 \n", "2 -999.000 -999.000 -999.000 \n", "3 -999.000 -999.000 -999.000 \n", "4 -999.000 -999.000 -999.000 \n", "\n", "[5 rows x 200 columns]"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["df = pd.read_csv(csv_path, skiprows=1)\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["A little better. But the file's 3rd row is also a header. Let us drop that row."]}, {"cell_type": "code", "execution_count": 20, "metadata": {"hidden": true}, "outputs": [{"data": {"text/html": ["
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Serial_NumSeasonNumBasinSub_basinNameISO_timeNatureLatitudeLongitude...jtwc_??_wrad34_rad3jtwc_??_wrad34_rad4jtwc_??_wrad50_rad1jtwc_??_wrad50_rad2jtwc_??_wrad50_rad3jtwc_??_wrad50_rad4jtwc_??_wrad64_rad1jtwc_??_wrad64_rad2jtwc_??_wrad64_rad3jtwc_??_wrad64_rad4
11874011S14064187401SIMMXXXX8741481874-01-11 06:00:00NR-13.7063.90...-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000
21874011S14064187401SIMMXXXX8741481874-01-11 12:00:00NR-999.-999....-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000
31874011S14064187401SIMMXXXX8741481874-01-11 18:00:00NR-999.-999....-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000
41874011S14064187401SIMMXXXX8741481874-01-12 00:00:00NR-999.-999....-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000
51874011S14064187401SIMMXXXX8741481874-01-12 06:00:00NR-14.8063.30...-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000-999.000
\n", "

5 rows \u00d7 200 columns

\n", "
"], "text/plain": [" Serial_Num Season Num Basin Sub_basin Name ISO_time \\\n", "1 1874011S14064 1874 01 SI MM XXXX874148 1874-01-11 06:00:00 \n", "2 1874011S14064 1874 01 SI MM XXXX874148 1874-01-11 12:00:00 \n", "3 1874011S14064 1874 01 SI MM XXXX874148 1874-01-11 18:00:00 \n", "4 1874011S14064 1874 01 SI MM XXXX874148 1874-01-12 00:00:00 \n", "5 1874011S14064 1874 01 SI MM XXXX874148 1874-01-12 06:00:00 \n", "\n", " Nature Latitude Longitude ... jtwc_??_wrad34_rad3 \\\n", "1 NR -13.70 63.90 ... -999.000 \n", "2 NR -999. -999. ... -999.000 \n", "3 NR -999. -999. ... -999.000 \n", "4 NR -999. -999. ... -999.000 \n", "5 NR -14.80 63.30 ... -999.000 \n", "\n", " jtwc_??_wrad34_rad4 jtwc_??_wrad50_rad1 jtwc_??_wrad50_rad2 \\\n", "1 -999.000 -999.000 -999.000 \n", "2 -999.000 -999.000 -999.000 \n", "3 -999.000 -999.000 -999.000 \n", "4 -999.000 -999.000 -999.000 \n", "5 -999.000 -999.000 -999.000 \n", "\n", " jtwc_??_wrad50_rad3 jtwc_??_wrad50_rad4 jtwc_??_wrad64_rad1 \\\n", "1 -999.000 -999.000 -999.000 \n", "2 -999.000 -999.000 -999.000 \n", "3 -999.000 -999.000 -999.000 \n", "4 -999.000 -999.000 -999.000 \n", "5 -999.000 -999.000 -999.000 \n", "\n", " jtwc_??_wrad64_rad2 jtwc_??_wrad64_rad3 jtwc_??_wrad64_rad4 \n", "1 -999.000 -999.000 -999.000 \n", "2 -999.000 -999.000 -999.000 \n", "3 -999.000 -999.000 -999.000 \n", "4 -999.000 -999.000 -999.000 \n", "5 -999.000 -999.000 -999.000 \n", "\n", "[5 rows x 200 columns]"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["df.drop(labels=0, axis=0, inplace=True)\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["### Automate across all files\n", "Now we need to repeat the above cleaning steps across all CSV files. In the steps below, we will read all CSV files, drop the headers, and write to disk. This step is necessary as it will ease subsequent processing using the DASK library."]}, {"cell_type": "code", "execution_count": 9, "metadata": {"hidden": true, "scrolled": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Processed Year.1842.ibtracs_all.v03r10.csv\n", "Processed Year.1843.ibtracs_all.v03r10.csv\n", "Processed Year.1844.ibtracs_all.v03r10.csv\n", "Processed Year.1845.ibtracs_all.v03r10.csv\n", "...Processed Year.2013.ibtracs_all.v03r10.csv\n", "Processed Year.2014.ibtracs_all.v03r10.csv\n", "Processed Year.2015.ibtracs_all.v03r10.csv\n", "Processed Year.2016.ibtracs_all.v03r10.csv\n", "Processed Year.2017.ibtracs_all.v03r10.csv\n", "CPU times: user 36.4 s, sys: 3.39 s, total: 39.8 s\n", "Wall time: 46.8 s\n"]}], "source": ["file_path = hurricane_raw_dir\n", "num_records = {}\n", "for file in file_list:\n", " df = pd.read_csv(os.path.join(file_path, file), skiprows=1)\n", " num_records[str(file.split('.')[1])] = df.shape[0]\n", " \n", " df.drop(labels=0, axis=0, inplace=True)\n", " df.to_csv(os.path.join(file_path, file))\n", " print(f'Processed {file}')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Cleaning hurricane observations with Dask\n", "The data collected from NOAA NCDC source is just too large to clean with Pandas or Excel. With `350,000 x 200` in dense matrix, this data is larger than memory for a normal computer. Hence traditional packages such as Pandas cannot be used as they expect data to fit fully in memory.\n", "\n", "Thus, in this part of the study, we use **[Dask](https://dask.org/)**, a distributed data analysis library. Functionally, Dask provides a `DataFrame` object that behaves similar to a traditional pandas `DataFrame` object. You can perform slicing, dicing, exploration on them. However transformative operations on the `DataFrame` get queued and are operated only when necessary. When executed, Dask will read data in chunks, distribute it to workers (be it cores on a single machine or multiple machines in a cluster set up) and collect the data back for you. Thus, DASK allows you to work with any larger than memory dataset as it performs operations on chunks of it, in a distributed manner."]}, {"cell_type": "markdown", "metadata": {"heading_collapsed": true}, "source": ["## Read input CSV data\n", "As mentioned earlier, DASK allows you to work with larger than memory datasets. These datasets can reside as one large file or as multiple files in a folder. For the latter, DASK allows you to just specify the folder containing the datasets as input. In turn, it provides you a single `DataFrame` object that represents all your datasets combined together. The operations you perform on this `DataFrame` get queued and executed only when necessary."]}, {"cell_type": "code", "execution_count": 3, "metadata": {"collapsed": true, "hidden": true}, "outputs": [], "source": ["fld_path = hurricane_raw_dir\n", "csv_path = os.path.join(fld_path,'*.csv')"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["Preemptively, specify the assortment of values that should be treated as null values."]}, {"cell_type": "code", "execution_count": 36, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 1.26 s, sys: 17.6 ms, total: 1.28 s\n", "Wall time: 1.29 s\n"]}], "source": ["table_na_values=['-999.','-999','-999.000', '-1', '-1.0','0','0.0']\n", "full_df = ddf.read_csv(csv_path, na_values=table_na_values, dtype={'Center': 'object'})"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["You can query the top few (or bottom few) records as you do on a regular Pandas `DataFrame` object."]}, {"cell_type": "code", "execution_count": 37, "metadata": {"hidden": true}, "outputs": [{"data": {"text/html": ["
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Unnamed: 0Serial_NumSeasonNumBasinSub_basinNameISO_timeNatureLatitude...jtwc_??_wrad34_rad3jtwc_??_wrad34_rad4jtwc_??_wrad50_rad1jtwc_??_wrad50_rad2jtwc_??_wrad50_rad3jtwc_??_wrad50_rad4jtwc_??_wrad64_rad1jtwc_??_wrad64_rad2jtwc_??_wrad64_rad3jtwc_??_wrad64_rad4
011842298N1108018421NIBBNOT NAMED1842-10-25 06:00:00NRNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
121842298N1108018421NIBBNOT NAMED1842-10-25 12:00:00NRNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
231842298N1108018421NIASNOT NAMED1842-10-25 18:00:00NRNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
341842298N1108018421NIASNOT NAMED1842-10-26 00:00:00NRNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
451842298N1108018421NIASNOT NAMED1842-10-26 06:00:00NRNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows \u00d7 201 columns

\n", "
"], "text/plain": [" Unnamed: 0 Serial_Num Season Num Basin Sub_basin Name \\\n", "0 1 1842298N11080 1842 1 NI BB NOT NAMED \n", "1 2 1842298N11080 1842 1 NI BB NOT NAMED \n", "2 3 1842298N11080 1842 1 NI AS NOT NAMED \n", "3 4 1842298N11080 1842 1 NI AS NOT NAMED \n", "4 5 1842298N11080 1842 1 NI AS NOT NAMED \n", "\n", " ISO_time Nature Latitude ... \\\n", "0 1842-10-25 06:00:00 NR NaN ... \n", "1 1842-10-25 12:00:00 NR NaN ... \n", "2 1842-10-25 18:00:00 NR NaN ... \n", "3 1842-10-26 00:00:00 NR NaN ... \n", "4 1842-10-26 06:00:00 NR NaN ... \n", "\n", " jtwc_??_wrad34_rad3 jtwc_??_wrad34_rad4 jtwc_??_wrad50_rad1 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " jtwc_??_wrad50_rad2 jtwc_??_wrad50_rad3 jtwc_??_wrad50_rad4 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " jtwc_??_wrad64_rad1 jtwc_??_wrad64_rad2 jtwc_??_wrad64_rad3 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " jtwc_??_wrad64_rad4 \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", "[5 rows x 201 columns]"]}, "execution_count": 37, "metadata": {}, "output_type": "execute_result"}], "source": ["full_df.head()"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["Drop the first duplicate index column."]}, {"cell_type": "code", "execution_count": 38, "metadata": {"collapsed": true, "hidden": true}, "outputs": [], "source": ["full_df = full_df.drop(labels=['Unnamed: 0'], axis=1)"]}, {"cell_type": "code", "execution_count": 39, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["200"]}, "execution_count": 39, "metadata": {}, "output_type": "execute_result"}], "source": ["all_columns=list(full_df.columns)\n", "len(all_columns)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["This dataset has `200` columns. Not all are unique, as you can see from the print out below:"]}, {"cell_type": "code", "execution_count": 40, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["['Serial_Num', 'Season', 'Num', 'Basin', 'Sub_basin', 'Name', 'ISO_time', 'Nature', 'Latitude',\n", " 'Longitude', 'Wind(WMO)', 'Pres(WMO)', 'Center', 'Wind(WMO) Percentile', 'Pres(WMO) Percentile',\n", " 'Track_type', 'Latitude_for_mapping', 'Longitude_for_mapping', 'Current Basin', 'hurdat_atl_lat',\n", " 'hurdat_atl_lon', 'hurdat_atl_grade', 'hurdat_atl_wind', 'hurdat_atl_pres', 'td9636_lat',\n", " 'td9636_lon', 'td9636_grade', 'td9636_wind', 'td9636_pres', 'reunion_lat', 'reunion_lon',\n", " 'reunion_grade', 'reunion_wind', 'reunion_pres', 'atcf_lat', 'atcf_lon', 'atcf_grade', 'atcf_wind',\n", " 'atcf_pres', 'mlc_natl_lat', 'mlc_natl_lon', 'mlc_natl_grade', 'mlc_natl_wind', 'mlc_natl_pres',\n", " 'ds824_sh_lat', 'ds824_sh_lon', 'ds824_sh_grade', 'ds824_sh_wind', 'ds824_sh_pres', 'ds824_ni_lat',\n", " 'ds824_ni_lon', 'ds824_ni_grade', 'ds824_ni_wind', 'ds824_ni_pres', 'bom_lat', 'bom_lon',\n", " 'bom_grade', 'bom_wind', 'bom_pres', 'ds824_au_lat', 'ds824_au_lon', 'ds824_au_grade',\n", " 'ds824_au_wind', 'ds824_au_pres', 'jtwc_sh_lat', 'jtwc_sh_lon', 'jtwc_sh_grade', 'jtwc_sh_wind',\n", " 'jtwc_sh_pres', 'jtwc_wp_lat', 'jtwc_wp_lon', 'jtwc_wp_grade', 'jtwc_wp_wind', 'jtwc_wp_pres',\n", " 'td9635_lat', 'td9635_lon', 'td9635_grade', 'td9635_wind', 'td9635_pres', 'ds824_wp_lat',\n", " 'ds824_wp_lon', 'ds824_wp_grade', 'ds824_wp_wind', 'ds824_wp_pres', 'jtwc_io_lat', 'jtwc_io_lon',\n", " 'jtwc_io_grade', 'jtwc_io_wind', 'jtwc_io_pres', 'cma_lat', 'cma_lon', 'cma_grade', 'cma_wind',\n", " 'cma_pres', 'hurdat_epa_lat', 'hurdat_epa_lon', 'hurdat_epa_grade', 'hurdat_epa_wind',\n", " 'hurdat_epa_pres', 'jtwc_ep_lat', 'jtwc_ep_lon', 'jtwc_ep_grade', 'jtwc_ep_wind', 'jtwc_ep_pres',\n", " 'ds824_ep_lat', 'ds824_ep_lon', 'ds824_ep_grade', 'ds824_ep_wind', 'ds824_ep_pres', 'jtwc_cp_lat',\n", " 'jtwc_cp_lon', 'jtwc_cp_grade', 'jtwc_cp_wind', 'jtwc_cp_pres', 'tokyo_lat', 'tokyo_lon',\n", " 'tokyo_grade', 'tokyo_wind', 'tokyo_pres', 'neumann_lat', 'neumann_lon', 'neumann_grade',\n", " 'neumann_wind', 'neumann_pres', 'hko_lat', 'hko_lon', 'hko_grade', 'hko_wind', 'hko_pres',\n", " 'cphc_lat', 'cphc_lon', 'cphc_grade', 'cphc_wind', 'cphc_pres', 'wellington_lat', 'wellington_lon',\n", " 'wellington_grade', 'wellington_wind', 'wellington_pres', 'newdelhi_lat', 'newdelhi_lon',\n", " 'newdelhi_grade', 'newdelhi_wind', 'newdelhi_pres', 'nadi_lat', 'nadi_lon', 'nadi_grade',\n", " 'nadi_wind', 'nadi_pres', 'reunion_rmw', 'reunion_wind_radii_1_ne', 'reunion_wind_radii_1_se',\n", " 'reunion_wind_radii_1_sw', 'reunion_wind_radii_1_nw', 'reunion_wind_radii_2_ne',\n", " 'reunion_wind_radii_2_se', 'reunion_wind_radii_2_sw', 'reunion_wind_radii_2_nw',\n", " 'bom_mn_hurr_xtnt', 'bom_mn_gale_xtnt', 'bom_mn_eye_diam', 'bom_roci', 'atcf_rmw', 'atcf_poci',\n", " 'atcf_roci', 'atcf_eye', 'atcf_wrad34_rad1', 'atcf_wrad34_rad2', 'atcf_wrad34_rad3',\n", " 'atcf_wrad34_rad4', 'atcf_wrad50_rad1', 'atcf_wrad50_rad2', 'atcf_wrad50_rad3', 'atcf_wrad50_rad4',\n", " 'atcf_wrad64_rad1', 'atcf_wrad64_rad2', 'atcf_wrad64_rad3', 'atcf_wrad64_rad4', 'tokyo_dir50',\n", " 'tokyo_long50', 'tokyo_short50', 'tokyo_dir30', 'tokyo_long30', 'tokyo_short30', 'jtwc_??_rmw',\n", " 'jtwc_??_poci', 'jtwc_??_roci', 'jtwc_??_eye', 'jtwc_??_wrad34_rad1', 'jtwc_??_wrad34_rad2',\n", " 'jtwc_??_wrad34_rad3', 'jtwc_??_wrad34_rad4', 'jtwc_??_wrad50_rad1', 'jtwc_??_wrad50_rad2',\n", " 'jtwc_??_wrad50_rad3', 'jtwc_??_wrad50_rad4', 'jtwc_??_wrad64_rad1', 'jtwc_??_wrad64_rad2',\n", " 'jtwc_??_wrad64_rad3', 'jtwc_??_wrad64_rad4']\n"]}], "source": ["pprint(all_columns, compact=True, width=100)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["Reading the metadata from NOAA NCDC site, we find sensor measurements get unique columns if they are collected by a different agency. Thus we find multiple **pressure**, **wind speed**, **latitude**, **longitude**, etc. columns with different suffixes and prefixes. Data is sparse as it gets distributed between these columns. For our geospatial analysis, it suffices if we can merge these columns together and get location information from the coordinates."]}, {"cell_type": "markdown", "metadata": {"heading_collapsed": true}, "source": ["## Merge all location columns\n", "Below we prototype merging location columns. If this succeeds, we will proceed to merge all remaining columns."]}, {"cell_type": "code", "execution_count": 41, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["('Latitude', 'Longitude')\n", "('Latitude_for_mapping', 'Longitude_for_mapping')\n", "('hurdat_atl_lat', 'hurdat_atl_lon')\n", "('td9636_lat', 'td9636_lon')\n", "('reunion_lat', 'reunion_lon')\n", "('atcf_lat', 'atcf_lon')\n", "('mlc_natl_lat', 'mlc_natl_lon')\n", "('ds824_sh_lat', 'ds824_sh_lon')\n", "('ds824_ni_lat', 'ds824_ni_lon')\n", "('bom_lat', 'bom_lon')\n", "('ds824_au_lat', 'ds824_au_lon')\n", "('jtwc_sh_lat', 'jtwc_sh_lon')\n", "('jtwc_wp_lat', 'jtwc_wp_lon')\n", "('td9635_lat', 'td9635_lon')\n", "('ds824_wp_lat', 'ds824_wp_lon')\n", "('jtwc_io_lat', 'jtwc_io_lon')\n", "('cma_lat', 'cma_lon')\n", "('hurdat_epa_lat', 'hurdat_epa_lon')\n", "('jtwc_ep_lat', 'jtwc_ep_lon')\n", "('ds824_ep_lat', 'ds824_ep_lon')\n", "('jtwc_cp_lat', 'jtwc_cp_lon')\n", "('tokyo_lat', 'tokyo_lon')\n", "('neumann_lat', 'neumann_lon')\n", "('hko_lat', 'hko_lon')\n", "('cphc_lat', 'cphc_lon')\n", "('wellington_lat', 'wellington_lon')\n", "('newdelhi_lat', 'newdelhi_lon')\n", "('nadi_lat', 'nadi_lon')\n"]}], "source": ["lat_columns = [x for x in all_columns if 'lat' in x.lower()]\n", "lon_columns = [x for x in all_columns if 'lon' in x.lower()]\n", "for x in zip(lat_columns, lon_columns):\n", " print(x)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["In this dataset, if data is collected by 1 agency, the corresponding duplicate columns from other agencies are empty. However there may be exceptions. Hence we define a custom function that will pick median value for a row, from a given list of columns. This way, we can consolidate latitude / longitude information from all the agencies."]}, {"cell_type": "code", "execution_count": 42, "metadata": {"collapsed": true, "hidden": true}, "outputs": [], "source": ["def pick_median_value(row, col_list):\n", " return row[col_list].median()"]}, {"cell_type": "code", "execution_count": 43, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 56.9 ms, sys: 5.31 ms, total: 62.2 ms\n", "Wall time: 58.3 ms\n"]}], "source": ["full_df['latitude_merged'] = full_df.apply(pick_median_value, axis=1,\n", " col_list = lat_columns)"]}, {"cell_type": "code", "execution_count": 44, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 58.3 ms, sys: 5.43 ms, total: 63.7 ms\n", "Wall time: 59.1 ms\n"]}], "source": ["full_df['longitude_merged'] = full_df.apply(pick_median_value, axis=1,\n", " col_list = lon_columns)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["With `dask`, the above operation was delayed and stored in a queue. It has not been evaluated yet. Next, let us evaluate for `5` records and print output. If results look good, we will merge all remaining related columns together."]}, {"cell_type": "code", "execution_count": 45, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 137 ms, sys: 6.17 ms, total: 143 ms\n", "Wall time: 141 ms\n"]}, {"data": {"text/html": ["
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Serial_NumSeasonNumBasinSub_basinNameISO_timeNatureLatitudeLongitude...jtwc_??_wrad50_rad1jtwc_??_wrad50_rad2jtwc_??_wrad50_rad3jtwc_??_wrad50_rad4jtwc_??_wrad64_rad1jtwc_??_wrad64_rad2jtwc_??_wrad64_rad3jtwc_??_wrad64_rad4latitude_mergedlongitude_merged
01842298N1108018421NIBBNOT NAMED1842-10-25 06:00:00NRNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN10.88579.815
11842298N1108018421NIBBNOT NAMED1842-10-25 12:00:00NRNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN10.81078.890
21842298N1108018421NIASNOT NAMED1842-10-25 18:00:00NRNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN10.79577.910
31842298N1108018421NIASNOT NAMED1842-10-26 00:00:00NRNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN10.79576.915
41842298N1108018421NIASNOT NAMED1842-10-26 06:00:00NRNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaN10.80575.820
\n", "

5 rows \u00d7 202 columns

\n", "
"], "text/plain": [" Serial_Num Season Num Basin Sub_basin Name ISO_time \\\n", "0 1842298N11080 1842 1 NI BB NOT NAMED 1842-10-25 06:00:00 \n", "1 1842298N11080 1842 1 NI BB NOT NAMED 1842-10-25 12:00:00 \n", "2 1842298N11080 1842 1 NI AS NOT NAMED 1842-10-25 18:00:00 \n", "3 1842298N11080 1842 1 NI AS NOT NAMED 1842-10-26 00:00:00 \n", "4 1842298N11080 1842 1 NI AS NOT NAMED 1842-10-26 06:00:00 \n", "\n", " Nature Latitude Longitude ... jtwc_??_wrad50_rad1 \\\n", "0 NR NaN NaN ... NaN \n", "1 NR NaN NaN ... NaN \n", "2 NR NaN NaN ... NaN \n", "3 NR NaN NaN ... NaN \n", "4 NR NaN NaN ... NaN \n", "\n", " jtwc_??_wrad50_rad2 jtwc_??_wrad50_rad3 jtwc_??_wrad50_rad4 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " jtwc_??_wrad64_rad1 jtwc_??_wrad64_rad2 jtwc_??_wrad64_rad3 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " jtwc_??_wrad64_rad4 latitude_merged longitude_merged \n", "0 NaN 10.885 79.815 \n", "1 NaN 10.810 78.890 \n", "2 NaN 10.795 77.910 \n", "3 NaN 10.795 76.915 \n", "4 NaN 10.805 75.820 \n", "\n", "[5 rows x 202 columns]"]}, "execution_count": 45, "metadata": {}, "output_type": "execute_result"}], "source": ["full_df.head(5)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["The results look good. Two additional columns (`latitude_merged`, `longitude_merged`) have been added. By merging related columns, the redundant sparse columns can be removed, thereby simplifying the dimension of the input dataset. \n", "\n", "Now that this prototype looks good, we will proceed by identifying the lists of remaining columns that are redundant and can be merged."]}, {"cell_type": "markdown", "metadata": {"heading_collapsed": true}, "source": ["## Merge similar columns\n", "To keep track of which columns have been accounted for, we will duplicate the `all_columns` list and remove ones that we have identified."]}, {"cell_type": "code", "execution_count": 46, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["200"]}, "execution_count": 46, "metadata": {}, "output_type": "execute_result"}], "source": ["columns_tracker = deepcopy(all_columns)\n", "len(columns_tracker)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["From the `columns_tracker` list, let us remove the redundant columns we already identified for location columns."]}, {"cell_type": "code", "execution_count": 47, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["142"]}, "execution_count": 47, "metadata": {}, "output_type": "execute_result"}], "source": ["columns_tracker = [x for x in columns_tracker if x not in lat_columns]\n", "columns_tracker = [x for x in columns_tracker if x not in lon_columns]\n", "len(columns_tracker)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["Thus, we have reduced the number of columns from `200` to `142`. We will progressively reduce this while retaining key information. \n", "\n", "### Merge wind columns\n", "Wind, pressure, grade are some of the meteorological observations this dataset contains. To start off, let us identify the wind columns:"]}, {"cell_type": "code", "execution_count": 48, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["['Wind(WMO)',\n", " 'hurdat_atl_wind',\n", " 'td9636_wind',\n", " 'reunion_wind',\n", " 'atcf_wind',\n", " 'mlc_natl_wind',\n", " 'ds824_sh_wind',\n", " 'ds824_ni_wind',\n", " 'bom_wind',\n", " 'ds824_au_wind',\n", " 'jtwc_sh_wind',\n", " 'jtwc_wp_wind',\n", " 'td9635_wind',\n", " 'ds824_wp_wind',\n", " 'jtwc_io_wind',\n", " 'cma_wind',\n", " 'hurdat_epa_wind',\n", " 'jtwc_ep_wind',\n", " 'ds824_ep_wind',\n", " 'jtwc_cp_wind',\n", " 'tokyo_wind',\n", " 'neumann_wind',\n", " 'hko_wind',\n", " 'cphc_wind',\n", " 'wellington_wind',\n", " 'newdelhi_wind',\n", " 'nadi_wind']"]}, "execution_count": 48, "metadata": {}, "output_type": "execute_result"}], "source": ["# pick all columns that have 'wind' in name\n", "wind_columns = [x for x in columns_tracker if 'wind' in x.lower()]\n", "\n", "# based on metadata doc, we decide to eliminate percentile and wind distance columns\n", "columns_to_eliminate = [x for x in wind_columns if 'radii' in x or 'percentile' in x.lower()]\n", "\n", "# trim wind_columns by removing the ones we need to eliminate\n", "wind_columns = [x for x in wind_columns if x not in columns_to_eliminate]\n", "wind_columns"]}, {"cell_type": "code", "execution_count": 49, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 56.7 ms, sys: 4.92 ms, total: 61.6 ms\n", "Wall time: 57.6 ms\n"]}], "source": ["full_df['wind_merged'] = full_df.apply(pick_median_value, axis=1,\n", " col_list = wind_columns)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["#### Merge pressure columns\n", "We proceed to identify all `pressure` columns. But before that, we update the `columns_tracker` list by removing those we identified for wind:"]}, {"cell_type": "code", "execution_count": 50, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["106"]}, "execution_count": 50, "metadata": {}, "output_type": "execute_result"}], "source": ["columns_tracker = [x for x in columns_tracker if x not in wind_columns]\n", "columns_tracker = [x for x in columns_tracker if x not in columns_to_eliminate]\n", "len(columns_tracker)"]}, {"cell_type": "code", "execution_count": 51, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["['Pres(WMO)',\n", " 'hurdat_atl_pres',\n", " 'td9636_pres',\n", " 'reunion_pres',\n", " 'atcf_pres',\n", " 'mlc_natl_pres',\n", " 'ds824_sh_pres',\n", " 'ds824_ni_pres',\n", " 'bom_pres',\n", " 'ds824_au_pres',\n", " 'jtwc_sh_pres',\n", " 'jtwc_wp_pres',\n", " 'td9635_pres',\n", " 'ds824_wp_pres',\n", " 'jtwc_io_pres',\n", " 'cma_pres',\n", " 'hurdat_epa_pres',\n", " 'jtwc_ep_pres',\n", " 'ds824_ep_pres',\n", " 'jtwc_cp_pres',\n", " 'tokyo_pres',\n", " 'neumann_pres',\n", " 'hko_pres',\n", " 'cphc_pres',\n", " 'wellington_pres',\n", " 'newdelhi_pres',\n", " 'nadi_pres']"]}, "execution_count": 51, "metadata": {}, "output_type": "execute_result"}], "source": ["# pick all columns that have 'pres' in name\n", "pressure_columns = [x for x in columns_tracker if 'pres' in x.lower()]\n", "\n", "# from metadata, we eliminate percentile and pres distance columns\n", "if columns_to_eliminate:\n", " columns_to_eliminate.extend([x for x in pressure_columns if 'radii' in x or 'percentile' in x.lower()])\n", "else:\n", " columns_to_eliminate = [x for x in pressure_columns if 'radii' in x or 'percentile' in x.lower()]\n", "\n", "# trim wind_columns by removing the ones we need to eliminate\n", "pressure_columns = [x for x in pressure_columns if x not in columns_to_eliminate]\n", "pressure_columns"]}, {"cell_type": "code", "execution_count": 52, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 122 ms, sys: 5.33 ms, total: 127 ms\n", "Wall time: 123 ms\n"]}], "source": ["full_df['pressure_merged'] = full_df.apply(pick_median_value, axis=1,\n", " col_list = pressure_columns)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["#### Merge grade columns"]}, {"cell_type": "code", "execution_count": 53, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["78"]}, "execution_count": 53, "metadata": {}, "output_type": "execute_result"}], "source": ["columns_tracker = [x for x in columns_tracker if x not in pressure_columns]\n", "columns_tracker = [x for x in columns_tracker if x not in columns_to_eliminate]\n", "len(columns_tracker)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["Notice the length of `columns_tracker` is reducing progressively as we identify redundant columns."]}, {"cell_type": "code", "execution_count": 54, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["['hurdat_atl_grade',\n", " 'td9636_grade',\n", " 'reunion_grade',\n", " 'atcf_grade',\n", " 'mlc_natl_grade',\n", " 'ds824_sh_grade',\n", " 'ds824_ni_grade',\n", " 'bom_grade',\n", " 'ds824_au_grade',\n", " 'jtwc_sh_grade',\n", " 'jtwc_wp_grade',\n", " 'td9635_grade',\n", " 'ds824_wp_grade',\n", " 'jtwc_io_grade',\n", " 'cma_grade',\n", " 'hurdat_epa_grade',\n", " 'jtwc_ep_grade',\n", " 'ds824_ep_grade',\n", " 'jtwc_cp_grade',\n", " 'tokyo_grade',\n", " 'neumann_grade',\n", " 'hko_grade',\n", " 'cphc_grade',\n", " 'wellington_grade',\n", " 'newdelhi_grade',\n", " 'nadi_grade']"]}, "execution_count": 54, "metadata": {}, "output_type": "execute_result"}], "source": ["# pick all columns that have 'grade' in name\n", "grade_columns = [x for x in columns_tracker if 'grade' in x.lower()]\n", "grade_columns"]}, {"cell_type": "code", "execution_count": 55, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 54.9 ms, sys: 5.59 ms, total: 60.5 ms\n", "Wall time: 56.4 ms\n"]}], "source": ["full_df['grade_merged'] = full_df.apply(pick_median_value, axis=1,\n", " col_list = grade_columns)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["#### Merge eye diameter columns"]}, {"cell_type": "code", "execution_count": 56, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["52"]}, "execution_count": 56, "metadata": {}, "output_type": "execute_result"}], "source": ["columns_tracker = [x for x in columns_tracker if x not in grade_columns]\n", "len(columns_tracker)"]}, {"cell_type": "code", "execution_count": 57, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["['bom_mn_eye_diam', 'atcf_eye', 'jtwc_??_eye']"]}, "execution_count": 57, "metadata": {}, "output_type": "execute_result"}], "source": ["# pick all columns that have 'eye' in name\n", "eye_dia_columns = [x for x in columns_tracker if 'eye' in x.lower()]\n", "eye_dia_columns"]}, {"cell_type": "code", "execution_count": 58, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 53.6 ms, sys: 4.74 ms, total: 58.4 ms\n", "Wall time: 54.8 ms\n"]}], "source": ["full_df['eye_dia_merged'] = full_df.apply(pick_median_value, axis=1,\n", " col_list = eye_dia_columns)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["### Identify remaining redundant columns"]}, {"cell_type": "code", "execution_count": 59, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["49"]}, "execution_count": 59, "metadata": {}, "output_type": "execute_result"}], "source": ["columns_tracker = [x for x in columns_tracker if x not in eye_dia_columns]\n", "len(columns_tracker)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["We are down to `49` columns, let us visualize what those look like."]}, {"cell_type": "code", "execution_count": 60, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["['Serial_Num', 'Season', 'Num', 'Basin', 'Sub_basin', 'Name', 'ISO_time', 'Nature', 'Center', 'Track_type',\n", " 'Current Basin', 'reunion_rmw', 'bom_mn_hurr_xtnt', 'bom_mn_gale_xtnt', 'bom_roci', 'atcf_rmw', 'atcf_poci',\n", " 'atcf_roci', 'atcf_wrad34_rad1', 'atcf_wrad34_rad2', 'atcf_wrad34_rad3', 'atcf_wrad34_rad4', 'atcf_wrad50_rad1',\n", " 'atcf_wrad50_rad2', 'atcf_wrad50_rad3', 'atcf_wrad50_rad4', 'atcf_wrad64_rad1', 'atcf_wrad64_rad2',\n", " 'atcf_wrad64_rad3', 'atcf_wrad64_rad4', 'tokyo_dir50', 'tokyo_short50', 'tokyo_dir30', 'tokyo_short30', 'jtwc_??_rmw',\n", " 'jtwc_??_poci', 'jtwc_??_roci', 'jtwc_??_wrad34_rad1', 'jtwc_??_wrad34_rad2', 'jtwc_??_wrad34_rad3',\n", " 'jtwc_??_wrad34_rad4', 'jtwc_??_wrad50_rad1', 'jtwc_??_wrad50_rad2', 'jtwc_??_wrad50_rad3', 'jtwc_??_wrad50_rad4',\n", " 'jtwc_??_wrad64_rad1', 'jtwc_??_wrad64_rad2', 'jtwc_??_wrad64_rad3', 'jtwc_??_wrad64_rad4']\n"]}], "source": ["pprint(columns_tracker, width=119, compact=True)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["Based on metadata shared by data provider, we choose to retain only the first `11` columns. We add the rest to the list `columns_to_eliminate`."]}, {"cell_type": "code", "execution_count": 61, "metadata": {"hidden": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["['Wind(WMO) Percentile', 'reunion_wind_radii_1_ne', 'reunion_wind_radii_1_se', 'reunion_wind_radii_1_sw',\n", " 'reunion_wind_radii_1_nw', 'reunion_wind_radii_2_ne', 'reunion_wind_radii_2_se', 'reunion_wind_radii_2_sw',\n", " 'reunion_wind_radii_2_nw', 'Pres(WMO) Percentile', 'reunion_rmw', 'bom_mn_hurr_xtnt', 'bom_mn_gale_xtnt', 'bom_roci',\n", " 'atcf_rmw', 'atcf_poci', 'atcf_roci', 'atcf_wrad34_rad1', 'atcf_wrad34_rad2', 'atcf_wrad34_rad3', 'atcf_wrad34_rad4',\n", " 'atcf_wrad50_rad1', 'atcf_wrad50_rad2', 'atcf_wrad50_rad3', 'atcf_wrad50_rad4', 'atcf_wrad64_rad1',\n", " 'atcf_wrad64_rad2', 'atcf_wrad64_rad3', 'atcf_wrad64_rad4', 'tokyo_dir50', 'tokyo_short50', 'tokyo_dir30',\n", " 'tokyo_short30', 'jtwc_??_rmw', 'jtwc_??_poci', 'jtwc_??_roci', 'jtwc_??_wrad34_rad1', 'jtwc_??_wrad34_rad2',\n", " 'jtwc_??_wrad34_rad3', 'jtwc_??_wrad34_rad4', 'jtwc_??_wrad50_rad1', 'jtwc_??_wrad50_rad2', 'jtwc_??_wrad50_rad3',\n", " 'jtwc_??_wrad50_rad4', 'jtwc_??_wrad64_rad1', 'jtwc_??_wrad64_rad2', 'jtwc_??_wrad64_rad3', 'jtwc_??_wrad64_rad4']\n"]}], "source": ["columns_to_eliminate.extend(columns_tracker[11:])\n", "pprint(columns_to_eliminate, width=119, compact=True)"]}, {"cell_type": "markdown", "metadata": {"hidden": true}, "source": ["### Drop all redundant columns\n", "So far, we have merged similar columns together and collected the lists of redundant columns to drop. Below we compile them into a single list."]}, {"cell_type": "code", "execution_count": 62, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["206"]}, "execution_count": 62, "metadata": {}, "output_type": "execute_result"}], "source": ["len(full_df.columns)"]}, {"cell_type": "code", "execution_count": 63, "metadata": {"hidden": true}, "outputs": [{"data": {"text/plain": ["189"]}, "execution_count": 63, "metadata": {}, "output_type": "execute_result"}], "source": ["columns_to_drop = lat_columns + lon_columns + wind_columns + pressure_columns + \\\n", " grade_columns + eye_dia_columns+columns_to_eliminate\n", "len(columns_to_drop)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Perform delayed computation\n", "In Dask, all computations are delayed and queued. The `apply()` functions called earlier are not executed yet, however respective columns have been created as you can see from the DataFrame display above. In the cells below, we will call `save()` to make Dask compute on this larger than memory dataset.\n", "\n", "Calling `visualize()` on the delayed compute operation or the `DataFrame` object will plot the dask task queue as shown below. The graphic below provides a glimpse on how Dask distributes its tasks and how it reads this 'larger than memory dataset' in chunks and operates on them."]}, {"cell_type": "markdown", "metadata": {}, "source": [" Drawing dask graphs requires the `graphviz` python library and the `graphviz` system library to be installed."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["!conda install --yes -c anaconda graphviz \n", "!conda install --yes -c conda-forge python-graphviz"]}, {"cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [{"data": {"image/png": 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FXbp0UUxMjKKjo01HBJBH9BUAt6LfAPjTmTNnNHfuXE2cOFELFixQVlaWbr75ZrVp00adOnVSVFSUI787RmZmphISEjRnzhwtWrRI69atU4kSJXI6pn379goLCzMdEwAkce8BTpSdna34+HhNmzZNU6dO1aFDh1S1alW1bdtWbdq0Ufv27VWiRAnTMfNl586dOR+3CxcuVEZGhpo0aaKYmBj17t1b5cuXNx0RgIvQpwByg/0KcCb2DsCReDiZkpKiSZMmadKkSVq3bp3KlSun7t27KyYmRs2aNVOhQoVMR/Sp7OxsrVq1StOmTdP06dO1d+9e3XTTTbr//vv1yCOPqEKFCqYjArgE+oq+AtyKfqPfAH/KzMzUt99+q48//lixsbGyLEvt2rVTz5491alTJ5UuXdp0RJ/bvXu3ZsyYoalTp2rVqlUqXbq0unfvrscee0y333676XgAPIh7j3sPzpSUlKRx48blPO6pX7++evbsqe7du6tmzZqm4/ncqVOnFBsbq6lTp2ru3LlKT09X69at9fDDD6tbt258NyQA+Uaf0qfAlbBfsV/Bmdg72DvgeN58OGlZlpYtW6axY8fqq6++Unh4uHr27KnevXurRYsWCgkJMR0xICzLUnx8vKZOnarPP/9cx48f11133aV+/fqpQ4cOnvn7ANgZffUb+gpwH/rtN/Qb4D/JyckaP368Pv74Yx06dEitW7fWfffdp65du6pUqVKm4wXM7t27NW3aNE2aNEmJiYmKjIxUv3799MADD3jq7wOAwOPe+w33Hpzm9OnTmjJlisaOHauEhARVr15dDz74oHr27OnKxz2Xcvr0aX3zzTeaPHmyvv32W5UsWVIPPfSQ+vbtq5tuusl0PAAOQJ/+hj4FLo/96jfsV3AS9o7fsHfAJbz1cPLcuXOaNGmS3nrrLW3ZskW33367+vXrp169eqlYsWKm4xmVlpamGTNmaNy4cVq6dKkqVaqkQYMGqW/fvipevLjpeIDn0FeXRl8Bzka/XRr9BvjGsmXL9NZbb2nOnDkqX768+vTpo8cee0w33HCD6WjGrVq1SmPHjtWUKVOUlZWlhx9+WH//+99Vo0YN09EAuAj33qVx78HO9u7dq3feeUdjx47VuXPndM8996h///5q0aKFgoKCTMcz6uDBg5owYYLGjx+vnTt3qmXLlnr66ad15513ev7vDYAL0aeXRp8C/4f96tLYr2BX7B2Xxt4BB/PGw8kjR45ozJgxGjNmjI4fP677779fAwcOVP369U1Hs6Wff/5Z//vf/zR+/HiFhISof//+GjhwoCpWrGg6GuB69FXe0FeAc9BveUO/AXmTmZmp6dOna+TIkVqzZo2aNm2qwYMHq0uXLq77cSi+cPLkSU2aNElvv/22du7cqU6dOunpp5/WHXfcYToaAAfj3ssb7j3Yxbp16zRy5EhNmzZN5cqV04ABA/TYY4+pbNmypqPZTnZ2thYtWqRRo0YpNjZWtWrV0uDBg/XAAw+ocOHCpuMBMIw+zT36FF7FfpU37FewC/aOvGHvgMMkyHKxlJQUa+jQoVaJEiWskiVLWgMHDrT27dtnOpZjHD9+3Bo1apR1/fXXW6GhoVb//v35+wf4CX1VMPQVYF/0W8HQb8DlZWVlWVOnTrVq1KhhBQcHW506dbLi4uJMx3KMrKwsa/bs2VabNm0sSVZUVJS1ePFi07EAOAz3XsFw78GUxMREKyYmxgoKCrLq169vffjhh9bZs2dNx3KMbdu2WQMHDrSKFClilStXzho+fDh//wCPok8Lhj6FF7BfFQz7FUxh7ygY9g44xEpXPpw8duyY9cILL1glSpSwIiIirDfffNM6ffq06ViOlZaWZn3wwQfW9ddfbxUuXNh66qmnrEOHDpmOBbgCfeVb9BVgH/Sbb9FvwB9lZ2dbU6dOtWrXrm2FhIRYDz/8sLVjxw7TsRxt+fLlVsuWLS1JVtu2ba2VK1eajgTA5rj3fIt7D4GSlJRkdevWzQoKCrIaNmxoffvtt6YjOdr+/futAQMGWOHh4VblypWtcePGWRkZGaZjAQgA+tS36FO4EfuV77FfIRDYO3yLvQM2566HkxkZGdaHH35olStXzrr66qutoUOHWqmpqaZjuUZaWpr14YcfWhUqVLCKFi1qDR06lK/6AvKJvvIv+gowh37zL/oNsKxVq1ZZTZo0sYKDg62YmBhry5YtpiO5yu8H6E6dOjHoA7gA955/ce/BX44ePWoNHDjQCgkJserWrWtNnTrVys7ONh3LNfbu3WsNHDjQCg8Pt2rUqGFNnTrVdCQAfkKf+hd9Crdgv/Iv9iv4A3uHf7F3wKbc83By/vz5Vp06daywsDDr2WefpcD86PTp09Yrr7xiXXXVVVaVKlWsadOmmY4EOAp9FTj0FRBY9Fvg0G/wot27d1u9e/e2goKCrDZt2liJiYmmI7naN998Y9WsWdMqXLiwNWTIEOvkyZOmIwGwAe69wOHeg6+cO3fOev31160SJUpY119/vfX555/zwMePduzYYXXv3t2SZHXs2NHavHmz6UgAfIQ+DSz6FE7FfhVY7FfwFfaOwGHvgM2sDJbD7d+/X127dlX79u114403auPGjRoxYoRKlixpOpprFS1aVM8//7y2bt2qZs2aqWfPnmrVqpW2b99uOhpga/RV4NFXQGDQb4FHv8FLMjIy9N///le1atXSmjVrNHPmTC1cuFD16tUzHc3VOnXqpKSkJL322msaM2aMatasqa+++sp0LACGcO8FHvcefGHRokWqU6eOXnrpJQ0ePFhbtmzRfffdp6CgINPRXOuGG27Q9OnTtXTpUh04cECRkZH6xz/+oTNnzpiOBqAA6NPAo0/hNOxXZrBfoaDYOwKPvQN249iHk5Zl6YMPPtBNN92kjRs3auHChZo1a5ZuvPFG09E847rrrtPEiRO1cuVK/frrr4qMjNTw4cOVmZlpOhpgK/SVefQV4B/0m3n0G9xuzZo1atiwoV599VW98MIL2rhxo+6++27TsTwjNDRUgwcP1rZt29S+fXvFxMTonnvu0YEDB0xHAxAg3Hvmce8hP3799Vf16dNH7dq1U/369bVlyxYNHTpURYsWNR3NM1q0aKG1a9dq9OjR+uijjxQZGanFixebjgUgj+hT8+hTOAH7lVnsV8gP9g7z2DtgF458OLl9+3Y1b95cAwYM0BNPPKHExES1adPGdCzPuv322/XDDz/ohRde0EsvvaSGDRtq3bp1pmMBtkBf2Qt9BfgO/WYv9Bvc5uzZs3rmmWfUuHFjlS5dWomJifrnP/+psLAw09E8KSIiQh9//LEWLlyopKQk3XTTTRo7dqwsyzIdDYAfce/ZC/cecmv69Om66aabNH/+fE2fPl1fffWVrr/+etOxPCkkJERPPPGENm3apHr16qlt27Z69NFHdfz4cdPRAOQCfWof9Cnsiv3KXtivkFvsHfbC3gHTHPdwcty4cbrlllt06tQprV69WsOHD1eRIkVMx/K80NBQ/etf/1JiYqJKliypJk2aaMSIEcrOzjYdDTCGvrIn+gooOPrNnug3uEViYqIaNmyocePG6YMPPtCSJUv4Sl+baN26tRITE9W/f3898cQTuvvuu3XkyBHTsQD4AfeePXHv4XJOnjypRx55RD179lTnzp21adMmdevWzXQsSKpQoYJmzpypadOmad68eapfv76WL19uOhaAS6BP7Ys+hZ2wX9kX+xUuh73Dntg7YJJjHk6mpqbq3nvvVf/+/fXYY48pISFBt9xyi+lY+JPq1atr6dKlGjFihP7zn/+oTZs22rdvn+lYQEDRV85AXwF5R785A/0Gp7IsSx999JGaNGmi4sWLa+3aterbt6+CgoJMR8PvFC1aVCNGjNCKFSu0adMm1a1bV3PnzjUdC4CPcO85A/ce/mz16tVq0KCBvv32W82aNUtjx45VqVKlTMfCn3Tv3l2JiYmqX7++WrRooUGDBik9Pd10LAC/Q586A30Kk9ivnIH9Cn/G3uEM7B0wwREPJ1esWKE6deooLi5Oixcv1ujRo/kW1zYWFBSkQYMGaeXKlTp48KDq16+vOXPmmI4FBAR95Sz0FZB79Juz0G9wmpSUFN15553629/+piFDhmjFihWqVq2a6Vi4jMaNG2vt2rVq06aNOnfurGeeeUaZmZmmYwEoAO49Z+Heg/TbJ+6HDRumpk2bqnr16kpMTFSXLl1Mx8JlRERE6Ouvv9Z7772ncePG6Y477tDevXtNxwI8jz51HvoUJrBfOQ/7FST2Dqdh70Cg2f7h5LvvvqtWrVrptttu04YNG9SyZUvTkZBLDRo00Nq1a9WlSxfdfffdGjp0KN9KF65GXzkXfQVcHv3mXPQbnGDdunW67bbbtGnTJq1YsUL//ve/FRISYjoWcqFkyZL6/PPP9cknn+j9999X27ZtdfjwYdOxAOQD955zce9514kTJ9StWzcNHTpUI0aM0Ny5c3XNNdeYjoVcevzxx7VmzRqdPHlSt956q5YsWWI6EuBZ9Kmz0acIFPYr52K/8jb2Dudi70Cg2Pbh5Llz5/Too49q0KBBGjx4sGbOnKnSpUubjoU8Klq0qCZMmKAJEybojTfeUKdOnfTrr7+ajgX4FH3lDvQVcCH6zR3oN9jZZ599pmbNmqly5cpavXq1GjVqZDoS8uGhhx7SypUrtXfvXt16661auXKl6UgAcol7zx2497xn69atatKkieLi4hQbG6u///3v/HhIB6pdu7ZWrVqlli1bql27dnrxxRdlWZbpWICn0KfuQJ/C39iv3IH9ylvYO9yBvQOBEGTZ8HI8ePCgOnXqpOTkZH3xxRdq37696UjwgYSEBMXExKhIkSKaO3euqlevbjoSUGD0lTvRVwD95lb0G+wiOztbzzzzjEaNGqV//OMfeu211/gqfRc4duyYHnjgAS1evFjjx4/X/fffbzoSgMvg3nMn7j33mzdvnnr16qV69epp2rRpqlChgulIKCDLsjRy5Ej961//Uq9evTR+/HiFh4ebjgW4Hn3qPvQpfI39yp3Yr9yPvcOd2DvgJwm2+46TP/30kxo3bqzTp09r9erVlJiLNG7cWD/88INKlSqlqKgoxcXFmY4EFAh95V70FbyOfnMv+g12cObMGfXo0UNjxozR559/ruHDhzM6u0Tp0qX1zTffaMCAAXrwwQf16quvmo4E4BK499yLe8/dPvzwQ3Xp0kXdunXT0qVLeeTjEkFBQXrmmWc0d+5cffPNN2rfvr2OHTtmOhbgavSpO9Gn8CX2K/div3I39g73Yu+Av9jq4eTixYsVHR2tihUravny5apWrZrpSPCxa665Rt9//71atWql1q1b64svvjAdCcgX+sr96Ct4Ff3mfvQbTEpJSVG7du20bNkyzZ8/X/fee6/pSPCx4OBgvfHGG/roo4/08ssvq0+fPsrIyDAdC8DvcO+5H/ee+1iWpRdffFFPPPGE/v3vf2vChAkKCwszHQs+1q5dO8XFxSk5OVmNGjXS9u3bTUcCXIc+9Qb6FAXFfuV+7FfuxN7hfuwd8IeQF1988UXTISRp9uzZuueee3TnnXdq1qxZKlmypOlI8JNChQqpR48eysrK0qBBg1SqVCk1btzYdCwg1+gr76Cv4DX0m3fQbzBhz549at68uU6cOKHFixfrtttuMx0JftSgQQPdfvvtevnllxUXF6du3bopNDTUdCzA87j3vIN7zz2ysrLUt29fjRkzRhMmTNCgQYMUFBRkOhb8pFy5curVq5dmzJih0aNHq127dipfvrzpWIAr0KfeQp8iv9ivvIX9yj3YO7yDvQM+ts8WDye/+OIL3Xvvverbt68+/vhj/mXkAUFBQWrRooUKFy6sZ555RsHBwWrevLnpWMAV0VfeQ1/BK+g376HfEEjJyclq1aqVwsPDtXTpUlWtWtV0JARAtWrV1K5dO40YMULz589Xjx49FB4ebjoW4Fnce97Dved8GRkZuu+++zRz5kzNmjVL3bp1Mx0JAVC8eHHdf//9WrJkiYYNG6YWLVqoYsWKpmMBjkafehN9irxiv/Im9ivnY+/wHvYO+JD5h5MfffSRHnvsMT3zzDN6++23+eouj4mOjlb58uX13HPP6ezZs2rTpo3pSMAl0VfeRl/Bzeg3b6Pf4G9btmxRixYtFBERocWLF/MdHjymQoUK6tSpk0aPHq0ZM2aoe/fuKlq0qOlYgOdw73kb954zpaWlqVevXpo/f75mz56ttm3bmo6EAAoLC1OPHj0UFxenV199VY0bN+bxBpBP9Km30afILfYrb2O/ci72Dm9j74APmH04+f777+uvf/2rXnnlFb388sumYsCwhg0bqmLFihoyZIjS0tLUunVr05GAC9BXkOgruBP9Bol+g/9s2bJFzZs3V/Xq1TV//nyVLl3adCQYEBERobvuukvvv/++Zs2apd69e/OV+0AAce9B4t5zmvT0dHXt2lVxcXFasGCBmjVrZjoSDAgNDVVMTIzWr1+vV199Vc2bN1elSpVMxwIchT6FRJ/iytivILFfORF7ByT2DhSYuYeTn376qf7yl7/o1Vdf1b///W8TEWAjDRo0UKVKlfTss88qPDxc0dHRpiMBOegr/B59BTeh3/B79Bt8befOnWrZsqWqVaum2NhYFS9e3HQkGFS2bFl16dJF77zzjmJjY9WrVy9+bA4QANx7+D3uPWfIzMxUr169tHz5ci1cuFCNGjUyHQkGFSpUSN27d9fatWv12muvqW3btqpQoYLpWIAj0Kf4PfoUl8J+hd9jv3IO9g78HnsHCsDMw8kZM2booYce0j//+U8NHTo00H952NTNN9+ssmXL6umnn1bx4sUVFRVlOhJAX+Gi6Cu4Af2Gi6Hf4Cv79u1Ty5YtdfXVV2v+/PkqUaKE6UiwgauvvlqdOnXSyJEjtWTJEvXs2VOFChUyHQtwLe49XAz3nr1lZ2fr4Ycf1pw5czR37lw1adLEdCTYQHBwsLp166b4+Hi9/vrruuuuu1SuXDnTsQBbo09xMfQp/oz9ChfDfmV/7B24GPYO5FPgH07Onz9f3bt315NPPqkRI0YE8i8NB2jUqJGKFCmi5557TtWqVVNkZKTpSPAw+gqXQ1/Byeg3XA79hoJKSUlRixYtVLhwYS1ZskRXX3216UiwkbJly6pVq1YaPny41q9fr5iYGAUFBZmOBbgO9x4uh3vPvv76179q8uTJ+uabb9SyZUvTcWAjISEhuueee7RgwQKNGjVK3bt3V6lSpUzHAmyLPsWl0Kc4j/0Kl8N+ZV/sHbgc9g7kw74gy7KsQP3VNmzYoGbNmumee+7RJ598wr9ccEnPPfecRo0apdjYWH5TCyPoK+QWfQWnod+QW/Qb8uPcuXNq27at9u3bp5UrV+qaa64xHQk2tWLFCrVt21aPP/643n77bdNxAFfh3kNuce/Zy4gRIzRkyBBNnz5dXbt2NR0HNnXixAndcccdysjIUFxcHI99gIugT5Eb9Km3sV8ht9iv7IW9A7nF3oE8SAjYw8kDBw6ocePGqlq1qhYsWKDw8PBA/GXhUJZl6cEHH9Q333yjFStWqF69eqYjwUPoK+QFfQUnod+QF/Qb8sqyLD300EOaPXs2/8wgV6ZNm6bevXvr7bff1sCBA03HAVyBew95wb1nH+f/nfjWW29p0KBBpuPA5uh64NLoU+QFfepN7FfIK/Yre6CzkRfsHciDwDycPHXqlKKjo/mqHeTJuXPn1KZNG+3fv18JCQkqX7686UjwAPoK+UFfwQnoN+QH/Ya8GDJkiEaOHKl58+apVatWpuPAIYYPH67nn39eM2bMUJcuXUzHARyNew/5wb1nXnx8vFq3bq3+/ftr9OjRpuPAIX7/3XY+/fRT03EAW6BPkR/0qfewXyE/2K/MYu9AfrB3IJf8/3DSsiz16tVL3333nVavXq0qVar48y8Hlzl69KgaN26sihUratGiRSpUqJDpSHAx+goFQV/Bzug3FAT9htyYOnWqevfurfHjx6tPnz6m48Bh+vXrpylTpmj16tWqVauW6TiAI3HvoSC498w5ePCgbr31Vt16662aNWuWQkJCTEeCg8ybN0+dO3fWm2++qaeeesp0HMAo+hQFQZ96B/sVCoL9ygz2DhQEewdyISHY33+FN998UzNmzNBnn31GiSHPypYtq5kzZ2rNmjV67rnnTMeBy9FXKAj6CnZGv6Eg6DdcydatW9WvXz89+eSTjM7IlzFjxigyMlJdu3bViRMnTMcBHIl7DwXBvWdGRkaGevXqpWLFiumzzz7jkQ/yrGPHjnrllVf07LPPatmyZabjAMbQpygo+tQb2K9QUOxXZrB3oCDYO5Abfv2Ok0uXLlW7du00bNgwPfPMM/76y8ADJk+erPvuu08TJ07Ugw8+aDoOXIi+gq/QV7Ab+g2+Qr/hYk6ePKnbb79dpUqV0nfffaewsDDTkeBQ579DzO23364ZM2YoKCjIdCTAMbj34Cvce4E1YMAAffLJJ1q5cqXq1q1rOg4cyrIs9ezZU8uXL9fatWt13XXXmY4EBBx9Cl+gT92N/Qq+wn4VWOwd8BX2DlyG/35U96FDh1S/fn01b95cU6ZM4V8aKLCBAwdqwoQJWrt2rWrUqGE6DlyEvoKv0VewC/oNvka/4c9iYmK0YsUKrV27VhUqVDAdBw53fgx98803NWjQINNxAEfg3oOvce8FxvkfEzl58mT16tXLdBw43IkTJ9SoUSOVK1dOS5cu5bvtwVPoU/gSfepe7FfwJfarwGDvgK+xd+AS/PNw0rIs3XXXXdq6dat+/PFHFS9e3Nd/CXhQenq6oqKiFBQUpPj4eIWGhpqOBBegr+AP9BXsgH6DP9Bv+L0JEyaob9++WrBggVq3bm06Dlzi1Vdf1WuvvaZVq1YpMjLSdBzA1rj34A/ce/63d+9e1a9fX/fee6/ee+8903HgEomJiWrUqJGef/55Pf/886bjAAFBn8If6FP3Yb+CP7Bf+Rd7B/yBvQOXkBDsjz/ru+++q4ULF+qzzz6jxOAzYWFh+vLLL7V161YNHTrUdBy4BH0Ff6CvYAf0G/yBfsN5O3fu1KBBg/T0008zOsOnhgwZosaNG+vee+/V2bNnTccBbI17D/7Avedf2dnZeuSRR3TNNdfojTfeMB0HLhIZGalhw4bppZdeUkJCguk4gN/Rp/AX+tRd2K/gL+xX/sXeAX9g78Cl+Pw7Tv70009q2LCh/vnPf/IPG/xi7Nixevzxx7VkyRI1b97cdBw4GH0Ff6OvYAr9Bn+j37wtMzNT0dHRSk9PV0JCgsLCwkxHgsskJyfr5ptv1kMPPaR33nnHdBzAlrj34G/ce/4xfPhwDR06VAkJCbrllltMx4HLWJalDh06aOfOnVq/fr2KFStmOhLgN/Qp/Ik+dQf2K/gb+5V/sHfA39g78Ce+/VHdWVlZaty4sUJDQ/X999+rUKFCvvpTA3/QtWtXJSYmKikpSVdddZXpOHAg+gqBQl8h0Og3BAr95l3Dhg3TK6+8orVr16p27dqm48ClPv/8cz344INauqY9FKEAACAASURBVHQpAxbwJ9x7CBTuPd/auHGjGjRooFdffVXPPvus6ThwqYMHD6pevXrq3bu3/ve//5mOA/gFfYpAoE+dj/0KgcB+5VvsHQgU9g78jm9/VPfbb7+tpKQkjRs3jhKDX33wwQdKTU3lqwyQb/QVAoW+QqDRbwgU+s2btm3bpldeeUVDhw5ldIZf3X///erSpYv69evHjzwC/oR7D4HCvec72dnZ+stf/qL69etr8ODBpuPAxa699lq99dZbev/997VixQrTcQCfo08RKPSps7FfIVDYr3yLvQOBwt6B3/PZd5xMTk5W3bp19dxzz+k///mPL/6UwGWd/xa6K1asUJMmTUzHgYPQVwg0+gqBQr8h0Og3b7EsS23atNHRo0e1Zs0ahYaGmo4Elztw4IBuuukmPfnkk3rttddMxwFsgXsPgca95xvvvPOOnn76aa1evZofKYuA6NChg5KTk/Xjjz+qcOHCpuMAPkOfItDoU+dhv0KgsV/5BnsHAo29A/+f735Ud9u2bXX48GGtWbNGYWFhvvhTApdlWZZatWqlY8eOac2aNXzVAXKNvkKg0VcIFPoNgUa/ectHH32kJ598UgkJCbr11ltNx4FHvPfee/r73/+uNWvWKDIy0nQcwDjuPQQa917B7dmzR3Xr1tWgQYP0yiuvmI4Djzj/iednn32W76IC16BPYQJ96jzsVzCB/arg2DsQaOwd+P9883Dyq6++UkxMjOLi4niJi4Datm2b6tWrp5EjR+pvf/ub6ThwAPoKptBX8Df6DabQb95w7NgxVa9eXQ8//LBGjhxpOg48JDs7W1FRUSpcuLC+++4703EAo7j3YAr3XsH07t1b69atU1JSksLDw03HgYe8+eabeuGFF7Rp0yZVqVLFdBygwOhTmEKfOgf7FUxhvyoY9g6Ywt4B+eLhZFpamurWrasmTZpo4sSJvgoG5No//vEPjRs3Ttu3b1eZMmVMx4GN0Vcwjb6Cv9BvMI1+c79Bgwbpyy+/1LZt21SyZEnTceAxa9euVaNGjTRlyhT16NHDdBzACO49mMa9lz/x8fGKjo7W119/rc6dO5uOA4/JyMhQvXr1FBkZqalTp5qOAxQIfQqT6FPnYL+CSexX+cPeAdPYOzyv4A8n//vf/+q1117T5s2bValSJV8FA3Lt5MmTqlmzpnr06KF33nnHdBzYGH0F0+gr+Av9BtPoN3fbsmWLIiMj9d5776lfv36m48CjHn74YS1dulRbtmxR0aJFTccBAo57D6Zx7+Vddna2GjdurOLFi2vx4sWm48Cj5syZo86dO+u7775T8+bNTccB8oU+hR3Qp/bHfgU7YL/KO/YOmMbe4XkFezh56NAh1ahRQ88++6z+85//+DIYkCfjx4/X448/rsTERNWuXdt0HNgQfQW7oK/ga/Qb7IJ+c69OnTpp//79WrNmjUJCQkzHgUcdOHBANWvW1JAhQ/Svf/3LdBwgoLj3YBfce3nzySefqF+/flq/fr3q1q1rOg48rEOHDvrll1+0Zs0aBQcHm44D5Bl9CrugT+2N/Qp2wH6VN+wdsAv2Dk8r2MPJQYMG6auvvtL27dtVpEgRXwYD8iQ7O1u33HKLatSooWnTppmOAxuir2AX9BV8jX6DXdBv7hQXF6fo6GgtXLhQbdq0MR0HHvfSSy9p1KhR2rVrl0qVKmU6DhAw3HuwC+693EtPT1fNmjXVrl07ffjhh6bjwON++ukn1a9fX5MnT1bPnj1NxwHyhD6FndCn9sV+BTthv8o99g7YBXuHp+X/4eSBAwd044036s0339Rf//pXXwcD8mzmzJnq3r271q1bp5tvvtl0HNgIfQW7oa/gK/Qb7IZ+c59WrVopIyNDy5cvNx0F0IkTJ1S1alU9+eSTevnll03HAQKCew92w72XO2PGjNHgwYO1fft2XX/99abjALrvvvu0du1abdq0ie/CBUehT2E39Kk9sV/BTtivcoe9A3bD3uFZ+X84+fjjj2vevHnatm2bwsPDfR0MyDPLsnT77berQoUKmjVrluk4sBH6CnZDX8FX6DfYDf3mLkuWLFHr1q313XffqXnz5qbjAJKkYcOG6b///a927typiIgI03EAv+Peg91w713ZuXPnVL16dXXr1k2jR482HQeQJG3fvl033XSTPvnkE91///2m4wC5Qp/CjuhT+2G/gh2xX10Zewfshr3Ds/L3cDI5OVk1a9bUe++9p759+/ojGJAvc+fOVefOnbV69WrddtttpuPABugr2BV9hYKi32BX9Jt7REdHq2jRolqwYIHpKECOU6dOqVq1aurTp4+GDx9uOg7gV9x7sCvuvcsbPXq0hgwZop9//lnXXnut6ThAjj59+iguLk6bN2/mu6TBEehT2BV9ai/sV7Aj9qvLY++AXbF3eFL+Hk4+9dRTmjVrlrZv367Q0FB/BAPyrWHDhqpataqmTp1qOgpsgL6CndFXKAj6DXZGvzlfXFycoqOjtXz5ckVHR5uOA/zB66+/rmHDhmnPnj0qUaKE6TiA33Dvwc649y4uMzNT1apV0z333KNRo0aZjgP8wc8//6xatWpp8uTJiomJMR0HuCz6FHZGn9oH+xXsjP3q0tg7YGfsHZ6TEJzX/43U1FSNHz9egwYNosRgS08//bRmzJihHTt2mI4Cw+gr2B19hfyi32B39JvzjRw5Ug0bNmR0hi098cQTkqSxY8caTgL4D/ce7I577+KmTp2q/fv3a+DAgaajABe48cYb1bVrV73xxhumowBXRJ/CzuhT+2C/gp2xX10cewfsjr3De/L8cPLDDz9UcHCwHn30UX/kAQosJiZGlStX1jvvvGM6Cgyjr2B39BXyi36D3dFvzrZr1y7Nnj1bzz33nOkowEWVKFFCjz76qEaNGqWMjAzTcQC/4N6D3XHvXdzo0aPVo0cP3XDDDaajABf13HPP6YcfftDy5ctNRwEuiz6F3dGn5rFfwe7Yry6OvQN2x97hPXl6OJmRkaF3331X/fv3V8mSJf2VCSiQkJAQDRgwQB9//LGOHTtmOg4Moa/gBPQV8oN+gxPQb8721ltvqVKlSuratavpKMAlDRo0SIcOHdK0adNMRwF8jnsPTsC9d6Hvv/9eq1ev1uDBg01HAS6pYcOGatq0qUaOHGk6CnBJ9CmcgD41j/0KTsB+9UfsHXAC9g7vydPDydmzZ+vQoUMaMGCAv/IAPvHYY48pKChIEydONB0FhtBXcAr6CnlFv8Ep6DdnOn36tCZOnKgBAwYoJCTEdBzgkipXrqx77rlH77//vukogM9x78EpuPf+aMyYMWrSpIkaNWpkOgpwWU899ZTmzJmjvXv3mo4CXBR9CqegT81hv4JTsF/9EXsHnIK9w1vy9HDyo48+UocOHVSpUiV/5QF8onjx4urdu7fGjRtnOgoMoa/gFPQV8op+g1PQb8705ZdfKi0tTQ888IDpKMAV9evXTytWrNCmTZtMRwF8insPTsG9939SUlI0a9Ys9e/f33QU4IruvvtuRUREaMKECaajABegT+Ek9Kk57FdwEvar/8PeAadg7/CWXD+c3LVrlxYtWqR+/fr5Mw/gM3379tVPP/2khIQE01EQYPQVnIa+Qm7Rb3Aa+s15xo4dq+7duysiIsJ0FOCK2rRpo2rVqmn8+PGmowA+w70Hp+He+82ECRNUuHBh9ezZ03QU4IpCQ0P14IMPaty4ccrKyjIdB/gD+hROQp+aw34FJ2G/+g17B5yGvcM7cv1wcvz48SpXrpzuvPNOf+YBfKZRo0a6+eabNXbsWNNREGD0FZyGvkJu0W9wGvrNWRITE7Vq1SrGKzhGUFCQHnnkEX366adKS0szHQfwCe49OA333m8+/vhjPfDAAypatKjpKECu9O3bV/v27dPChQtNRwH+gD6F09Cngcd+Badhv/oNewechr3DO3L1cNKyLE2aNEl9+vRRaGiovzMBPvPoo49q2rRpOnv2rOkoCBD6Ck5FX+FK6Dc4Ff3mHBMnTtSNN96o5s2bm44C5FqfPn107Ngxffvtt6ajAAXGvQen8vq9l5CQoM2bN+vRRx81HQXItRo1aig6OloTJ040HQXIQZ/CiejTwGO/ghN5fb9i74BTeX3v8IpcPZxcuXKl9uzZo3vvvdffeQCfiomJ0ZkzZzRv3jzTURAg9BWcir7CldBvcCr6zRksy9L06dPVu3dvBQUFmY4D5Np1112nZs2aaerUqaajAAXGvQen8vq9N2XKFNWsWVMNGjQwHQXIk969e2v27Nk6ffq06SiAJPoUzkWfBg77FZzK6/sVewecyut7h1fk6uHk1KlTVbNmTdWrV8/feQCfuuaaa3THHXdoypQppqMgQOgrOBV9hSuh3+BU9JszxMfHa/fu3erZs6fpKECe9erVi09SwRW49+BUXr73LMvSjBkz1KtXL9NRgDyLiYlRWloanwSELdCncDL6NHDYr+BkXt6v2DvgVF7eO7zkig8ns7Ozc75yA3Cinj17as6cOTp16pTpKPAz+gpOR1/hUug3OB39Zn+MV3CyHj16KD093bM/7gjuwL0Hp/PqvRcXF6c9e/YoJibGdBQgzyIiItS8eXPPfucj2At9CiejTwOH/QpO5tX9ir0DTufVvcNLrvhwMiEhQfv37+c3K3Cs7t27Kz09XbGxsaajwM/oKzgdfYVLod/gdPSbvfGdPeB05z9JNX36dNNRgHzj3oPTefXe++qrr1S7dm3VrVvXdBQgX3r27Km5c+fq7NmzpqPA4+hTOB196n/sV3A6r+5X7B1wOq/uHV5yxYeT3377rapWrao6deoEIg/gcxEREWrUqBHfIt8D6Cs4HX2FS6Hf4HT0m70lJSVp37596ty5s+koQL516tRJCxcuVGZmpukoQL5w78HpvHrvffvtt9xQcLTOnTvr7NmzWrZsmeko8Dj6FE5Hn/of+xXcwIv7FXsHnM6re4eXXPHhZGxsrDp27BiILIDfdOzYUfPmzZNlWaajwI/oK7gBfYWLod/gBvSbfc2bN08RERFq0KCB6ShAvnXs2FHHjh3TqlWrTEcB8oV7D27gtXtv165d2rZtGx+7cLRrr71W9erV47unwCj6FG5An/of+xXcwIv7FXsH3MBre4fXXPbh5JEjR7R+/XqKDI7XsWNHHTx4UImJiaajwE/oK7gFfYU/o9/gFvSbfcXGxqpDhw4KDr7i19UBtlWzZk1Vq1aNT1LBkbj34BZeu/fmzZun4sWLKyoqynQUoEA6duzIDQWj6FO4BX3qX+xXcAOv7VfsHXALr+0dXnPZyyI2NlahoaFq2bJloPIAfnHLLbeofPnynjlCvIi+glvQV/gz+g1uQb/Z08mTJxUfH68OHTqYjgIUWPv27fmRKXAk7j24hdfuvdjYWLVu3VphYWGmowAF0qFDB23dulU7d+40HQUeRZ/CLehT/2G/gpt4ab9i74BbeG3v8JrLPpxctmyZGjdurKuuuipQeQC/CA4OVqtWrfTdd9+ZjgI/oa/gFvQV/ox+g1vQb/YUHx+v9PR0tW7d2nQUoMDatGmj9evX6/jx46ajAHnCvQe38NK9l52dreXLl6tNmzamowAFFhUVpSJFimjZsmWmo8CD6FO4CX3qP+xXcBMv7VfsHXALL+0dXnTZh5MrVqxQdHR0oLIAftW0aVPFx8crKyvLdBT4AX0FN6Gv8Hv0G9yEfrOfuLg4Va9eXeXLlzcdBSiw6OhoWZalhIQE01GAPOHeg5t45d7buHGjUlNT+diFK4SFhalhw4aKi4szHQUeRJ/CTehT/2G/gpt4ab9i74CbeGXv8KJLPpw8evSotm3bpqioqEDmAfymadOmOnHihDZt2mQ6CnyMvoLb0Fc4j36D29Bv9hMXF6emTZuajgH4REREhG688UY+SQVH4d6D23jl3ouLi1Px4sVVt25d01EAn4iOjuaGghH0KdyGPvUP9iu4iVf2K/YOuI1X9g4vuuTDyfj4eElS48aNAxYG8KfIyEiVLFlSK1asMB0FPkZfwW3oK5xHv8Ft6Dd7yczM1OrVqxmv4CpNmzZ1/fAMd+Heg9t45d6Li4tT48aNFRISYjoK4BNNmzbV1q1bdeTIEdNR4DH0KdyGPvU99iu4kRf2K/YOuI1X9g4vuuTDyVWrVqlWrVq6+uqrA5kH8Jvg4GA1atTIE9/22mvoK7gNfYXz6De4Df1mLxs3btSpU6fUpEkT01EAn4mKitLq1auVnZ1tOgqQK9x7cBuv3HurVq3ihoKrnP/nefXq1YaTwGvoU7gNfep77FdwIy/sV+wdcBuv7B1edMmHkxs2bFCDBg0CmQXwu5tvvlmJiYmmY8DH6Cu4EX0FiX6DO9Fv9vHjjz8qPDxctWrVMh0F8Jmbb75Zp06d0o4dO0xHAXKFew9u5PZ77/Tp09qxY4duueUW01EAnyldurQqVaqkDRs2mI4CD6FP4Ub0qe+xX8GNvLBfsXfAjdy+d3jVJR9OJiYmql69eoHMAvhdvXr1tHnzZmVkZJiOAh+ir+BG9BUk+g3uRL/ZR1JSkurWratChQqZjgL4TJ06dRQcHKykpCTTUYBc4d6DG7n93ktKSlJ2drYiIyNNRwF8KjIykhsKAUWfwq3oU99iv4IbeWG/Yu+AG7l97/Cqiz6c/PXXX7V3715+swLXiYyMVFpamrZt22Y6CnyEvoJb0Veg3+BW9Jt9bNiwgY6B6xQtWlTVqlXjK3/hCNx7cCu333uJiYkqXry4qlatajoK4FP16tVz9SfvYT/0KdyKPvUt9iu4kdv3K/YOuJXb9w6vuujDyZ9++kmSeAEO16lVq5ZCQ0Nde4R4EX0Ft6KvQL/Breg3+zj/FfuA2/DdPeAU3HtwK7ffe0lJSapTp46CgoJMRwF8ql69etq6davS0tJMR4FH0KdwK/rUt9iv4FZu3q/YO+BWbt87vOqiDye3bt2q4sWLq2LFioHOA/hVeHi4qlatygtwF6Gv4Fb0Feg3uBX9Zg/Hjx/X4cOHVbt2bdNRAJ+rVasWHQNH4N6DW7n93tu2bRs3FFypdu3ayszM1M6dO01HgUfQp3Ar+tR32K/gZm7er9g74FZu3zu86qIPJ5OTk1WlSpUARwECo2rVqkpOTjYdAz5CX8HN6Ctvo9/gZvSbebt27ZIkegauVKVKlZx/xgE7496Dm7n53tu1axcfu3Cl8z8u2a0fu7Af+hRuRZ/6DvsV3MzN+xV7B9zMzXuHV/FwEp5TpUoVisxF6Cu4GX3lbfQb3Ix+My85OVlBQUGqXLmy6SiAz1WtWlWnT5/WkSNHTEcBLot7D27m1nvPsizt3bs350EE4CYlSpRQ6dKlXfsJfNgLfQo3o099h/0Kbubm/Yq9A27m1r3Dyy75cJLfrMCtqlatym9WXIS+gpvRV95Gv8HN6DfzkpOTVb58eRUtWtR0FMDn+O4ecAruPbiZW++9AwcO6Ny5c3wSEK7Fd09BoNCncDv61DfYr+Bmbt6v2DvgZm7dO7zsog8nd+/ezVduwLWqVKmiffv2KTMz03QU+AB9BTejr7yNfoOb0W/m0TFws+uvv14hISEMWLA9uhhu5tZ7b/fu3ZLExy5ci4c+CBT6FG5Hn/oGv2eCm7l5v+JjF27m1r3Dyy54OGlZlo4cOaJrrrnGRB7A76655hplZWUpJSXFdBQUEH0Ft6OvvIt+g9vRb+b98ssvdAxcKzQ0VFdffbV++eUX01GAS+Leg9u59d47fPiwJPGxC9cqX748NxQCgj6F29GnvsF+BTdz637F3gG3c+ve4WUXPJw8ceKE0tPTVaZMGRN5AL87/882ReZ89BXcjr7yLvoNbke/mXf06FGVLVvWdAzAb8qWLUvHwNa49+B2br33UlJSVKJECYWFhZmOAvhF2bJldfToUdMx4AH0KdyOPvUN9iu4nRv3K/YOuJ1b9w4vu+Dh5Pn/z+UIgVud/2ebInM++gpuR195F/0Gt6PfzEtJSaFj4GpuHJ7hLtx7cDu33nvcUHC7MmXKuO7jFvZEn8Lt6FPfoCvgdm7cr9g74HZu3Tu87IKHk+e/+oUig1uVKVNGQUFBfKWXC9BXcDv6yrvoN7gd/WZeSkoKX/ULV+OTVLA77j24nVvvPW4ouN35T95blmU6ClyOPoXb0ae+QVfA7dy4X7F3wO3cund42QUPJ1NTUyVJpUqVCngYfzl16pQGDBig8uXLX/G/+8477ygkJEQ7d+684I/Fx8erUqVKevbZZwuUZ/LkySpUqJCWLVuW5//d7OxsPfnkk/r+++913XXXae3atQXKUhDDhg3TggUL9N5776latWo6duyYJN/9ffKX0NBQFStWLCcvnIu+oq9yi76C09Bv9Ftu0W/Ir9TUVFd1jGSvnqFjzLv66qvpGNga9x73Xm45tYvdeu+lpqaqdOnSpmP4lJ0+dvm4Na906dLKyMjQ6dOnTUeBy9Gn9Glu0Kdgv6IrcsPJXeHG/Yq9g70jt5z6sevWvcPLLng4ee7cOUlS4cKFAx7GX4oVK6YePXqoUKFCV/zv9uvXT9nZ2Rf9Y1FRUWrevHmB89x77735/uqY+fPn69y5c7rjjju0bt061a9fv8B58uOXX37Rjh071K5dOz355JOaO3euihUrJsl3f5/8KTw8XGlpaaZjoIDoK/oqN+grOBH9Rr/lBv2Ggjh37pyrOkayV8/QMeaFh4fn/PsUsCPuPe693HBDF7vt3uOG4oa6Eqd/3J7/59ttH7uwH/qUPr0S+hQSXUFXXJnTu8KN+xV7B3tHbrjhY5d/x7vHBR/Z6enpkqSwsLCAh/GX7OxsBQcHKyjo/7V353FV1nn/x98sbpgrOuOWaxq3e2mOimFZKu63ZdgypiWNWuNkZfWrphmnGseZljvHrMwlM7vdSp10VEwNTRT3BKXIBc3SxnBJQwSE6/eHN5SGyHLO+V7L6/l49IdwhLcHrncfPny5CLrqY4ODL54lvdJjQ0JCivV2riYkJKRUfy8lJaXg7xbnRLu/LF++XDVr1iz4c0RExCWv99Xz5C8UmTvQV/RVcdBXcCL6jX4rDvoNZZGdne2qjpHs1zN0jFl0DOyOeY95rzjoYvthhmKGuhqnX7f5n99uu3ZhP/QpfXo19CkkuoKuuDqnd4Vbv2aS2Hew7yga1y7s5Bd3nMzOzla5cuWMfhIuXrxYt912mz744AO1atWq4Basq1at0qhRoxQVFaUpU6YUPP7VV1/VggULNGbMGD3//PMFL9+2bZvGjRun119/XW+88UaJ/k1r165Vly5dVLt2bc2aNeuKj9u6davGjh2rCRMmaMCAAfr8888LXrdr1y49+OCD+vvf/65BgwYV3Jb453788UfFxsbqscceu+TvFuaDDz7Q4sWLtX37dk2YMEF//etfC32erpQpPj5ed9xxh95//32NGjVK1157raZOnarExEQNGTJE1157reLi4q763Kxbt04zZ87Ujh079I9//EPPPfecmjZtqnnz5hX6+Ct93EwqX748ReYC9NVF9NWV0VdwKvrtIvrtyug3lMWFCxeUl5dnfHlVWM8U9bnq1J4pS8d88cUXhT5PdusYyX49U758+YJFLWBHzHsXMe9dGfOePWVlZdlyhpLcd+36Y4YqKpMvrl03zFAVKlSQxEEf+B99ehF9Wjj6FBL7q59zalfY7WsmyX5d4cb9FfuOi9h3XBn7DtiOdZlZs2ZZlStXvvzFAfXdd99ZISEh1qOPPmp9/vnn1meffWbt37/fGjVqlGVZlnX69GmrQoUKVkpKinXkyBGrWrVqlmVZ1rlz56xy5cpZ2dnZ1unTp60mTZpYWVlZlmVZ1qRJk6wGDRpc9X2fP3/ekmRNmDDB+vHHH61XX33VqlixopWRkWFZlmUNHz7cevLJJy3Lsqxjx45ZTZs2tc6ePWtZlmUtWLDACg8Pt06cOGGlp6dbnTp1snJzcy3Lsqw+ffpYL7/8csH7qVu3rhUfH2/Fx8dbb731VrGfm/Hjx1sPPPDAFZ+nojJlZmZajRs3tkaOHGlduHDBWrhwoVWhQgVr7ty5Vl5envXMM89Yffr0KVaO0aNHW88++2zBnyMiIqxFixYV/Dn/ebrSx820iIgI64UXXjAdA2VEX9FXxUFfwYnoN/qtOOg3lNaPP/5oSbKWL19uNMfl18/s2bOv+LnqxJ7xRccU9jwtWrTIVh1jWZYte+b555+32rRpYzQDUBTmPea94mDes5++fftaI0aMMJrB7deuv2aoQF27Tp+hdu/ebUmyvvzyS6M54H70KX16NfQp2F85uyvYXxWPG/dX7DvYdxQH+w7YyOZf3HHSsqyC28ea8utf/1q1atXSgAED1K5dO3Xr1k3vvPOOzpw5o4kTJ2rq1Knq3LmzNm/erPr162v9+vWSpMTEROXm5urIkSOaPXu2rr/++oKfQunSpUuJToDHxsaqcuXKGjdunEJDQ7Vu3bpfPObdd99VixYtdM0110iS+vfvr9OnT2vBggWaMWOGOnXqVPBcLly4UH/4wx8u+furV6/W1q1bNXr0aJ89T0VlqlixomrXrq3u3bsrJCREPXv2VFZWlm699VYFBQXplltu0ddff12qLFf6aZ8rfdxMCw4OVm5urukYKCP66iL6qmToKzgB/XYR/VYy9BuKKy8vT5Js1zMpKSlX/Fx1as+UtWOkXz5P+/bts1XHSPbsGToGdse8dxHzXskw75mXl5dn/NeFeeHa9ccMZeraddoMlf9r+9x27cJ+6NOL6NPio0+9h/3VT5zYFeyviseNXzOx77iIfUfJsO+ASaGXv6BcuXK2uB1wcHCwQkN/ird//34NGDBAI0aMkCQ9++yzBa87c+aMnnnmGcXGxiooKEi5ubnau3ev6tevX/CYoKCgEhVZ/mODg4PVoUMH7dq1S/3797/kMWlpaZeUflhYmNq0aaNDhw7pu+++RdLGQQAAIABJREFUU4MGDQpel18sP7d69WqVL19ejz/+eMEAXVKXP09FZbpc1apVL/lzuXLllJmZWaocV1LUx82k7Ozsgtvkw7noq58en5+Dvio9+gp2Qr/99Pj8HPRb6dFvuFz+EiInJ8dwkkuvn6I+V4OCghzZM77omPx8+c+T3TpGsmfP0DGwO+a9nx6fn4N5r/Ts2MOSO7u4fPnytpuhJPddu/6Yoa6W6XJevXbzf92c265d2A99+tPj83PQp6VHn7oT+6ufOLEr7NYTkj27wo1fM7Hv+Onx+TnYd5SeHa9byZ3Xrpf94qh3hQoVbFFkl2vbtq02bdp0ycsOHDiglJQUjRw5Ui+88IKaNWtW8LrGjRsrMTHRJ+/7/Pnzuu66637x8vr162vr1q2XvKxcuXKqX7++qlevrpUrV17yumPHjl3y5xdffFGnTp3SxIkTfZLzaplMuNLHzbSsrCyKzAXoq1+ir0qPvoKd0G+/RL+VHv2Gy5UvX15BQUEFS3y7KOpz1ak944WOkezZM3QM7I5575eY90rPjj0subOLy5cvz7V7GafMUFfLZIIdr938z2+3XbuwH/r0l+jT0qNP3Yn9VeGc0hV26wnJnl3hxq+Z2Hf8EvuO0rPjdSu589r1sl8cnCxfvrwsyzL+0xt5eXmXZOjbt6/mzJmjadOmKScnR5s2bdLu3bu1fv16nT17VpL05ZdfKjc3Vzk5ORo8eLBSUlK0e/duSdLRo0eVkZEhy7KK9f7zH3fmzBl9//33Gjx4sKSLt1TPv+Xq0KFDdfr0ae3du1fSxZ942bdvn+68807169dPu3bt0owZM3T+/HktWbJEycnJl7z9SpUqae7cuZo4caLi4+OLlev8+fM6f/78FZ+nojLlPz7/35aRkVHwmPzXFdeFCxd04cKFS/788xz5z9OVPm6mcQLcHeiri+irotFXcCL67SL6rWj0G0orKCjINj/5+/Prp6jPVSf2jK865vLnyW4dIxX9sTOFjoHdMe9dxLxXNOY9+7HLNwHdfO36a4a6Wqb8x5f12nX6DMUd0hAo9OlF9OmV0adgf/UTJ3aF3b5mkuzZFW78mol9x0XsO4rGvgO2Yl1mxYoVliTrxx9/vPxVATN37lxLknXXXXdZhw8ftizLsvLy8qzY2FgrKCjIatSokfXss89aeXl5VlpamlWvXj3rpptusqZNm2Z16dLF6tWrl5Wenm499thjVnh4uHXnnXdaI0eOtJo2bWrNmDGjyPedl5dnPfDAA1ZMTIz1+uuvW4MGDbL27NljWZZlJSYmWo0aNbJuuOEGKyUlxbIsy5ozZ44VFRVlrVixwvrtb39rffjhhwVv609/+pNVrlw5q0KFCtazzz5b8PKFCxdakqwxY8ZY33//vdW/f3+revXq1ltvvVVktsTEROv666+3GjZsaK1YsaLQ56moTJs3b7YqVKhg3X333VZ6err1yiuvWJKs//f//p+Vnp5uxcbGWiEhIVZcXFyROb766isrIiLCat26tbV//35r7dq1VqVKlazBgwdbJ0+evOR52rt3b6EfN9Nq1KhhvfPOO6ZjoIzoK/qKvoJb0W/0G/0Gf6tSpYo1a9Ysoxkuv36u1DGWZTmuZ3zVMYU9T0XlMdExKSkpRX7sTHnooYesnj17Gs0AFIV5j3mPec+ZHnjgAatPnz5GM7j52vX3DFVUJl9cu26YoVavXm1Jsk6fPm00B9yPPqVP6VMUB/srZ3eFnb5msmtXuHF/xb6DfQf7DjjM5iDLuvRI9MaNG3XzzTfr6NGjqlu3rk8PafrC6dOnFRYWpvLlyxe87MKFCwoKClJISIhyc3MVHBysoKAgSRdPOZcrV05BQUEKDQ0tePnV5OTkKCMjQ9WrV7/qY7OyspSWlqamTZtekku6eGI7Ly9PYWFhJfhXll1RmUwo7ONmimVZKl++vObOnauhQ4eajoMyoK8uoq98i76CHdBvF9FvvkW/4efq16+vp556So8++qjpKL9wpc9Veqb4eUyxU88MHTpUeXl5WrRokekoQKGY9y5ycg9fLZMJdupht85748aN0/bt27Vx40bTUQrFtVs8XLtX9uGHH2ro0KHKzs5WSEiI6ThwMfr0IvrUt+hT92F/dZGTu8JuPSHZqyvcuL9i33GRk6/bq2UywU7XrVv3HR6WGHr5S2rVqiVJSk9Pt2WRFVYsoaE//TMuHz4rV678i8f/93//d6FvOygoSEuWLJEklStXrlglJl28zXpEREShr6tYsWKx3kZJspU1UyBz5CvucxkIp06d0oULFwo+1+Fc9BV95csc+egr2AH9Rr/5Mkc++g0/Fx4erhMnTpiOUagrfa66pWfc2jGSvXrmxIkTuu6660zHAK6IeY95z5c58tmph90679l5hpLcfe368nopy7Xr9hkqPT1d1atX55AP/I4+pU/pUxSHnbuC/VXx2O1rJsleXeHG/RX7DvYdvsyRz07XrVv3HV72i4OT4eHhki4WmVstXbrUdIQrsks2u+Twh/zP7fzPdTgXfWWWXbLZJYc/0FfeRb+ZZZdsdsnhD/SbeeHh4a7uGMm+15Bdctklh7+kp6frN7/5jekYwBUx75lll2x2yeEPbp33mKHMsUsuu+Twl/T0dNddt7An+tQcu+SySw5/oU99g64wxy657JLDX9y4v2LfYZZdstklhz+4dd/hZcGXv6BmzZoKDg627U9vAGWV/7lNkTkffQW3o6+8i36D29Fv5tWqVcvVyyuAb1LB7pj34HZunffCw8N18uRJ5eXlmY4C+MWJEye4cwoCgj6F29GnvsH+Cm7nxv0V+w64nVv3HV72i4OTISEhql69ur7//nsTeQC/4wS4e9BXcDv6yrvoN7gd/WYei2e4mWVZOnHiBB0DW2Peg9u5dd6rVauW8vLydPLkSdNRAL9ghkKg0KdwO/rUN9hfwc3cur9i3wG3c+u+w8t+cXBSkho0aKAjR44EOgsQEIcPH1Z4eLjCwsJMR4EP0FdwM/rK2+g3uBn9Zl79+vX19ddfm44B+MXx48d1/vx5NWzY0HQUoEjMe3Azt857DRo0kCTmKLjW4cOHde2115qOAQ+gT+F29KlvsL+Cm7l5f8W+A27m1n2HlxV6cLJx48Y6dOhQgKMAgXHo0CE1adLEdAz4CH0FN6OvvI1+g5vRb+Y1adJEX3/9tXJzc01HAXwu//+f9AzsjnkPbubWea9Ro0YKDg7m2oVrpaWlufLahf3Qp3A7+tQ32F/Bzdy8v2LfATdz677Dywo9ONmkSROlpaUFOgsQEHyx4i70FdyMvvI2+g1uRr+Z17hxY+Xk5Ojo0aOmowA+l5aWptDQ0IK72AB2xbwHN3PrvFexYkXVqVOHbwLClfK/PmjcuLHpKPAA+hRuRp/6DvsruJmb91fsO+Bmbt13eFmhBycbNWrEFytwrbS0NL5YcRH6Cm5GX3kb/QY3o9/My3/+WWDBjdLS0tSgQQOFhoaajgIUiXkPbubmeY+7p8CtDh8+rNzcXL4JiIChT+FW9KnvsL+Cm7l5f8W+A27m5n2HVxV6cPK6667Tf/7zH505cybQeQC/sixLBw4cULNmzUxHgY/QV3Ar+gr0G9yKfrOHunXrKiwsTPv27TMdBfC5/fv30zFwBOY9uJXb571mzZoxQ8GV9u/fL0lq2rSp4STwCvoUbkWf+g77K7iZm/dX7DvgVm7fd3hVoQcnW7duLcuytGfPnkDnAfzq8OHDOnPmjNq0aWM6CnyEvoJb0Veg3+BW9Js9BAcHq1WrVnQMXCkpKUlt27Y1HQO4KuY9uJXb573WrVsrOTnZdAzA55KSktSgQQPVrFnTdBR4BH0Kt6JPfYf9FdzMzfsr9h1wK7fvO7yq0IOTjRs3VvXq1ZWUlBToPIBfJSUlKSgoSK1btzYdBT5CX8Gt6CvQb3Ar+s0+2rZtS8fAdXJzc5WSksLyCo7AvAe3cvu817ZtW3377bdKT083HQXwqeTkZGYoBBR9CreiT32L/RXcyO37K/YdcCu37zu8qtCDk0FBQWrVqhU/6QXXSU5OVuPGjVW1alXTUeAj9BXcir4C/Qa3ot/so02bNtq9e7fpGIBPHThwQOfOnXPt4hnuwrwHt3L7vJd/VxiuXbiNm+96BHuiT+FW9Klvsb+CG7l9f8W+A27l9n2HVxV6cFLipzfgTsnJyZz+diH6Cm5EX0Gi3+BO9Jt9tGvXTidOnNDRo0dNRwF8Jjk5WSEhIWrZsqXpKECxMO/Bjdw+79WrV0+1atXi2oWr5OTkKDU11dXXLuyHPoUb0ae+x/4KbuSF/RX7DriR2/cdXnXFg5MdO3bUjh07lJ2dHcg8gF8lJibqpptuMh0DPkZfwY3oK0j0G9yJfrOPG2+8USEhIdqyZYvpKIDPJCYmqlWrVgoLCzMdBSgW5j24kRfmvY4dOzJDwVV27dqlrKws11+7sB/6FG5Dn/oe+yu4kRf2V+w74EZe2Hd40RUPTkZGRiozM1Off/55IPMAfvPtt9/q8OHD6tatm+ko8DH6Cm5DXyEf/Qa3od/spWrVqmrVqpUSEhJMRwF8JiEhQZGRkaZjAMXGvAe38cq8FxkZyQwFV9m4caNq1aqlFi1amI4Cj6FP4Tb0qe+xv4IbeWF/xb4DbuOVfYcXXfHgZIsWLVS7dm2GELjGxo0bFRoayglwF6Kv4Db0FfLRb3Ab+s1+IiMjtWnTJtMxAJ/IysrSzp07Xb94hrsw78FtvDLvRUZG6uuvv9Y333xjOgrgE/nfvA8KCjIdBR5Dn8Jt6FP/YH8FN/HK/op9B9zGK/sOL7riwcmgoCB16dKFIQSukZCQoBtuuEHXXHON6SjwMfoKbkNfIR/9Breh3+wnMjJSO3bsUGZmpukoQJlt3bpVWVlZrl88w12Y9+A2Xpn3OnXqpHLlynHtwjU2b97MDAUj6FO4DX3qH+yv4CZe2V+x74DbeGXf4UVXPDgpSd26ddOGDRuUl5cXqDyA32zYsMH1A4iX0VdwE/oKP0e/wU3oN/vp1q2bsrOzlZiYaDoKUGbr169XgwYN1LhxY9NRgBJh3oObeGXeq1y5sm644QatX7/edBSgzFJTU3Xs2DF+5RyMoE/hJvSp/7C/gpt4aX/FvgNu4pV9hxcVeXCyV69eOn78uHbt2hWoPIBfHDt2TElJSerdu7fpKPAT+gpuQV/hcvQb3IJ+s6dGjRrp+uuv16pVq0xHAcps5cqVdAwciXkPbuG1ea9Xr15auXKl6RhAma1cuVI1atTgV87BGPoUbkGf+g/7K7iJl/ZX7DvgFl7bd3hNkQcn27Ztq/r16/MFCxxvxYoVqlixoqKiokxHgZ/QV3AL+gqXo9/gFvSbfUVHR7N4huOdOnVKW7duVXR0tOkoQIkx78EtvDbvRUdHKy0tTampqaajAGWyatUq9ezZU6GhoaajwKPoU7gFfepf7K/gBl7bX7HvgFt4bd/hNUUenAwKClLv3r0ZQuB4q1at0i233KKwsDDTUeAn9BXcgr7C5eg3uAX9Zl/R0dFKSkrSkSNHTEcBSu2TTz6RZVnq0aOH6ShAiTHvwS28Nu917txZNWvW5NqFo2VmZmrDhg3cOQVG0adwA/rU/9hfwQ28tr9i3wG38Nq+w2uKPDgpXRxCEhMTdeLEiUDkAXwuJydHa9as4YsVD6Cv4HT0Fa6EfoPT0W/21r17d4WFhbHAgqOtWLFCXbp0Uc2aNU1HAUqFeQ9O58V5LyQkRLfffrtWrFhhOgpQap9++qnOnz/vqWsX9kOfwg3oU/9jfwU38OL+in0HnM6L+w6vKdbByfLly2vp0qWByAP43CeffKIffvhBgwYNMh0FfkZfwenoK1wJ/Qano9/srVKlSurdu7c+/PBD01GAUsnJydGyZcs0ePBg01GAUmPeg9N5dd4bNGiQ1q1bp++//950FKBUFi1apE6dOql+/fqmo8Dj6FM4HX3qf+yv4HRe3V+x74DTeXXf4SVXPThZpUoVRUdHa+HChYHIA/jcggUL1LlzZzVu3Nh0FPgZfQWno69wJfQbnI5+s7+YmBitW7dOx48fNx0FKLG4uDidOnVKd955p+koQKkx78HpvDrvDRo0SBUqVOCbgHCk7Oxsffzxx4qJiTEdBaBP4Wj0aeCwv4KTeXV/xb4DTufVfYeXXPXgpCQNHTqUIQSOlJWVpX/96198seIh9BWcir7C1dBvcCr6zRkGDhzIN6ngWAsXLlSXLl3UqFEj01GAMmHeg1N5ed6rXLky3wSEY3n1m/ewJ/oUTkafBg77KziZl/dX7DvgVF7ed3hJsQ5O9u/fX+XLl9eSJUv8nQfwqbi4OJ09e1ZDhgwxHQUBQl/BqegrXA39Bqei35whLCxMffv25ZtUcJzz589zZw+4BvMenMrr815MTIzi4+P5JiAcZ9GiReratasnv3kPe6JP4VT0aeCwv4JTeX1/xb4DTuX1fYdXFOvgZOXKlTVo0CC99957/s4D+NR7772nqKgoNWjQwHQUBAh9Baeir3A19Bucin5zjnvuuUeffvqpDh8+bDoKUGxLly5VRkaGZxfPcBfmPTiV1+e9/v37KywsTHPnzjUdBSi2M2fOaPHixbrnnntMRwEK0KdwIvo08NhfwYm8vr9i3wGn8vq+wyuKdXBSkkaOHKnNmzdr9+7d/swD+Mx//vMfLVu2TCNHjjQdBQFGX8Fp6CsUF/0Gp6HfnGXAgAGqU6eOZs2aZToKUGzTp09Xv379VLduXdNRAJ9g3oPTMO9dvPPRvffeq+nTp8uyLNNxgGKZN2+e8vLydO+995qOAhSgT+FE9Gngsb+CE7G/Yt8B52Hf4R3FPjjZo0cPNW/eXO+++64/8wA+M3v2bFWuXFl33HGH6SgIMPoKTkNfobjoNzgN/eYsoaGhuv/++zVr1izl5uaajgNcVVpamuLj4xUbG2s6CuAzzHtwGua9i0aPHq0vv/xSCQkJpqMAxTJjxgwNGTJENWrUMB0FuAR9CqehTwOP/RWchv3VRew74DTsO7yj2Acng4KC9OCDD+q9997TuXPn/JkJKDPLsjRr1iwNGzZMYWFhpuMgwOgrOAl9hZKg3+Ak9JszPfTQQzp69KhWrlxpOgpwVdOnT1fdunXVp08f01EAn2Heg5Mw7/2kXbt2uvHGGzV9+nTTUYCrSkpK0vbt2z3/zXvYE30KJ6FPzWF/BSdhf3UR+w44CfsObyn2wUlJGjFihM6dO6f58+f7Kw/gE6tXr9ZXX33FFyseRl/BKegrlBT9Bqeg35ypadOm6tGjh9544w3TUYAinT9/XjNnztSDDz6okJAQ03EAn2Leg1Mw711q1KhRWrhwoY4fP246ClCkf/7zn4qIiNDNN99sOgpQKPoUTkGfmsP+Ck7B/upS7DvgFOw7vCXIsiyrJH9hxIgR2rJli/bu3avg4BKduwQCpmfPngoKCtLq1atNR4FB9BWcgL5CadBvcAL6zblWrFihfv366fPPP1e7du1MxwEKNW3aND366KNKS0tT3bp1TccBfI55D07AvHepzMxMNWzYUI888ogmTJhgOg5QqOPHj6tRo0aaPHmyfve735mOAxSKPoUT0Kfmsb+CE7C/+iX2HXAC9h2ekljiJho/frxSU1MVFxfnj0BAmSUlJWnt2rUaP3686SgwjL6C3dFXKC36DXZHvzlbnz591KpVK73++uumowCFsixLkydP1rBhw1g6w7WY92B3zHu/VKlSJT388MOaMmWKMjIyTMcBCjV58mRVrVpVw4YNMx0FuCL6FE5An5rH/gp2x/6qcOw7YHfsO7ynxHeclKTevXsrNzdXa9as8UcmoEx++9vfKikpSbt371ZQUJDpODCMvoKd0VcoC/oNdka/Od/MmTM1ZswYHTx4UA0aNDAdB7jE0qVLdccdd2jPnj1q2bKl6TiA3zDvwc6Y9wp3/PhxNW7cWK+99ppGjx5tOg5wiXPnzqlhw4Z69NFH9fzzz5uOAxSJPoWd0af2wf4Kdsb+6srYd8DO2Hd4TsnvOCldPAW+du1abdmyxdeBgDI5ePCgFi5cqPHjx1NikERfwb7oK5QV/Qa7ot/c4b777lN4eDg/tQ9bmjRpkvr168fSGa7HvAe7Yt67sl/96lf67W9/q1deeUU5OTmm4wCXePvtt5WZmakxY8aYjgJcFX0KO6NP7YP9FeyM/dWVse+AXbHv8KZS3XFSkqKiolSpUiVuoQtbGTFihBISEvTFF18oNDTUdBzYBH0FO6Kv4Av0G+yIfnOP119/Xc8884z279+v+vXrm44DSJKWL1+uAQMGaMuWLerUqZPpOIDfMe/Bjpj3inb48GG1aNFCU6dOVWxsrOk4gCQpIyNDTZs21YgRI/T3v//ddBygWOhT2BF9aj/sr2BH7K+ujn0H7Ih9hycllvrg5Nq1a3X77bcrPj5e3bt393UwoMT27dunli1bavbs2brvvvtMx4GN0FewG/oKvkK/wW7oN3c5f/68mjdvrsGDB+uf//yn6TiALMvSb37zG9WrV09Lly41HQcICOY92A3zXvGMHj1aK1eu1FdffaUKFSqYjgNo0qRJ+utf/6oDBw7oV7/6lek4QLHRp7Ab+tR+2F/BbthfFQ/7DtgN+w7PKv3BSUnq0aOHcnJy9Nlnn/kyFFAq9957r3bs2KGUlBSFhISYjgOboa9gJ/QVfIl+g53Qb+7z5ptv6vHHH9e+fft07bXXmo4Dj1uyZInuvPNO7dy5U+3btzcdBwgY5j3YCfNe8Rw9elTXXXedXnnlFT388MOm48DjfvzxRzVt2lSjRo3Siy++aDoOUCL0KeyEPrUv9lewE/ZXxce+A3bCvsOzynZwcuPGjbr55pu1YsUK9enTx5fBgBLZuXOnbrrpJs2bN08xMTGm48CG6CvYBX0FX6PfYBf0mztlZ2fr+uuv1y233KJ3333XdBx4WE5Ojtq1a6fWrVtr4cKFpuMAAcW8B7tg3iuZcePGaeHChUpNTVWVKlVMx4GHPffcc3rzzTd18OBB1ahRw3QcoMToU9gFfWpf7K9gF+yvSoZ9B+yCfYenle3gpCQNGTJEe/fuVVJSksqVK+erYECJdO/eXTk5OUpISFBQUJDpOLAp+gp2QF/BH+g32AH95l4LFy7UPffco82bN6tTp06m48CjJk+erKeeekp79uxR8+bNTccBAo55D3bAvFcyp06dUvPmzfXQQw/pb3/7m+k48Ki0tDS1bNlSEydO1GOPPWY6DlAq9CnsgD61P/ZXsAP2VyXHvgN2wL7D0xKDy/oWXn75ZR06dEhTp071RSCgxObPn6+NGzfq9ddfp8RQJPoKptFX8Bf6DabRb+4WExOjbt26ady4cSrjz90BpXLy5Em9+OKLeuKJJ1g6w7OY92Aa817J1ahRQ3/605/02muvad++fabjwKOefPJJNW7cWL///e9NRwFKjT6FHdCn9sf+Cqaxvyod9h0wjX0HynzHSUl69tln9fbbb+urr75SrVq1fJELKJbMzExFRETotttu06xZs0zHgQPQVzCFvoK/0W8whX7zhvxfVTF37lzdc889puPAYx555BEtWbKEX80Hz2PegynMe6WX/6v6IiIitHjxYtNx4DHx8fG69dZb9e9//1t9+/Y1HQcoE/oUJtGnzsH+Ciaxvyo99h0whX0H5Itf1S1JZ8+eVUREhHr27KnZs2f7IBdQPM8884zefPNNpaamqk6dOqbjwAHoK5hCX8Hf6DeYQr95R2xsrFasWKGUlBRVr17ddBx4xLZt29SlSxfNnDlTw4cPNx0HMIp5D6Yw75VNXFycoqOjtXz5cvXr1890HHhEVlaWbrzxRjVp0kTLly83HQfwCfoUJtCnzsP+Ciawvyob9h0whX0H5KuDk5K0fPlyDRgwQHFxcerVq5cv3iRQpKSkJHXs2FGTJ0/WmDFjTMeBg9BXCDT6CoFCvyHQ6DdvOXnypFq2bKlBgwZp2rRppuPAAy5cuKBOnTqpWrVqWrduHb8qBRDzHgKPec837r33XiUkJGjPnj3cfQYBMWHCBL388stKTk5W06ZNTccBfIY+RaDRp87D/gqBxv7KN9h3INDYd+D/+O7gpCTdeeed2rlzp5KTk3XNNdf46s0Cv5CXl6fIyEhJUkJCgoKDgw0ngtPQVwgU+gqBRr8hUOg3b1qwYIHuueceffLJJ7rttttMx4HLTZw4US+99JKSkpJ03XXXmY4D2AbzHgKFec930tPT1bJlS91999365z//aToOXC41NVXt2rXTpEmTNG7cONNxAJ+iTxFI9Klzsb9CILG/8h32HQgU9h34mUSffvTfeOMNnT59Wn/+8599+WaBX/if//kf7dq1S7NmzaLEUCr0FQKFvkKg0W8IFPrNm4YOHar+/fvr4YcfVmZmpuk4cLHU1FS9+OKL+vOf/8zSGbgM8x4ChXnPd2rVqqVXXnlFU6dO1aZNm0zHgYvl5ubqwQcfVLt27TR27FjTcQCfo08RKPSps7G/QqCwv/It9h0IFPYd+Dmf3nFSkt59913FxsZqzZo1uvXWW335pgFJUnJysjp16qQ//vGPeu6550zHgYPRV/A3+gqm0G/wN/rN27755hu1adNG9957r6ZOnWo6DlwoJydHXbt2lSRt3rxZoaGhhhMB9sO8B39j3vOPfv36KTU1Vbt27eJXzMIvJk6cqBdeeEHbtm1TmzZtTMcB/IY+hb/Rp87H/gr+xv7KP9h3wN/Yd+Ayvv1V3fmGDh2qhIQE7d69W+Hh4b5+8/CwrKwsderUSVWqVNH69esVEhJiOhIcjr6Cv9BXMI1+g7/Qb5Ckjz76SHfddZf+9a9/acCAAabjwGWeeeYZTZkyRdu3b1dERITpOIBtMe/BX5j3/Of48eNq27at+vTpo3fffdd0HLjMjh071LVrV02aNEmPPfaY6TiAX9Gn8Cf61D3YX8GC3XEUAAAgAElEQVSf2F/5D/sO+Av7DhTCPwcnT506pfbt26tjx4766KOPfP3m4WF/+MMfNGfOHH3++edq3Lix6ThwAfoK/kJfwTT6Df5CvyHf8OHDtXLlSiUlJalOnTqm48AlPvvsM916661666239NBDD5mOA9ga8x78hXnPv1auXKl+/frpf//3f3X33XebjgOXyMjI0I033qhGjRopLi5OQUFBpiMBfkefwh/oU/dhfwV/YH/lX+w74C/sO1CIRL/8svYaNWpo1qxZWrp0qd555x1/vAt40Mcff6w33nhDb775JiUGn6Gv4A/0FeyAfoM/0G/4uSlTpqhKlSoaPny4cnNzTceBC3z//fe67777NHDgQJbOQDEw78EfmPf8r0+fPho9erQefvhhHTx40HQcuMSYMWN08uRJzZ49m0M+8Az6FP5An7oP+yv4Gvsr/2PfAX9g34Er8csdJ/M9//zzevnll7VhwwZ16tTJX+8GHrBv3z7ddNNNGjJkiGbMmGE6DlyIvoKv0FewG/oNvkK/oTDbtm3TzTffrPHjx+ull14yHQcOlpubq969e+vgwYPavn27atasaToS4BjMe/AV5r3AyczMVNeuXSVJCQkJCgsLM5wITvbmm29q7NixWr58ufr06WM6DhBQ9Cl8iT51L/ZX8BX2V4HFvgO+wr4DRfDPr+rOl5eXp/79+2vPnj3avn27fvWrX/nrXcHFMjIy1LlzZ4WGhmrTpk2qVKmS6UhwIfoKvkBfwY7oN/gC/YaivP/++xo+fLgWLVqkO++803QcONRTTz2lKVOmaOPGjerQoYPpOICjMO/BF5j3Au/w4cPq2LGjevXqpQ8++MB0HDhUYmKiunfvrj/+8Y96/vnnTccBjKBP4Qv0qfuxv4IvsL8KLPYd8AX2HbgK/x6clKSTJ0+qY8eOatKkieLi4hQaGurPdweXsSxL9957r9asWaPt27erUaNGpiPBxegrlAV9BTuj31AW9BuK46GHHtKiRYu0ZcsWXX/99abjwGE+/PBDxcTEaNasWRoxYoTpOIAjMe+hLJj3zFm1apX69eun119/XWPHjjUdBw7z3XffqWPHjmrfvr0+/vhjBQcHm44EGEOfoizoU+9gf4WyYH9lBvsOlAX7DhRDot8nv5o1a+qjjz5SYmKiHnnkEX+/O7jMX/7yF3344YeaN28eJQa/o69QFvQV7Ix+Q1nQbyiON954Q9dff7369++v9PR003HgINu2bdPw4cM1ZswYls5AGTDvoSyY98yJjo7WCy+8oMcff1wrV640HQcOcu7cOQ0cOFBhYWF6//33OeQDz6NPUVr0qbewv0Jpsb8yh30HyoJ9B4ojZMKECRP8/U7q1q2r9u3b66mnnlKFChXUrVs3f79LuMD8+fP1hz/8QVOmTNHQoUNNx4FH0FcoDfoKTkC/oTToNxRXaGioBg8erJkzZ2rJkiW67777VK5cOdOxYHOHDh3Sbbfdps6dO2vOnDl8gwooI+Y9lAbznnk333yzDh48qBdffFF9+vRRnTp1TEeCzeXl5enuu+/W559/rrVr16pBgwamIwG2QJ+ipOhT72F/hdJgf2Ue+w6UBvsOFNM3fv9V3T83bdo0jRkzRu+//77uu+++QL1bONCGDRvUq1cvPfbYY/rb3/5mOg48iL5CcdFXcBr6DcVFv6E0vvjiC0VGRur222/X/PnzWSTiis6cOaNu3brJsixt3LhR1apVMx0JcA3mPRQX85595OTkqG/fvkpJSVFiYqKuvfZa05FgY+PGjdO0adO0Zs0aRUZGmo4D2Ap9ipKgT72L/RWKi/2VvbDvQHGx70AJJAb04KT00xC6fPly3XbbbYF813CIXbt2qUePHurdu7fmzZunoKAg05HgUfQVroa+glPRb7ga+g1l8emnnyo6OlqjR4/W5MmTTceBDZ07d07R0dFKS0tTYmKi6tevbzoS4DrMe7ga5j37OX36tCIjIxUcHKz4+HiFh4ebjgQb+vvf/65nn31W8+bNU0xMjOk4gC3RpygO+hTsr3A17K/siX0HroZ9B0oo8Acn8/LyNGzYMC1dulQrV65UVFRUIN89bO6rr75SVFSU2rRpo2XLlqlixYqmI8HD6CsUhb6Ck9FvKAr9Bl9YsmSJYmJi9PTTT+ull14yHQc2kp2drcGDB2vr1q2Kj49Xq1atTEcCXIl5D0Vh3rOvb775RlFRUapWrZrWrVunGjVqmI4EG3nzzTf1yCOP6NVXX9Xjjz9uOg5ga/QpikKfIh/7K1wJ+yv7Yt+BorDvQCkE/uCkJOXm5uqee+5RXFyc1q5dq44dOwY6Amzo66+/1s0336z69etr9erVuuaaa0xHAugrFIq+ghvQbygM/QZfmjNnjh544AG99NJLeuaZZ0zHgQ3w/x4gsLjmUBjmPfs7cOCAoqKi1KhRIz5GKPD+++9rxIgRzNZACdCnKAx9isuxv8Ll+Fra/vgYoTDsO1BKZg5OSlJWVpYGDhyonTt3avXq1brhhhtMxIBNpKWlqUePHgoPD9fatWtVrVo105GAAvQVfo6+gpvQb/g5+g3+MHXqVI0dO1Yvv/yynnjiCdNxYNCFCxc0bNgwLVu2THFxcYqMjDQdCfAE5j38HPOec6SkpOiWW25RmzZt9PHHH6ty5cqmI8GgefPm6f7779eTTz6piRMnmo4DOAp9ip+jT3El7K+Qj/2Vc7DvwM+x70AZJAabes8VKlTQkiVL1L59e/Xo0UObN282FQWGffnll7r55ptVs2ZNxcXFUWKwHfoK+egruA39hnz0G/zlkUce0WuvvaYnn3xSL7zwguk4MCQrK0tDhgzR8uXLtWzZMpbOQAAx7yEf856ztGzZUqtXr1ZycrJ69+6tH374wXQkGDJjxgwNGzZM48aN45APUAr0KfLRpygK+ytI7K+chn0H8rHvQFkZOzgpSWFhYfr3v/+tHj16qGfPnlqzZo3JODBg165dioqKUv369fXJJ58oPDzcdCSgUPQV6Cu4Ff0G+g3+Nm7cOE2bNk1/+ctfNHbsWBn6pQcw5Ny5cxo4cKDWr1+vuLg43XrrraYjAZ7DvAfmPWdq3769NmzYoMOHD6tHjx5KT083HQkBNnXqVP3ud7/T+PHj9fLLL5uOAzgWfQr6FMXB/srb2F85E/sOsO+ALxg9OClJ5cuX1/z58zVgwAANHDhQS5cuNR0JAbJhwwb16NFD7dq107p161SzZk3TkYAi0VfeRV/B7eg376LfECgPPfSQ5syZo7ffflujR4/WhQsXTEdCAJw4cUI9e/bUrl27tG7dOnXt2tV0JMCzmPe8i3nP2SIiIhQfH69Tp07plltu0ZEjR0xHQgBYlqU//elPGjt2rF555RVNmjTJdCTA8ehTb6JPUVLsr7yJ/ZWzse/wLvYd8BXjByclqVy5cpo7d66GDx+uIUOGaPLkyaYjwc/+93//V7169dLtt9+u5cuXq3LlyqYjAcVCX3kPfQWvoN+8h35DoN1333366KOPNHfuXA0aNEhnz541HQl+tH//fnXp0kVHjx7Vhg0bdMMNN5iOBHge8573MO+5Q7NmzbRhwwYFBQWpc+fO2rlzp+lI8KPs7GwNHz5cf/vb3zR9+nQ9/vjjpiMBrkGfegt9itJif+Ut7K/cgX2H97DvgC/Z4uCkJIWEhOitt97Sq6++qscff1yjRo3ipzhcavLkyRo2bJhGjRqlBQsWqEKFCqYjASVCX3kHfQWvod+8g36DKQMHDlR8fLx27NihyMhI7vLhUomJiYqMjFRYWJg2btyoiIgI05EA/B/mPe9g3nOXBg0aaPPmzWrXrp2ioqK0bNky05HgB6dOnVJ0dLSWLFmipUuXauTIkaYjAa5Dn3oDfYqyYn/lDeyv3IV9h3ew74Cv2ebgZL5HH31U8+bN05w5czR48GCdPn3adCT4SGZmpoYPH64nnnhCU6ZM0eTJkxUcbLtPQaDY6Cv3oq/gdfSbe9FvsIObbrpJmzdvVk5Ojrp27aqtW7eajgQfmjNnjm699VZ17txZCQkJql+/vulIAArBvOdezHvudc011+hf//qXYmJiNHjwYP3P//yP6UjwoS+//FJdu3bVvn37lJCQoH79+pmOBLgWfepu9Cl8hf2Vu7G/ci/2He7FvgP+YsvPopiYGK1bt047d+7UTTfdpOTkZNORUEZpaWmKjIzUsmXLtGzZMj388MOmIwE+QV+5D30FXES/uQ/9Bjtp0qSJNm3apFatWikqKkrvvPOO6Ugoo+zsbP3+97/XiBEj9Pvf/16LFy/mV6QANse85z7Me+5Xrlw5zZo1S3/961/15JNP6u6779aPP/5oOhbKaPHixerUqZOqVaumxMREtW3b1nQkwPXoU3eiT+Fr7K/ch/2VN7DvcB/2HfAnWx6clKQuXbro888/V8OGDdWpUyfNnj3bdCSU0qpVq9SxY0fl5eVp27Zt6tOnj+lIgE/RV+5BXwGXot/cg36DHdWoUUMrV67UX/7yF40ZM0b333+/zp07ZzoWSuHo0aO69dZb9d5772n+/Pl6+eWXFRISYjoWgGJg3nMP5j1vefrpp7V27VrFx8erY8eOSklJMR0JpZCbm6sJEyborrvu0n//93/r008/5W5HQIDRp+5An8Kf2F+5B/srb2Hf4R7sO+Bvtj04KUm1a9dWXFycHn30UT3wwAO6//77+YkvB7lw4YImTJig/v37Kzo6WgkJCWrWrJnpWIBf0FfORl8BV0a/ORv9BrsLCgrS008/rY8//ljLly9Xp06dtHv3btOxUAJxcXHq2LGj0tPTtXnzZsXExJiOBKCEmPecjXnPu7p3767t27erRo0a6ty5M3dAcpivv/5aPXr00N///ndNmzZNc+bMUaVKlUzHAjyJPnU2+hSBwP7K+dhfeRP7Dmdj34FAsfXBSUkKDQ3VpEmT9NFHH2nFihXq0KGDtm/fbjoWriI1NVWdO3fWK6+8omnTpumDDz7gNtdwPfrKmegr4OroN2ei3+Ak/fr10/bt21W1alV16dJFb7zxhizLMh0LRcjMzNTDDz+sPn36qFevXtq+fbtat25tOhaAUmLecybmPTRo0EDx8fGKjY3V6NGjdffdd+v06dOmY+Eq5s2bp7Zt2+rEiRPasmWLYmNjTUcCPI8+dSb6FIHG/sp52F+BfYczse9AINn+4GS+O+64Q7t371bDhg3VtWtXTZw4URcuXDAdC5exLEtvvfWWOnTooODgYO3cuVMjR440HQsIKPrKGegroOToN2eg3+BUTZs21YYNG/TUU0/pscceU//+/XX06FHTsVCI7du3q0OHDpo/f77mz5+v2bNnq0qVKqZjAfAB5j1nYN7Dz1WoUEGvvfaa4uLitGHDBrVr107r1q0zHQuFOHXqlIYNG6b77rtPw4YN07Zt29S2bVvTsQD8H/rUOehTmMT+yjnYX+Hn2Hc4A/sOmOCYg5OSVL9+fcXFxelvf/ubXnzxRd10003asWOH6Vj4P6mpqerevbv+8Ic/6NFHH1VCQoJatGhhOhZgBH1lb/QVUHr0m73Rb3C60NBQTZgwQevXr1dqaqpatmypadOm8dP7NpGRkaEnnnhCnTt3Vp06dbR7925+tRHgQsx79sa8hyvp2bOnkpKS1LFjR91+++0aOXKkTp06ZToW/s/ChQv1X//1X1q7dq2WL1+uKVOm8KtkAZuiT+2NPoUdsL+yN/ZXuBL2HfbGvgOmOOrgpCQFBwfriSee0O7du1WtWjV17txZ48eP17lz50xH86ycnBy99NJLat++vTIyMrRlyxb99a9/Vbly5UxHA4yir+yHvgJ8g36zH/oNbtO1a1clJSXpd7/7nX7/+9/rlltuUWpqqulYnrZ69Wq1adNGs2bN0ttvv621a9fq2muvNR0LgJ8w79kP8x6Ko1atWvroo4+0cOFCrVy5Ui1bttSiRYtMx/K0I0eOaODAgbr77rs1YMAApaSkqG/fvqZjAbgK+tR+6FPYEfsr+2F/hath32E/7DtgmuMOTuZr0aKFPv30U82cOVOzZ89WixYtNGfOHH6SI8DWrFmjG264QRMnTtTTTz+txMRE3XjjjaZjAbZCX9kDfQX4Hv1mD/Qb3CosLEz/+Mc/tH37dmVmZqpNmzZ69NFH9cMPP5iO5ilHjhzR/fffr969e6tVq1ZKTk5WbGysgoKCTEcDEADMe/bAvIeSGjJkiL744gvFxMTo7rvv1m233aakpCTTsTwlOztbkydPVqtWrZSSkqLVq1dr+vTpql69uuloAEqAPjWPPoXdsb+yB/ZXKCn2HfbAvgN24NiDk5IUFBSk+++/X3v27FGvXr30wAMPKCoqitvpBkBqaqr69eunnj17KiIiQnv37tWECRM49Q1cAX1lDn0F+Bf9Zg79Bq9o166dNm3apFdffVVz585VRESEZs2apby8PNPRXC0jI0PPPfecWrRooa1bt+rf//63li1bpgYNGpiOBiDAmPfMYd5DWVSrVk2TJ0/W+vXrdfr0aXXo0EFjx47VyZMnTUdzvY8++kgRERF67rnn9PTTT2vPnj26/fbbTccCUEr0qTn0KZyE/ZUZ7K9QFuw7zGHfATtx9MHJfHXq1NGsWbO0ZcsWWZalTp06adiwYdq/f7/paK7z7bff6pFHHlGbNm307bff6tNPP9WHH36oJk2amI4GOAJ9FTj0FRBY9Fvg0G/wotDQUI0dO1b79u3TkCFDNGrUKHXo0EHLly83Hc11srOzNXXqVLVo0UJvvvmmJk6cqOTkZH4FGgDmvQBi3oMvdevWTdu2bdPbb7+tDz/8UM2bN9ekSZOUkZFhOprrbNiwQVFRUbrrrrvUrVs3paam6rnnnlPFihVNRwPgA/Rp4NCncCr2V4HD/gq+xL4jcNh3wI5ccXAyX8eOHfXZZ59p3rx52rZtm/7rv/5LI0eO1KFDh0xHc7zvvvtO48aN03XXXafly5frzTff1I4dO3TLLbeYjgY4En3lP/QVYBb95j/0GyDVrFlTU6ZM0e7du9W4cWMNHDhQnTt3VlxcnOlojpeTk6Pp06erefPmGj9+vIYMGaKvvvpKjz32GD/pC+ASzHv+w7wHfwkODtbIkSOVmpqqhx9+WBMnTlTTpk312muvKTMz03Q8x9u8ebN69uyp7t27q1y5ctq8ebPmzJmj+vXrm44GwMfoU/+iT+EW7K/8h/0V/Il9h/+w74CtWS514cIF67333rOaNWtmlS9f3nrwwQetPXv2mI7lOAcPHrTGjh1rhYWFWfXq1bOmTJlinT9/3nQswFXoK9+grwD7od98g34Drmz79u1W3759LUlW165drcWLF1u5ubmmYzlKRkaG9cYbb1hNmjSxypcvb40ePdo6cuSI6VgAHIJ5zzeY9xBo33//vfXkk09aYWFhVt26da1JkyZZp06dMh3LcT755BOrV69eliSrW7du1qeffmo6EoAAo099gz6F27G/Kjv2Vwg09h2+wb4DDrDZtQcn82VnZ1szZsywIiIirKCgIKtv377W2rVrTceyva1bt1oxMTFWSEiI1ahRI+v111+3zp07ZzoW4Gr0VenQV4D90W+lQ78Bxbdp0yZrwIABVnBwsNW8eXPrrbfe4nq5iu+++856/vnnrfDwcKtSpUrW6NGjrbS0NNOxADgU817pMO/BtO+++84aP368VbVqVatKlSrWuHHjrEOHDpmOZWvZ2dnW3Llzrfbt21uSrB49elhxcXGmYwEwjD4tOfoUXsT+quTYX8E09h2lw74DDrI5yLIsy+QdLwMlLy9P//73v/Xqq69q/fr1atOmjWJjYzVs2DDVqFHDdDxb+PHHH7VgwQLNmDFDiYmJ6tChQ8EtrkNDQ03HAzyDvro6+gpwJvrt6ug3oGxSU1P12muvac6cOapcubKGDRum2NhYtWrVynQ0W7AsS/Hx8ZoxY4YWL16sKlWq6JFHHtHDDz+s2rVrm44HwAWY966OeQ92dObMGU2fPl2TJ0/WsWPH1LdvXz300EPq06ePQkJCTMezhbS0NM2cOVPvvvuujh8/rrvuukvjx4/XjTfeaDoaABuhT6+OPgXYX10N+yvYEfuOq2PfAYdK9MzByZ/bvn273n77bS1YsEAXLlzQkCFD9MADD6h79+6e+8LFsiwlJiZq9uzZmjdvnnJycjR48GCNGjVK3bt3Nx0P8Dz66if0FeAu9NtP6DfA944fP64ZM2Zo5syZOnjwoLp27arY2Fjdcccdqlatmul4AXfkyBF98MEHmjlzpvbv36/OnTsrNjZW9957rypVqmQ6HgCXYt77CfMenCInJ0eLFy/WO++8o08//VT16tXTgw8+qPvvv1/XXXed6XgBd+7cOX388ceaOXOm1q1bpzp16mjEiBEaNWqUGjZsaDoeABujTy9FnwKFY391KfZXcAr2HT9h3wEX8ObByXxnz57V/PnzNWPGDG3dulV16tTRkCFDFBMTo8jISAUHB5uO6Dfbt2/XggULtGjRIh0+fFitW7cuOBFfs2ZN0/EAXIa+oq8At6Lf6DfAnyzL0rp16zR9+nQtXbpUkhQdHa2YmBgNGDBAVapUMZzQf44ePaoPP/xQCxcu1KZNm1SjRo2COxi0bt3adDwAHsK8x7wHZzpw4IBmzpyp2bNn69ixY7rxxhsVExOjmJgYNWnSxHQ8vzl//rxWrlyphQsXatmyZTp//rz69u2r2NhY9e3blzulACgx+pQ+Ba6G/RX7KzgT+w72HXAFbx+c/LnU1FQtXLhQCxYs0N69e1WvXj317dtX0dHRuv322x3/Ux0ZGRmKj4/XypUrtWLFCqWlpalZs2YaOnSoYmJi1K5dO9MRARQTfUVfAW5Fv9FvgD+dPn1aS5cu1cKFC7VmzRqFhoaqR48e6tOnj6Kjo9WsWTPTEcskLy9PO3fuVFxcnFauXKnNmzfrmmuu0aBBgxQTE6NevXqpfPnypmMC8DjmPeY9OE9ubq7i4+O1YMECLV68WCdOnFCHDh0KZqjOnTs7/q4q33zzjVatWqVVq1bpk08+0Y8//qioqCjFxMTozjvv1K9+9SvTEQG4AH1KnwLFwf6K/RWciX0H+w44FgcnC7N3714tXrxYK1eu1NatWxUUFKSuXbuqR48eioyM1G9+8xvb/2RHZmamtm3bpo0bNyo+Pl4bNmxQdna2brjhBkVHR2vw4MHq2LGj6ZgAyoi+AuBW9BsAfzpx4oSWLFmiFStWaO3atTpz5oyaN2+uXr16KTIyUt26ddO1115rOmaRLMtSSkqKEhIS9Nlnn2n16tU6fvy46tWrp+joaA0YMEDR0dGqWLGi6agAUCjmPcB5cnJytHbtWn388cdatWqV0tLSVKNGDd1+++2KiopSt27d1KZNG9sf/PnPf/6jhIQEbdy4UWvWrFFycrLCwsLUvXt39evXT3fccYfq1q1rOiYAF6NPARQH+yvAmdh3AI7CwcmrOXnypD755BOtWrVKGzZs0MGDBxUSEqK2bduqa9euatu2rdq2bavWrVvrmmuuMZIxMzNTKSkpSkpKUlJSkhITE7Vjxw7l5OSoQYMGioqKUq9evRQdHa1f//rXRjIC8D/6CoBb0W8A/CknJ0ebNm3SqlWrtG7dOu3atUs5OTm69tpr1a1bN91www1q27at2rRpo3r16hnJmJeXp4MHDyopKUnJycnatm2bNm3apFOnTumaa65R586d1bNnT/Xu3Zuf7gXgSMx7gDOlpqYW3FUsISFBp0+fVpUqVdS5c2d16tSpYIZq3ry5sV/J+v333xfMUJ9//rkSEhK0f/9+hYSEqHXr1urRo4eio6MVFRXFN+wBGEOfArga9leAM7HvAGyPg5MldezYMSUkJCghIUFbtmzRnj17dPbsWQUFBalJkyaKiIhQ48aN1aRJEzVu3FiNGjVSnTp1FB4errCwsFK9z/Pnz+vEiRM6fvy4Dh8+rLS0NB06dEhpaWlKTU3VgQMHlJubq0qVKqlVq1b6zW9+o65du6pbt25q2LChj58BAE5BXwFwK/oNgD+dO3dO27Zt02effabNmzdr9+7d+vbbbyVJ4eHhatWqlZo0aVLwX+PGjVW3bl3Vrl1b1atXL9X7zM3N1YkTJ3TixAkdOXKkoF8OHTqkAwcOKCUlRRkZGQoODlbTpk3VoUOHgo5p27atsW+cAYC/MO8BzpOXl6eUlBRt3LhRmzZt0o4dO/TVV1/pwoULqlChglq2bKlmzZoVXLdNmjRRw4YNFR4erlq1apV6njlz5ozS09N17Nixgms2//pNSUnRd999J0mqXbu22rdvry5duqhr167q0qWLqlat6sunAAB8gj4FUBzsrwBnYt8B2A4HJ8vKsiylpaUpKSlJe/bs0VdffVVQNEePHlVeXl7BYytVqlTwhUtISIgqVKjwi3I7f/68MjMzlZeXVzB4ZGRkXPKYunXrFhTlddddpzZt2qht27Zq1qyZ7W/bD8Ac+gqAW9FvAPzt5MmT2r17t5KTk/XFF19cshjOysoqeFxoaKjCw8MVHh6uSpUqKTg4WNWqVbvkbeXl5emHH36QJJ09e7agZ36uatWqBQuypk2bqmXLlmrXrp1atWpV6gUZADgZ8x7gTFlZWdq7d6+Sk5O1d+9eHThwoGCOOnXq1CWPrVatmmrXrl0wO1WpUuUX31w/e/asLly4oKysLJ04cULp6enKyckpeH358uXVsGHDgjnq+uuvL7iDC3dGAeBk9CmA4mB/BTgP+w7AOA5O+lN2draOHDmi48ePF5RS/n+5ubnKysrSuXPnLvk7FStWVKVKlRQUFKRatWoVDC35/zVq1Ihb3APwOfoKgFvRbwD8ybIsHTt2TP/5z3/0/fffX9IxmZmZsixLp0+fvuTv/HwZXaVKFYWHh6t27doFHVOvXj2Fh4eb+OcAgCMx7wHO9MMPP+ibb74puF7T02q1ByYAAACLSURBVNOVnp5+yTfoL1y4cMnfqVq1asE3CH9+zdaqVUt16tRRvXr1FBwcbOKfAwDG0KcArob9FeBM7DuAgODgJAAAAAAAAAAAAAAAAAAA8IxEflwIAAAAAAAAAAAAAAAAAAB4BgcnAQAAAAAAAAAAAAAAAACAZ3BwEgAAAAAAAAAAAAAAAAAAeMb/B4Nz7/LXqnWoAAAAAElFTkSuQmCC\n", "text/plain": [""]}, "execution_count": 34, "metadata": {}, "output_type": "execute_result"}], "source": ["full_df.visualize()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Below we execute all the column merge and the column drop operations that we have queued so far. We store the resulting `DataFrame` in a new variable."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["if 'hurricanes_merged' not in os.listdir(data_dir):\n", " os.mkdir(os.path.join(data_dir,'hurricanes_merged'))\n", "\n", "merged_csv_path = os.path.join(data_dir, 'hurricanes_merged')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Please note that the merge operation below might take about one hour. If you do not want to run the cell below, preprocessed data has been provided in sample data."]}, {"cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["CPU times: user 43min 46s, sys: 6min 3s, total: 49min 49s\n", "Wall time: 45min 45s\n"]}], "source": ["merged_df = full_df.drop(columns_to_drop, axis=1)\n", "merged_df.to_csv(os.path.join(merged_csv_path, 'hurr_dask_*.csv'))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The `save()` operation spawns several workers that compute in parallel. Notice the ouput file name contains a wildcard (`*`). This allows Dask to both read data in chunks and write outputs in chunks. The save operation will result in creating a number of processed CSV files whose names are prefixed with `hurr_dask` and suffixed with a number."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Preview results\n", "Once Dask realizes a delayed computation, it returns the result as an in-memory Pandas DataFrame object. Thus, the `merged_df` variable represents a Pandas `DataFrame` object with `348,703` records and `17` columns."]}, {"cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [{"data": {"text/plain": ["(348703, 17)"]}, "execution_count": 65, "metadata": {}, "output_type": "execute_result"}], "source": ["merged_df.shape"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Creating hurricane tracks using Geoanalytics\n", "\n", "The data collected so far are a set of Point observations representing all hurricanes recorded in the last `169` years. To make sense of these points, we need to connect them together to create a track for each hurricane. This part of the notebook uses ArcGIS GeoAnalytics server to reconstruct such hurricane tracks. The GeoAnalytics server is capable of processing on massive datasets in a scalable and distributed fashion.\n", "\n", "The DASK process merged redundant columns together and ouput a folder full of CSV files. The GeoAnalytics server is also capabale of accepting a folder of datasets as 1 dataset and working on them. Thus in this part, we register that folder as a **datastore** and on the GeoAnalytics server for processing.\n", "\n", "**Reconstruct tracks**:\n", "Reconstruct tracks is a type of data aggregation tool available in the `arcgis.geoanalytics` module. This tool works with a layer of point features or polygon features that are time enabled. It first determines which points belong to a track using an identification number or identification string. Using the time at each location, the tracks are ordered sequentially and transformed into a line representing the path of movement. The map below shows a subset of the point datasets."]}, {"cell_type": "markdown", "metadata": {}, "source": [""]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Create a data store\n", "For the GeoAnalytics server to process your big data, it needs the data to be registered as a data store. In our case, the data is in multiple CSV files and we will register the folder containing the files as a data store of type `bigDataFileShare`.\n", "\n", "Let us connect to our Organization."]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": ["gis = GIS(url='https://pythonapi.playground.esri.com/portal', username='arcgis_python', password='amazing_arcgis_123')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Get the geoanalytics datastores and search for the registered datasets:"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"data": {"text/plain": ["[,\n", " ,\n", " ,\n", " ,\n", " ]"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["# Query the data stores available\n", "datastores = get_datastores()\n", "bigdata_fileshares = datastores.search()\n", "bigdata_fileshares"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The dataset `hurricanes_dask_csv` data is registered as a big data file share with the Geoanalytics datastore, so we can reference it:"]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": ["data_item = bigdata_fileshares[1]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["If there is no big data file share for hurricane track data registered on the server, we can register one that points to the shared folder containing the CSV files."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["data_item = datastores.add_bigdata(\"Hurricane_tracks\", r\"\\\\path_to_hurricane_data\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Once a big data file share is registered, the GeoAnalytics server processes all the valid file types to discern the schema of the data, including information about the geometry in a dataset. If the dataset is time-enabled, as is required to use some GeoAnalytics Tools, the manifest reports the necessary metadata about how time information is stored as well.\n", "\n", "This process can take a few minutes depending on the size of your data. Once processed, querying the manifest property will return a schema. As you can see from below, the schema contains the columns we merged using DASK previously."]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"text/plain": ["1"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["datasets = data_item.manifest['datasets']\n", "len(datasets)"]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [{"data": {"text/plain": ["['hurricanes_merged']"]}, "execution_count": 23, "metadata": {}, "output_type": "execute_result"}], "source": ["[dataset['name'] for dataset in datasets]"]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [{"data": {"text/plain": ["{'name': 'hurricanes_merged',\n", " 'format': {'quoteChar': '\"',\n", " 'fieldDelimiter': ',',\n", " 'hasHeaderRow': True,\n", " 'encoding': 'UTF-8',\n", " 'escapeChar': '\"',\n", " 'recordTerminator': '\\n',\n", " 'type': 'delimited',\n", " 'extension': 'csv'},\n", " 'schema': {'fields': [{'name': 'col_1', 'type': 'esriFieldTypeBigInteger'},\n", " {'name': 'Serial_Num', 'type': 'esriFieldTypeString'},\n", " {'name': 'Season', 'type': 'esriFieldTypeBigInteger'},\n", " {'name': 'Num', 'type': 'esriFieldTypeBigInteger'},\n", " {'name': 'Basin', 'type': 'esriFieldTypeString'},\n", " {'name': 'Sub_basin', 'type': 'esriFieldTypeString'},\n", " {'name': 'Name', 'type': 'esriFieldTypeString'},\n", " {'name': 'ISO_time', 'type': 'esriFieldTypeString'},\n", " {'name': 'Nature', 'type': 'esriFieldTypeString'},\n", " {'name': 'Center', 'type': 'esriFieldTypeString'},\n", " {'name': 'Track_type', 'type': 'esriFieldTypeString'},\n", " {'name': 'Current Basin', 'type': 'esriFieldTypeString'},\n", " {'name': 'latitude_merged', 'type': 'esriFieldTypeDouble'},\n", " {'name': 'longitude_merged', 'type': 'esriFieldTypeDouble'},\n", " {'name': 'wind_merged', 'type': 'esriFieldTypeDouble'},\n", " {'name': 'pressure_merged', 'type': 'esriFieldTypeDouble'},\n", " {'name': 'grade_merged', 'type': 'esriFieldTypeDouble'},\n", " {'name': 'eye_dia_merged', 'type': 'esriFieldTypeDouble'}]},\n", " 'geometry': {'geometryType': 'esriGeometryPoint',\n", " 'spatialReference': {'wkid': 4326},\n", " 'fields': [{'name': 'longitude_merged', 'formats': ['x']},\n", " {'name': 'latitude_merged', 'formats': ['y']}]},\n", " 'time': {'timeType': 'instant',\n", " 'fields': [{'name': 'ISO_time', 'formats': ['yyyy-MM-dd HH:mm:ss']}],\n", " 'timeReference': {'timeZone': 'UTC'}}}"]}, "execution_count": 24, "metadata": {}, "output_type": "execute_result"}], "source": ["datasets[0]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Perform data aggregation using reconstruct tracks tool\n", "\n", "When you add a big data file share, a corresponding item gets created in your GIS. You can search for it like a regular item and query its layers."]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [{"data": {"text/plain": ["[]"]}, "execution_count": 25, "metadata": {}, "output_type": "execute_result"}], "source": ["search_result = gis.content.search(\"bigDataFileShares_hurricanes_dask_csv\", item_type = \"big data file share\")\n", "search_result"]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [{"data": {"text/plain": [""]}, "execution_count": 26, "metadata": {}, "output_type": "execute_result"}], "source": ["data_item = search_result[0]\n", "cleaned_csv = data_item.layers[0]\n", "cleaned_csv"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Execute reconstruct tracks tool\n", "\n", "The `reconstruct_tracks()` function is available in the `arcgis.geoanalytics.summarize_data` module. In this example, we are using this tool to aggregate the numerous points into line segments showing the tracks followed by the hurricanes. The tool creates a feature layer item as an output which can be accessed once the processing is complete."]}, {"cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Submitted.\n", "Executing...\n", "Executing (ReconstructTracks): ReconstructTracks \"Feature Set\" Serial_Num Geodesic # # # # # # \"{\"serviceProperties\": {\"name\": \"hurricane_tracks_aggregated_ga\", \"serviceUrl\": \"https://datascience-arcpy.esri.com/server/rest/services/Hosted/hurricane_tracks_aggregated_ga/FeatureServer\"}, \"itemProperties\": {\"itemId\": \"545adb07c4ba4da7b72ddbe47bd275d2\"}}\" \"{\"defaultAggregationStyles\": false}\"\n", "Start Time: Thu Nov 15 21:57:30 2018\n", "Using URL based GPRecordSet param: https://datascience-arcpy.esri.com/server/rest/services/DataStoreCatalogs/bigDataFileShares_hurricanes_dask_csv/BigDataCatalogServer/hurricanes_merged\n", "{\"messageCode\":\"BD_101028\",\"message\":\"Starting new distributed job with 352 tasks.\",\"params\":{\"totalTasks\":\"352\"}}\n", "{\"messageCode\":\"BD_101029\",\"message\":\"0/352 distributed tasks completed.\",\"params\":{\"completedTasks\":\"0\",\"totalTasks\":\"352\"}}\n", "{\"messageCode\":\"BD_101029\",\"message\":\"177/352 distributed tasks completed.\",\"params\":{\"completedTasks\":\"177\",\"totalTasks\":\"352\"}}\n", "{\"messageCode\":\"BD_101029\",\"message\":\"241/352 distributed tasks completed.\",\"params\":{\"completedTasks\":\"241\",\"totalTasks\":\"352\"}}\n", "{\"messageCode\":\"BD_101029\",\"message\":\"308/352 distributed tasks completed.\",\"params\":{\"completedTasks\":\"308\",\"totalTasks\":\"352\"}}\n", "{\"messageCode\":\"BD_101029\",\"message\":\"352/352 distributed tasks completed.\",\"params\":{\"completedTasks\":\"352\",\"totalTasks\":\"352\"}}\n", "{\"messageCode\":\"BD_101081\",\"message\":\"Finished writing results:\"}\n", "{\"messageCode\":\"BD_101082\",\"message\":\"* Count of features = 12757\",\"params\":{\"resultCount\":\"12757\"}}\n", "{\"messageCode\":\"BD_101083\",\"message\":\"* Spatial extent = {\\\"xmin\\\":-180,\\\"ymin\\\":-68.5,\\\"xmax\\\":180,\\\"ymax\\\":81}\",\"params\":{\"extent\":\"{\\\"xmin\\\":-180,\\\"ymin\\\":-68.5,\\\"xmax\\\":180,\\\"ymax\\\":81}\"}}\n", "{\"messageCode\":\"BD_101084\",\"message\":\"* Temporal extent = Interval(MutableInstant(1842-10-25 06:00:00.000),MutableInstant(2017-06-13 06:00:00.000))\",\"params\":{\"extent\":\"Interval(MutableInstant(1842-10-25 06:00:00.000),MutableInstant(2017-06-13 06:00:00.000))\"}}\n"]}, {"name": "stderr", "output_type": "stream", "text": ["{\"messageCode\":\"BD_101054\",\"message\":\"Some records have either missing or invalid geometries.\"}\n"]}, {"name": "stdout", "output_type": "stream", "text": ["{\"messageCode\":\"BD_101054\",\"message\":\"Some records have either missing or invalid geometries.\"}\n", "Succeeded at Thu Nov 15 21:57:54 2018 (Elapsed Time: 24.21 seconds)\n"]}], "source": ["agg_result = reconstruct_tracks(cleaned_csv, \n", " track_fields='Serial_Num', # the Hurricane id number\n", " method='GEODESIC', output_name='hurricane_tracks_aggregated_ga')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Analyze the result of aggregation\n", "The reconstruct tracks produces summary statistics such as `MIN`, `MAX`, `MEAN`, `MEDIAN`, `RANGE`, `SD`, `VAR`, `SUM`, for numeric columns and `COUNT` for ordinal columns during the aggregation process. Let us list the fields in this dataset to view them."]}, {"cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["['Serial_Num', 'COUNT', 'COUNT_col_1', 'SUM_col_1', 'MIN_col_1', 'MAX_col_1',\n", " 'MEAN_col_1', 'RANGE_col_1', 'SD_col_1', 'VAR_col_1', 'COUNT_Season',\n", " 'SUM_Season', 'MIN_Season', 'MAX_Season', 'MEAN_Season', 'RANGE_Season',\n", " 'SD_Season', 'VAR_Season', 'COUNT_Num', 'SUM_Num', 'MIN_Num', 'MAX_Num',\n", " 'MEAN_Num', 'RANGE_Num', 'SD_Num', 'VAR_Num', 'COUNT_Basin', 'ANY_Basin',\n", " 'COUNT_Sub_basin', 'ANY_Sub_basin', 'COUNT_Name', 'ANY_Name', 'COUNT_ISO_time',\n", " 'ANY_ISO_time', 'COUNT_Nature', 'ANY_Nature', 'COUNT_Center', 'ANY_Center',\n", " 'COUNT_Track_type', 'ANY_Track_type', 'COUNT_Current_Basin',\n", " 'ANY_Current_Basin', 'COUNT_latitude_merged', 'SUM_latitude_merged',\n", " 'MIN_latitude_merged', 'MAX_latitude_merged', 'MEAN_latitude_merged',\n", " 'RANGE_latitude_merged', 'SD_latitude_merged', 'VAR_latitude_merged',\n", " 'COUNT_longitude_merged', 'SUM_longitude_merged', 'MIN_longitude_merged',\n", " 'MAX_longitude_merged', 'MEAN_longitude_merged', 'RANGE_longitude_merged',\n", " 'SD_longitude_merged', 'VAR_longitude_merged', 'COUNT_wind_merged',\n", " 'SUM_wind_merged', 'MIN_wind_merged', 'MAX_wind_merged', 'MEAN_wind_merged',\n", " 'RANGE_wind_merged', 'SD_wind_merged', 'VAR_wind_merged',\n", " 'COUNT_pressure_merged', 'SUM_pressure_merged', 'MIN_pressure_merged',\n", " 'MAX_pressure_merged', 'MEAN_pressure_merged', 'RANGE_pressure_merged',\n", " 'SD_pressure_merged', 'VAR_pressure_merged', 'COUNT_grade_merged',\n", " 'SUM_grade_merged', 'MIN_grade_merged', 'MAX_grade_merged',\n", " 'MEAN_grade_merged', 'RANGE_grade_merged', 'SD_grade_merged',\n", " 'VAR_grade_merged', 'COUNT_eye_dia_merged', 'SUM_eye_dia_merged',\n", " 'MIN_eye_dia_merged', 'MAX_eye_dia_merged', 'MEAN_eye_dia_merged',\n", " 'RANGE_eye_dia_merged', 'SD_eye_dia_merged', 'VAR_eye_dia_merged',\n", " 'TRACK_DURATION', 'globalid', 'OBJECTID', 'END_DATETIME', 'START_DATETIME']\n"]}], "source": ["agg_tracks_layer = agg_result.layers[0]\n", "agg_fields = [f.name for f in agg_tracks_layer.properties.fields]\n", "pprint(agg_fields, compact=True)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["To get the number of hurricanes the reconstruct tracks tool identified, we run a query on the aggregation layer and get just the number of records."]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/plain": ["12362"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["agg_tracks_layer.query(return_count_only=True)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["# Conclusion\n", "In this notebook, we observed how to download meteorological data over FTP from NEIC website. The data came in `169` CSV files for the past `169` years. We sanitized it initially using Pandas to remove bad header rows. We then used DASK library to read all `169` files as a single file and merged data from redundant columns. This pre-processing resulted in multiple output CSV files covering a total of 348k records and 17 columns.\n", "\n", "This data was fed to the ArcGIS GeoAnalytics server for aggregation. The reconstruct tracks tool on GeoAnalytics server reduced this point dataset into hurricane tracks (lines) and during this aggregation, it calculated summary statistics for the numerical columns. The tool identified **`12,362`** individual hurricanes from the past `169` years.\n", "\n", "In Part 2 of this study, we will visualize and explore this dataset to understand the prevelance, duration of hurricanes and the communities affected by hurricanes worldwide.\n", "\n", "In Part 3, we will analyze this aggregated result comprehensively, answer important questions such as, does the intensity of hurricanes increase over time and draw conclusions."]}], "metadata": {"anaconda-cloud": {}, "esriNotebookRuntime": {"notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "4.0"}, "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2"}, "toc": {"base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "**Table of Contents**", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": true}}, "nbformat": 4, "nbformat_minor": 2} \ No newline at end of file diff --git a/samples/04_gis_analysts_data_scientists/wildfire_analysis_using_sentinel-2_imagery.ipynb b/samples/04_gis_analysts_data_scientists/wildfire_analysis_using_sentinel-2_imagery.ipynb deleted file mode 100644 index db4a196ea3..0000000000 --- a/samples/04_gis_analysts_data_scientists/wildfire_analysis_using_sentinel-2_imagery.ipynb +++ /dev/null @@ -1,858 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pawnee Fire Analysis\n", - "\n", - "> * 🔬 Data Science\n", - "* 🖥️ Requires RasterAnalytics Portal Configuration\n", - "* 🖥️ Requires GeoEnrichment Portal Configuration\n", - "* 🖥️ Requires GeoAnalytics Portal Configuration\n", - "\n", - "The Pawnee Fire was a large wildfire that burned in Lake County, California. The fire started on June 23, 2018 and burned a total of 15,185 acres (61 km2) before it was fully contained on July 8, 2018.\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Table of Contents

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remote Sensing using Sentinel-2 data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "import warnings\n", - "\n", - "from IPython.display import HTML\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "import arcgis\n", - "from arcgis import GIS\n", - "from arcgis.raster.functions import *\n", - "from arcgis.geoanalytics.use_proximity import create_buffers\n", - "from arcgis.geoenrichment import enrich\n", - "from arcgis.features import SpatialDataFrame\n", - "from arcgis.raster.analytics import create_image_collection\n", - "from arcgis.raster.analytics import list_datastore_content\n", - "\n", - "gis= GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this analysis, we will be using Sentinel-2 data.\n", - "\n", - "Sentinel-2 is an Earth observation mission developed by ESA as part of the Copernicus Programme to perform terrestrial observations in support of services such as forest monitoring, land cover change detection, and natural disaster management.\n", - "\n", - "In this analysis data downloaded from https://earthexplorer.usgs.gov/ is used for creating hosted image service. \n", - "We add the data to the datastore and we then run the create_image_collection function which creates a collection with the input_rasters specified and publishes the collection as an image service. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use list_datasore_content() in order to see the contents in the rasterstore." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/rasterStores/LocalRasterStore/Hosted_GeneratedRasterProduct_AANS52.crf/',\n", - " '/rasterStores/LocalRasterStore/L1C_T10SEJ_A015697_20180624T190108.zip',\n", - " '/rasterStores/LocalRasterStore/me.txt',\n", - " '/rasterStores/LocalRasterStore/m_7013bfcd7273e0a4a779fce061167d5c/',\n", - " '/rasterStores/LocalRasterStore/m_b5f745ad6ef601d5e6adf104c8b4ef70/',\n", - " '/rasterStores/LocalRasterStore/m_d0221b176ab5ed2398828b8079d62ef8/',\n", - " '/rasterStores/LocalRasterStore/pawnee_fire_multispectral/',\n", - " '/rasterStores/LocalRasterStore/pool_chips_1/',\n", - " '/rasterStores/LocalRasterStore/pool_chips_2/',\n", - " '/rasterStores/LocalRasterStore/S2A_MSIL1C_20180624T184921_N0206_R113_T10SEJ_20180624T234856.SAFE/',\n", - " '/rasterStores/LocalRasterStore/S2B_MSIL1C_20180622T185919_N0206_R013_T10SEJ_20180622T205930.SAFE/',\n", - " '/rasterStores/LocalRasterStore/sentinel_data/']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_datastore_content(\"/rasterStores/LocalRasterStore\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "run_control": { - "marked": false - } - }, - "outputs": [], - "source": [ - "sentinel_collection = create_image_collection(image_collection=\"pawnee_fire_multispectral\",\n", - " input_rasters=[\"/rasterStores/LocalRasterStore/S2A_MSIL1C_20180624T184921_N0206_R113_T10SEJ_20180624T234856.SAFE\",\n", - " \"/rasterStores/LocalRasterStore/S2B_MSIL1C_20180622T185919_N0206_R013_T10SEJ_20180622T205930.SAFE\"],\n", - " raster_type_name=\"Sentinel-2\", \n", - " raster_type_params={\"productType\":\"All\",\"processingTemplate\":\"Multispectral\"},\n", - " context={\"image_collection_properties\":{\"imageCollectionType\":\"Satellite\"},\"byref\":True}, gis = gis)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "sentinel = sentinel_collection.layers[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Select before and after rasters" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "aoi = {'spatialReference': {'latestWkid': 3857, 'wkid': 102100},\n", - " 'xmax': -13643017.100720055,\n", - " 'xmin': -13652113.10708598,\n", - " 'ymax': 4739654.477447927,\n", - " 'ymin': 4731284.622850712}\n", - "arcgis.env.analysis_extent = aoi\n", - "sentinel.extent = aoi" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AcquisitionDateCategoryCenterXCenterYCloudCoverCreationTimeCreatorGroupNameHighPSLowPS...SOrderSensingOrbitSensorNameShape_AreaShape_LengthStereoIDTagThumbnailZOrderSHAPE
02018-06-24 18:49:21.0241-1.362127e+074.758219e+0601548778472000rjacksonMTD_MSIL1C6060...None113Sentinel-2A2.008228e+10562920.108277NoneMSNoneNone{'rings': [[[-13549665.930399999, 4828694.125]...
12018-06-22 18:59:19.0241-1.364188e+074.762904e+0601548778472000rjacksonMTD_MSIL1C6060...None13Sentinel-2B1.417943e+10488606.729731NoneMSNoneNone{'rings': [[[-13611168.835900001, 4689604.0966...
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2 rows × 26 columns

\n", - "
" - ], - "text/plain": [ - " AcquisitionDate Category CenterX CenterY CloudCover \\\n", - "0 2018-06-24 18:49:21.024 1 -1.362127e+07 4.758219e+06 0 \n", - "1 2018-06-22 18:59:19.024 1 -1.364188e+07 4.762904e+06 0 \n", - "\n", - " CreationTime Creator GroupName HighPS LowPS \\\n", - "0 1548778472000 rjackson MTD_MSIL1C 60 60 \n", - "1 1548778472000 rjackson MTD_MSIL1C 60 60 \n", - "\n", - " ... SOrder SensingOrbit \\\n", - "0 ... None 113 \n", - "1 ... None 13 \n", - "\n", - " SensorName Shape_Area Shape_Length StereoID Tag Thumbnail ZOrder \\\n", - "0 Sentinel-2A 2.008228e+10 562920.108277 None MS None None \n", - "1 Sentinel-2B 1.417943e+10 488606.729731 None MS None None \n", - "\n", - " SHAPE \n", - "0 {'rings': [[[-13549665.930399999, 4828694.125]... \n", - "1 {'rings': [[[-13611168.835900001, 4689604.0966... \n", - "\n", - "[2 rows x 26 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "selected = sentinel.filter_by(where=\"acquisitiondate BETWEEN timestamp '2018-06-15 00:00:00' AND timestamp '2018-06-24 19:59:59'\",\n", - " geometry=arcgis.geometry.filters.intersects(aoi))\n", - "\n", - "df = selected.query(out_fields=\"*\", order_by_fields=\"OBJECTID ASC\").df\n", - "df['AcquisitionDate'] = pd.to_datetime(df['AcquisitionDate'], unit='ms')\n", - "df.tail(40)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "prefire = sentinel.filter_by('OBJECTID=2') \n", - "midfire = sentinel.filter_by('OBJECTID=1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visual Assessment" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "truecolor = extract_band(midfire, [4,3,2])\n", - "truecolor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize Burn Scars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We extract the [13, 12, 4] bands to improve visibility of fire and burn scars. This band combination pushes further into the SWIR range of the electromagnetic spectrum, where there is less susceptibility to smoke and haze generated by a burning fire." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "extract_band(midfire, [13,12,4])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For comparison, the same area before the fire started shows no burn scar." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "extract_band(prefire, [13,12,4])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quantitative Assessment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The **Normalized Burn Ratio (NBR)** can be used to delineate the burnt areas and identify the severity of the fire. \n", - "\n", - "The formula for the NBR is very similar to that of NDVI except that it uses near-infrared band 9 and the short-wave infrared band 13:\n", - "\\begin{align}\n", - "{\\mathbf{NBR}} = \\frac{\\mathbf{B9} - \\mathbf{B13}}{\\mathbf{B9} + \\mathbf{B13} + \\mathbf{WS}} \\\\ \n", - "\\end{align}\n", - "\n", - "The NBR equation was designed to be calcualted from reflectance, but it can be calculated from radiance and digital_number_(dn) with changes to the burn severity table below. The WS parameter is used for water suppression, and is typically 2000. \n", - "\n", - "For a given area, NBR is calculated from an image just prior to the burn and a second NBR is calculated for an image immediately following the burn. Burn extent and severity is judged by taking the difference between these two index layers:\n", - "\n", - "\\begin{align}\n", - "{\\Delta \\mathbf{NBR}} = \\mathbf{NBR_{prefire}} - \\mathbf{NBR_{postfire}} \\\\ \n", - "\\end{align}\n", - "\n", - "The meaning of the ∆NBR values can vary by scene, and interpretation in specific instances should always be based on some field assessment. However, the following table from the USGS FireMon program can be useful as a first approximation for interpreting the NBR difference:\n", - "\n", - "\n", - "| \\begin{align}{\\Delta \\mathbf{NBR}} \\end{align} | Burn Severity |\n", - "| ------------- |:-------------:|\n", - "| 0.1 to 0.27 | Low severity burn |\n", - "| 0.27 to 0.44 | Medium severity burn |\n", - "| 0.44 to 0.66 | Moderate severity burn |\n", - "| > 0.66 | High severity burn |\n", - "\n", - "[Source: http://wiki.landscapetoolbox.org/doku.php/remote_sensing_methods:normalized_burn_ratio]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use Band Arithmetic and Map Algebra " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to perform raster analysis on raw pixel value, we filter out the scenes from the sentinel image service again and create new layers" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "nbr_prefire = band_arithmetic(prefire, \"(b9 - b13) / (b9 + b13 + 2000)\")\n", - "nbr_postfire = band_arithmetic(midfire, \"(b9 - b13) / (b9 + b13 + 2000)\")\n", - "\n", - "nbr_diff = nbr_prefire - nbr_postfire" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "burnt_areas = colormap(remap(nbr_diff, \n", - " input_ranges=[0.1, 0.27, # low severity \n", - " 0.27, 0.44, # medium severity\n", - " 0.44, 0.66, # moderate severity\n", - " 0.66, 1.00], # high severity burn\n", - " output_values=[1, 2, 3, 4], \n", - " no_data_ranges=[-1, 0.1], astype='u8'), \n", - " colormap=[[4, 0xFF, 0xC3, 0], [3, 0xFA, 0x8E, 0], [2, 0xF2, 0x55, 0], [1, 0xE6, 0, 0]])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Visualize burnt areas\n", - "burnt_areas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With this, we have computed the NBR on scenes from before and after the burn, and computed the NBR difference to identify places that have been affected by the fire. We've also normalized the values to match a burn severity index, and applied a color map that brings out the extent of fire damage.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Area calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "pixx = (aoi['xmax'] - aoi['xmin']) / 1200.0\n", - "pixy = (aoi['ymax'] - aoi['ymin']) / 450.0\n", - "\n", - "res = burnt_areas.compute_histograms(aoi, pixel_size={'x':pixx, 'y':pixy})\n", - "\n", - "numpix = 0\n", - "histogram = res['histograms'][0]['counts'][1:]\n", - "for i in histogram:\n", - " numpix += i" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Report burnt area" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Fire has consumed 3,569 acres till 2018-06-22

." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sqmarea = numpix * pixx * pixy # in sq. m\n", - "acres = 0.00024711 * sqmarea # in acres\n", - "\n", - "HTML('

Fire has consumed {:,} acres till {}

.' \\\n", - " .format(int(acres), df.iloc[-1]['AcquisitionDate'].date()))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "plt.title('Distribution by severity', y=-0.1)\n", - "plt.pie(histogram, labels=['Low Severity', 'Medium Severity', 'Moderate Severity', 'High Severity']);\n", - "plt.axis('equal');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize burnt areas" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "firemap = gis.map()\n", - "firemap.extent = aoi\n", - "firemap.add_layer([truecolor, burnt_areas])\n", - "\n", - "firemap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Persist the burnt areas layer in the GIS" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If required, using the save(), we can persist the output in the gis as a new layer. This uses distributed raster analysis to perform the analysis at the source resolution." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "burnt_areas = burnt_areas.save()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Raster to Feature layer conversion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use Raster Analytics and Geoanalytics to convert the burnt area raster to a feature layer. The `to_features()` method converts the raster to a feature layer and `create_buffers()` fills holes in the features and dissolves them to output one feature that covers the extent of the Pawnee Fire." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "burnt_areas = burnt_areas.layers[0]\n", - "fire_item = burnt_areas.to_features(output_name='Pawnee_Fire_Feature_Layer', gis=gis)\n", - "fire_layer = fire_item.layers[0]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fire = create_buffers(fire_layer, 100, 'Meters', dissolve_option='All', multipart=True, output_name='PawneeFireArea_Buffer')\n", - "fire = fire.layers[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Feature Layer" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vectormap = gis.map()\n", - "vectormap.basemap = 'dark-gray'\n", - "vectormap.extent = aoi\n", - "\n", - "vectormap.add_layer(fire)\n", - "vectormap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Impact Assessment" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "### Assess Human Impact" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "hidden": true - }, - "outputs": [], - "source": [ - "from arcgis import geometry \n", - " \n", - "sdf = SpatialDataFrame.from_layer(fire)\n", - "\n", - "fire_geometry = sdf.iloc[0].SHAPE\n", - "sa_filter = geometry.filters.intersects(geometry=fire_geometry, sr=4326)\n", - "\n", - "def age_pyramid(df):\n", - " %matplotlib inline\n", - " warnings.simplefilter(action='ignore', category=FutureWarning)\n", - " pd.options.mode.chained_assignment = None \n", - " plt.style.use('ggplot')\n", - "\n", - " df = df[[x for x in impacted_people.columns if 'MALE' in x or 'FEM' in x]]\n", - " sf = pd.DataFrame(df.sum())\n", - " age = sf.index.str.extract('(\\d+)').astype('int64')\n", - " f = sf[sf.index.str.startswith('FEM')]\n", - " m = sf[sf.index.str.startswith('MALE')]\n", - " sf = sf.reset_index(drop = True)\n", - " f = f.reset_index(drop = True)\n", - " m = m.reset_index(drop = True)\n", - " sf['age'] = age\n", - " f[\"age\"] = age\n", - " m[\"age\"] = age\n", - " f = f.sort_values(by='age', ascending=False).set_index('age')\n", - " m = m.sort_values(by='age', ascending=False).set_index('age')\n", - " \n", - "\n", - " popdf = pd.concat([f, m], axis=1)\n", - " popdf.columns = ['F', 'M']\n", - " popdf['agelabel'] = popdf.index.map(str) + ' - ' + (popdf.index+4).map(str)\n", - " popdf.M = -popdf.M\n", - " \n", - " sns.barplot(x=\"F\", y=\"agelabel\", color=\"#CC6699\", label=\"Female\", data=popdf, edgecolor='none')\n", - " sns.barplot(x=\"M\", y=\"agelabel\", color=\"#008AB8\", label=\"Male\", data=popdf, edgecolor='none')\n", - " plt.ylabel('Age group')\n", - " plt.xlabel('Number of people');\n", - " return plt;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Age Pyramid of Affected Population" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "impacted_people = enrich(sdf, 'Age')\n", - "age_pyramid(impacted_people);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conclusion\n", - "\n", - "In this notebook example, we used Sentinel-2 data in order to perform remote sensing. For this we filtered out pre and post fire scenes. Using extract_band() we carried out visual assessment of the burnt area. We then computed the NBR on these scenes and computed the NBR difference to identify places that have been affected by the fire, using raster functions. We also normalized the values to match the burn severity index, applied a color map raster function that brings out the extent of fire damage and calculated the burnt area. Finally, we carried out a human impact assessment by plotting the age pyramid of affected population " - ] - } - ], - "metadata": { - "esriNotebookRuntime": { - "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", - "notebookRuntimeVersion": "5.0" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}