OSM Analysis - Hotels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OSM data analysis of Hotels\n",
    "\n",
    "This python notebook analyses hotel 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": 10,
   "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 = {\"tourism\": \"hotel\"}\n",
    "filename = \"hotels\"\n",
    "completeness_tags = [\"name\", \"stars\", \"opening_hours\", \"phone\",\"email\", \"beds\", \"rooms\"]"
   ]
  },
  {
   "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/316984536 __ 2011-11-14T05:05:45Z\n",
      "1 __ node/317040754 __ 2017-06-04T02:52:48Z\n",
      "2 __ node/339102258 __ 2009-03-01T11:31:41Z\n",
      "3 __ node/354456043 __ 2020-07-17T11:30:44Z\n",
      "4 __ node/553319545 __ 2009-11-03T19:46:11Z\n",
      "5 __ node/558561826 __ 2018-02-20T03:43:33Z\n",
      "6 __ node/671300772 __ 2014-08-15T01:23:18Z\n",
      "7 __ node/845563398 __ 2015-04-30T09:13:42Z\n",
      "8 __ node/849979315 __ 2018-08-28T20:09:45Z\n",
      "9 __ node/849979321 __ 2011-11-14T05:05:46Z\n",
      "10 __ node/943246297 __ 2018-11-02T03:19:53Z\n",
      "11 __ node/943246301 __ 2010-10-10T07:16:23Z\n",
      "12 __ node/943246319 __ 2011-07-04T22:07:27Z\n",
      "13 __ node/943246321 __ 2011-07-04T22:07:27Z\n",
      "14 __ node/943246322 __ 2011-07-04T22:07:27Z\n",
      "15 __ node/943246323 __ 2018-09-16T09:48:18Z\n",
      "16 __ node/943246324 __ 2011-07-04T21:47:02Z\n",
      "17 __ node/949307742 __ 2015-04-07T09:38:16Z\n",
      "18 __ node/949307744 __ 2015-04-07T09:38:16Z\n",
      "19 __ node/992508275 __ 2010-11-17T09:45:15Z\n",
      "20 __ node/992508276 __ 2017-03-13T01:53:06Z\n",
      "21 __ node/1114720610 __ 2019-03-24T03:19:06Z\n",
      "22 __ node/1341029283 __ 2011-06-27T11:57:49Z\n",
      "23 __ node/1341029402 __ 2011-11-03T04:34:59Z\n",
      "24 __ node/1341029439 __ 2011-11-13T06:15:02Z\n",
      "25 __ node/1349642553 __ 2017-09-10T05:38:45Z\n",
      "26 __ node/1349658484 __ 2015-04-13T18:23:15Z\n",
      "27 __ node/1349658485 __ 2011-07-04T21:19:51Z\n",
      "28 __ node/1349658487 __ 2011-07-04T21:19:51Z\n",
      "29 __ node/1349658489 __ 2011-07-04T22:07:27Z\n",
      "30 __ node/1349658500 __ 2011-07-04T21:19:53Z\n",
      "31 __ node/1349664584 __ 2014-09-05T13:36:52Z\n",
      "32 __ node/1349680673 __ 2018-11-29T07:49:26Z\n",
      "33 __ node/1349680681 __ 2018-06-27T13:31:42Z\n",
      "34 __ node/1349680686 __ 2013-12-31T10:39:02Z\n",
      "35 __ node/1349680697 __ 2015-04-27T18:48:25Z\n",
      "36 __ node/1349680700 __ 2011-07-04T21:47:01Z\n",
      "37 __ node/1349680727 __ 2011-07-04T21:47:01Z\n",
      "38 __ node/1349680754 __ 2015-04-30T09:13:42Z\n",
      "39 __ node/1349680757 __ 2015-05-09T10:37:36Z\n",
      "40 __ node/1349680776 __ 2020-05-12T19:55:28Z\n",
      "41 __ node/1349697700 __ 2014-09-05T13:30:45Z\n",
      "42 __ node/1349697713 __ 2014-09-05T13:30:44Z\n",
      "43 __ node/1349697722 __ 2011-07-04T22:07:26Z\n",
      "44 __ node/1349697726 __ 2011-07-04T22:07:26Z\n",
      "45 __ node/1349697727 __ 2011-07-04T22:07:26Z\n",
      "46 __ node/1349697737 __ 2011-07-04T22:07:26Z\n",
      "47 __ node/1349697738 __ 2011-07-04T22:07:26Z\n",
      "48 __ node/1349697742 __ 2017-01-26T05:49:14Z\n",
      "49 __ node/1350011393 __ 2013-02-24T11:19:45Z\n",
      "50 __ node/1350011407 __ 2013-08-24T06:50:06Z\n",
      "51 __ node/1350011409 __ 2011-07-05T06:12:31Z\n",
      "52 __ node/1350011414 __ 2011-07-05T06:12:31Z\n",
      "53 __ node/1390371550 __ 2019-03-27T21:26:14Z\n",
      "54 __ node/1477827143 __ 2017-06-07T08:23:53Z\n",
      "55 __ node/1489546013 __ 2019-06-17T09:14:20Z\n",
      "56 __ node/1490108258 __ 2011-11-05T08:53:15Z\n",
      "57 __ node/1490108260 __ 2019-05-22T06:40:26Z\n",
      "58 __ node/1499860699 __ 2011-11-12T12:32:13Z\n",
      "59 __ node/1500932986 __ 2017-08-17T05:44:28Z\n",
      "60 __ node/1500932989 __ 2018-03-09T08:23:28Z\n",
      "61 __ node/1502562675 __ 2019-05-22T06:47:30Z\n",
      "62 __ node/1502562678 __ 2011-11-14T05:05:42Z\n",
      "63 __ node/1622172123 __ 2015-05-08T05:59:46Z\n",
      "64 __ node/1656490102 __ 2012-03-02T20:19:38Z\n",
      "65 __ node/1787042048 __ 2017-06-01T10:58:04Z\n",
      "66 __ node/1833834684 __ 2012-07-22T07:00:48Z\n",
      "67 __ node/1833834799 __ 2015-05-01T21:02:18Z\n",
      "68 __ node/1833834964 __ 2012-07-22T07:00:55Z\n",
      "69 __ node/1839678362 __ 2012-07-27T10:45:47Z\n",
      "70 __ node/1858151612 __ 2018-01-13T15:05:16Z\n",
      "71 __ node/1891343758 __ 2012-08-31T11:20:19Z\n",
      "72 __ node/1905155349 __ 2015-07-17T06:42:55Z\n",
      "73 __ node/1937661896 __ 2017-06-08T09:17:08Z\n",
      "74 __ node/1956285190 __ 2018-11-17T08:51:58Z\n",
      "75 __ node/1956360568 __ 2014-09-12T03:38:50Z\n",
      "76 __ node/1962379599 __ 2015-07-17T06:44:34Z\n",
      "77 __ node/1963991187 __ 2012-10-14T03:36:56Z\n",
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    },
    {
     "name": "stdout",
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      "577 __ relation/4848436 __ 2015-04-29T16:08:31Z\n",
      "578 __ relation/7853916 __ 2019-03-27T21:25:51Z\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  | tourism       | hotel             |\n",
    "| Name            | name          | Name of hotel     |\n",
    "| Number of stars | stars         | Number of stars   |\n",
    "| Opening hours   | opening_hours | Mo-Fr 10:00-17:00 |\n",
    "| Phone           | phone         | Ph no.            |\n",
    "| Email           | email         | email address     |\n",
    "| Number of beds  | beds          | number_of_beds    |\n",
    "| Number of Rooms | rooms         | number_of_rooms   |"
   ]
  },
  {
   "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 7 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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 91 artists>"
      ]
     },
     "execution_count": 12,
     "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 12 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
}