OSM Analysis - Cafes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OSM data analysis of Cafes\n",
    "\n",
    "This python notebook analyses cafe data within Kathmandu. First, OSM data within a given geojson bounds is extracted using the `osmnx` package. After this, by making requests to the Overpass API, we look at the latest date at which each entity is modified. Finally, after joining these two datasets, we generate summary statisics and time series plots to understand attribute coverage and data recency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import requests\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import osmnx as ox\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "\n",
    "boundary_file = \"kathmandu.geojson\"\n",
    "tags = {\"amenity\": \"cafe\"}\n",
    "filename = \"cafes\"\n",
    "completeness_tags = [\"name\", \"opening_hours\", \"phone\",\"email\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "kathmandu_valley = gpd.read_file(boundary_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the plot\n",
    "kathmandu_valley.plot()\n",
    "north=kathmandu_valley.bounds['maxy'].values.item()\n",
    "south=kathmandu_valley.bounds['miny'].values.item()\n",
    "east=kathmandu_valley.bounds['maxx'].values.item()\n",
    "west=kathmandu_valley.bounds['minx'].values.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 __ node/432195183 __ 2012-09-02T09:18:43Z\n",
      "1 __ node/435005609 __ 2018-09-28T08:11:50Z\n",
      "2 __ node/669269004 __ 2011-05-13T08:19:33Z\n",
      "3 __ node/669512726 __ 2014-10-19T06:18:07Z\n",
      "4 __ node/669530117 __ 2020-03-30T10:51:33Z\n",
      "5 __ node/669530248 __ 2020-03-30T10:51:33Z\n",
      "6 __ node/671207877 __ 2017-07-12T08:51:56Z\n",
      "7 __ node/675712062 __ 2018-11-26T10:27:56Z\n",
      "8 __ node/873531754 __ 2017-07-12T07:03:20Z\n",
      "9 __ node/990902922 __ 2013-10-01T10:56:57Z\n",
      "10 __ node/992508268 __ 2010-11-17T09:45:14Z\n",
      "11 __ node/993710662 __ 2020-03-30T10:51:33Z\n",
      "12 __ node/1044375800 __ 2018-08-25T10:00:46Z\n",
      "13 __ node/1096317916 __ 2012-09-02T10:48:18Z\n",
      "14 __ node/1349658492 __ 2013-04-28T14:51:13Z\n",
      "15 __ node/1349658493 __ 2019-03-27T21:26:19Z\n",
      "16 __ node/1349664587 __ 2018-01-02T19:53:19Z\n",
      "17 __ node/1349680676 __ 2014-11-15T09:16:39Z\n",
      "18 __ node/1349680730 __ 2014-08-16T04:26:02Z\n",
      "19 __ node/1349697710 __ 2011-07-04T22:07:25Z\n",
      "20 __ node/1350468459 __ 2020-03-30T10:51:33Z\n",
      "21 __ node/1389188958 __ 2018-11-29T06:18:03Z\n",
      "22 __ node/1398437051 __ 2012-12-23T07:48:22Z\n",
      "23 __ node/1399487321 __ 2012-12-23T07:48:54Z\n",
      "24 __ node/1399501739 __ 2020-10-07T04:26:27Z\n",
      "25 __ node/1399507907 __ 2018-11-27T09:31:25Z\n",
      "26 __ node/1418080904 __ 2013-05-16T03:48:02Z\n",
      "27 __ node/1497531274 __ 2020-03-30T10:51:33Z\n",
      "28 __ node/1497531288 __ 2020-03-30T10:51:33Z\n",
      "29 __ node/1498876026 __ 2020-03-30T10:51:33Z\n",
      "30 __ node/1551745102 __ 2020-03-30T10:51:33Z\n",
      "31 __ node/1769321635 __ 2014-09-16T04:35:15Z\n",
      "32 __ node/1787021172 __ 2015-05-02T03:44:58Z\n",
      "33 __ node/1787036666 __ 2012-10-04T05:44:13Z\n",
      "34 __ node/1815689369 __ 2020-03-30T10:51:33Z\n",
      "35 __ node/1819665900 __ 2012-12-07T09:39:39Z\n",
      "36 __ node/1849461233 __ 2019-09-05T03:57:06Z\n",
      "37 __ node/1849461260 __ 2012-10-03T22:14:17Z\n",
      "38 __ node/1858151606 __ 2016-08-27T05:19:33Z\n",
      "39 __ node/1875759281 __ 2020-03-30T10:51:33Z\n",
      "40 __ node/1880051587 __ 2019-07-13T18:31:03Z\n",
      "41 __ node/1891985126 __ 2017-03-30T16:31:35Z\n",
      "42 __ node/1894153478 __ 2015-07-17T06:42:54Z\n",
      "43 __ node/1898414114 __ 2012-09-04T14:53:56Z\n",
      "44 __ node/1898521001 __ 2012-09-04T15:46:32Z\n",
      "45 __ node/1904127993 __ 2020-03-30T10:51:33Z\n",
      "46 __ node/1904243455 __ 2012-09-07T13:45:00Z\n",
      "47 __ node/1904311760 __ 2015-02-22T13:31:20Z\n",
      "48 __ node/1904346816 __ 2015-02-22T13:31:20Z\n",
      "49 __ node/1904414901 __ 2020-03-30T14:43:54Z\n",
      "50 __ node/1905155371 __ 2020-03-30T14:43:54Z\n",
      "51 __ node/1905155386 __ 2020-03-30T14:43:54Z\n",
      "52 __ node/1905155387 __ 2020-03-30T14:43:54Z\n",
      "53 __ node/1905315994 __ 2012-09-08T08:35:43Z\n",
      "54 __ node/1917103306 __ 2015-07-17T06:44:24Z\n",
      "55 __ node/1918582153 __ 2015-07-17T06:44:36Z\n",
      "56 __ node/1927427331 __ 2020-03-30T14:43:54Z\n",
      "57 __ node/1927559646 __ 2017-06-12T05:12:01Z\n",
      "58 __ node/1927600087 __ 2020-03-30T14:43:54Z\n",
      "59 __ node/1937659964 __ 2020-03-30T14:43:54Z\n",
      "60 __ node/1937673105 __ 2012-09-29T08:29:08Z\n",
      "61 __ node/1937692244 __ 2012-09-29T08:48:50Z\n",
      "62 __ node/1937702064 __ 2017-06-08T10:30:45Z\n",
      "63 __ node/1937703863 __ 2019-02-07T01:28:23Z\n",
      "64 __ node/1937710492 __ 2017-06-18T07:05:51Z\n",
      "65 __ node/1937710496 __ 2017-06-21T08:56:52Z\n",
      "66 __ node/1937718428 __ 2017-06-16T08:33:44Z\n",
      "67 __ node/1940788944 __ 2020-03-30T14:43:54Z\n",
      "68 __ node/1942418343 __ 2020-03-30T14:43:54Z\n",
      "69 __ node/1945608815 __ 2012-10-04T07:08:01Z\n",
      "70 __ node/1945637495 __ 2016-08-02T06:33:52Z\n",
      "71 __ node/1947167450 __ 2017-06-30T05:53:24Z\n",
      "72 __ node/1950423187 __ 2018-10-02T17:01:52Z\n",
      "73 __ node/1960049080 __ 2020-03-30T14:43:54Z\n",
      "74 __ node/1969476951 __ 2012-10-17T08:43:33Z\n",
      "75 __ node/1989508623 __ 2015-07-17T06:43:49Z\n",
      "76 __ node/1990673886 __ 2016-07-22T10:46:05Z\n",
      "77 __ node/1990673919 __ 2015-05-25T13:51:27Z\n",
      "78 __ node/1990673920 __ 2017-06-15T10:44:21Z\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "648 __ way/194786251 __ 2012-12-06T16:03:04Z\n",
      "649 __ way/194786252 __ 2012-12-06T16:03:04Z\n",
      "650 __ way/197419891 __ 2020-03-30T10:51:33Z\n",
      "651 __ way/198208040 __ 2020-03-30T10:51:33Z\n",
      "652 __ way/203134380 __ 2013-01-27T17:11:16Z\n",
      "653 __ way/203155884 __ 2013-01-27T19:38:19Z\n",
      "654 __ way/205315152 __ 2013-02-13T08:09:06Z\n",
      "655 __ way/206055912 __ 2017-03-11T05:12:04Z\n",
      "656 __ way/206990123 __ 2018-03-03T12:25:47Z\n",
      "657 __ way/208509600 __ 2014-02-03T19:24:06Z\n",
      "658 __ way/209868954 __ 2020-03-30T10:51:33Z\n",
      "659 __ way/210179963 __ 2014-05-02T06:15:30Z\n",
      "660 __ way/210206513 __ 2015-07-17T06:39:43Z\n",
      "661 __ way/211076664 __ 2020-03-30T10:51:33Z\n",
      "662 __ way/211154108 __ 2019-09-10T07:08:38Z\n",
      "663 __ way/211750790 __ 2018-11-29T07:53:01Z\n",
      "664 __ way/211905448 __ 2018-10-05T07:58:55Z\n",
      "665 __ way/214073220 __ 2017-12-22T05:37:10Z\n",
      "666 __ way/214678173 __ 2019-11-07T08:50:34Z\n",
      "667 __ way/215562060 __ 2014-09-13T08:34:31Z\n",
      "668 __ way/219014721 __ 2013-04-26T15:28:23Z\n",
      "669 __ way/220213916 __ 2018-01-14T06:03:23Z\n",
      "670 __ way/285870298 __ 2017-06-23T07:01:16Z\n",
      "671 __ way/298909134 __ 2015-04-30T07:06:37Z\n",
      "672 __ way/303141421 __ 2014-09-13T08:16:08Z\n",
      "673 __ way/303141425 __ 2014-09-13T08:16:08Z\n",
      "674 __ way/329837102 __ 2015-02-23T10:31:32Z\n",
      "675 __ way/341336357 __ 2017-06-21T07:43:35Z\n",
      "676 __ way/341581371 __ 2018-10-07T03:31:46Z\n",
      "677 __ way/341600445 __ 2015-10-13T20:58:07Z\n",
      "678 __ way/347553422 __ 2015-05-23T10:28:48Z\n",
      "679 __ way/348491987 __ 2019-05-26T11:24:06Z\n",
      "680 __ way/373163129 __ 2015-10-01T02:48:55Z\n",
      "681 __ way/391847150 __ 2016-11-18T22:52:01Z\n",
      "682 __ way/427163245 __ 2016-06-25T06:10:30Z\n",
      "683 __ way/427163246 __ 2016-06-25T06:10:30Z\n",
      "684 __ way/435441195 __ 2016-08-03T10:17:25Z\n",
      "685 __ way/440880101 __ 2017-12-06T09:24:15Z\n",
      "686 __ way/501657270 __ 2020-03-30T10:51:33Z\n",
      "687 __ way/501762038 __ 2020-03-30T10:51:33Z\n",
      "688 __ way/501925698 __ 2018-12-12T11:43:29Z\n",
      "689 __ way/502031452 __ 2017-12-06T09:24:15Z\n",
      "690 __ way/511284101 __ 2017-07-29T13:10:46Z\n",
      "691 __ way/517508239 __ 2018-03-16T05:51:10Z\n",
      "692 __ way/550825613 __ 2018-01-04T09:19:08Z\n",
      "693 __ way/697671084 __ 2019-06-17T03:53:43Z\n",
      "694 __ way/697970100 __ 2019-06-18T09:53:55Z\n",
      "695 __ way/697979011 __ 2019-06-18T10:35:54Z\n",
      "696 __ way/819450587 __ 2020-06-25T12:35:48Z\n"
     ]
    }
   ],
   "source": [
    "osm_data=ox.geometries_from_bbox(north, south, east, west,tags)\n",
    "timestamp = pd.DataFrame({'unique_id': [], 'timestamp': []})\n",
    "\n",
    "entitylist = osm_data['unique_id'].to_list()\n",
    "for entity in entitylist:\n",
    "    index = entitylist.index(entity)\n",
    "    overpass_url = \"http://overpass-api.de/api/interpreter\"\n",
    "    feat_type = entity.split(\"/\")[0]\n",
    "    feat_id = entity.split(\"/\")[1]\n",
    "    query = f'[out:json][timeout:25];({feat_type}({feat_id}););out meta;'\n",
    "    r = requests.get(overpass_url, params={'data': query})\n",
    "    try:\n",
    "        element = r.json()['elements'][0]\n",
    "        timestamp = timestamp.append({'unique_id': entity, 'timestamp': element['timestamp']}, ignore_index=True)\n",
    "        print(str(index) +\" __ \"+ str(entity) +\" __ \"+ element['timestamp'])\n",
    "    except:\n",
    "        print(\"Exception: \" + str(entity))\n",
    "        \n",
    "del(osm_data[\"geometry\"])\n",
    "final = pd.merge(osm_data, timestamp, how='inner', on='unique_id')        \n",
    "# final.to_csv(path_or_buf=filename, sep=\",\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tag completeness\n",
    "\n",
    "In this section, the entire OSM dataset is checked for completeness based on availability of the following standard tags (key value pairs) for hotels. In addition, a completeness score is also assigned for each individual entity based on the availaibility of the following tags.   \n",
    "\n",
    "| Information     | Key           | Value             |\n",
    "|-----------------|---------------|-------------------|\n",
    "| Infrastructure  | amenity       | cafe              |\n",
    "| Name            | name          | Name of hotel     |\n",
    "| Opening hours   | opening_hours | Mo-Fr 10:00-17:00 |\n",
    "| Phone           | phone         | Ph no.            |\n",
    "| Email           | email         | email address     |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arogya/projects/c2m2/osm_analysis/env/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "completeness = pd.DataFrame({'tag': [], 'completeness_percentage': []})\n",
    "\n",
    "total_entries = len(final)\n",
    "for tag in completeness_tags:\n",
    "    if tag in final.columns:\n",
    "        nulls = final[tag].isna().sum()  \n",
    "        completeness = completeness.append({'tag': tag,'completeness_percentage': ((total_entries - nulls)/total_entries)*100}, ignore_index=True)\n",
    "    else:\n",
    "        completeness = completeness.append({'tag': tag,'completeness_percentage': 0.0 }, ignore_index=True)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "x = completeness.tag\n",
    "y = completeness.completeness_percentage\n",
    "\n",
    "plt.xticks(rotation=90) \n",
    "plt.bar(x, y, tick_label = x, \n",
    "        width = 0.6, color = [ 'green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "completeness_scores =  pd.DataFrame({'unique_id': [], 'completeness_score': []})\n",
    "for index, row in final.iterrows():\n",
    "    total_tags = len(completeness_tags)\n",
    "    count = 0\n",
    "    \n",
    "    for tag in completeness_tags:\n",
    "        if tag in final.columns:\n",
    "            if row[tag] is None:\n",
    "                count = count\n",
    "            else:\n",
    "                count = count + 1\n",
    "    completeness_scores = completeness_scores.append({'unique_id': row.unique_id, 'completeness_score': count/total_tags}, ignore_index=True)\n",
    "\n",
    "final = pd.merge(final, completeness_scores, how='inner', on='unique_id')        \n",
    "final.to_csv(path_or_buf=filename+\".csv\", sep=\",\", index=False)\n",
    "completeness.to_csv(path_or_buf=filename+\"_completeness.csv\", sep=\",\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data recency\n",
    "\n",
    "In this section, the entire OSM dataset is checked for recency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 91 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final.timestamp = pd.to_datetime(final.timestamp)\n",
    "final['YearMonth'] =final['timestamp'].apply(lambda x: '{year}-{month}'.format(year=x.year, month=x.month))\n",
    "# final.groupby('YearMonth')\n",
    "counts = final.value_counts(subset=['YearMonth']).rename_axis('unique_values').reset_index(name='counts')\n",
    "counts = counts.sort_values(by=\"unique_values\")\n",
    "counts.to_csv(path_or_buf=filename+\"_counts.csv\", sep=\",\", index=False)\n",
    "\n",
    "x = counts.unique_values\n",
    "y = counts.counts\n",
    "\n",
    "plt.xticks(rotation=90) \n",
    "plt.bar(x, y, tick_label = x, \n",
    "        width = 0.6, color = [ 'green'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 11 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final.timestamp = pd.to_datetime(final.timestamp)\n",
    "final['Year'] = final['timestamp'].apply(lambda x: '{year}'.format(year=x.year))\n",
    "# final.groupby('YearMonth')\n",
    "\n",
    "year_counts = final.value_counts(subset=['Year']).rename_axis('unique_values').reset_index(name='counts')\n",
    "year_counts = year_counts.sort_values(by=\"unique_values\")\n",
    "\n",
    "\n",
    "x = year_counts.unique_values\n",
    "y = year_counts.counts\n",
    "\n",
    "\n",
    "plt.xticks(rotation=90) \n",
    "plt.bar(x, y, tick_label = x, \n",
    "        width = 0.6, color = [ 'green'])"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}