{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nenn5vEr1_NJ"
   },
   "source": [
    "# Performing Spatial Queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wIFWAO9r1_NM"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "Spatial queries allows you to select features in a layer by their spatial relationships with features from another layer. GeoPandas provides `sjoin()` and `sjoin_nearest()` functions that can be used to perform a spatial query.\n",
    "\n",
    "## Overview of the Task\n",
    "\n",
    "We will be working with 2 data layers for the city of Melbourne, Australia. Given the data layers for the pubs and bars in the city and the locations of all metro stations, we want to find out which bars and pubs are within 500 meters of a metro station.\n",
    "\n",
    "**Input Layers**:\n",
    "* `metro_stations_accessbility.zip`: A shapefile of metro stations in Melbourne\n",
    "* `Bars_and_pubs__with_patron_capacity.csv`: CSV file with location of bars and pubs in Melbournse\n",
    "\n",
    "**Output**:\n",
    "* `spatial_query.gpkg`: A GeoPackage containing all bars and pubs within 500 meters of a metro station\n",
    "\n",
    "**Data Credit**\n",
    "\n",
    "* 2019 The City of Melbourne Open Data Portal. Data provided by Metro Trains Melbourne and Census of Land Use and Employment (CLUE)\n",
    "\n",
    "\n",
    "**Watch Video Walkthrough** <a href='https://www.youtube.com/watch?v=V-gyuP7vMSg' target='_blank'><img src='https://github.com/spatialthoughts/geopython-tutorials/blob/main/images/yt_logo.png?raw=1'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5F4UU8ky1_NM"
   },
   "source": [
    "## Setup and Data Download\n",
    "\n",
    "The following blocks of code will install the required packages and download the datasets to your Colab environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "LZY3cd9K1_NO"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "4SqUL0h81_NO"
   },
   "outputs": [],
   "source": [
    "data_folder = 'data'\n",
    "output_folder = 'output'\n",
    "\n",
    "if not os.path.exists(data_folder):\n",
    "    os.mkdir(data_folder)\n",
    "if not os.path.exists(output_folder):\n",
    "    os.mkdir(output_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "BilOp9-B1_NO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloaded data/Bars_and_pubs__with_patron_capacity.csv\n",
      "Downloaded data/metro_stations_accessbility.zip\n"
     ]
    }
   ],
   "source": [
    "def download(url):\n",
    "    filename = os.path.join(data_folder, os.path.basename(url))\n",
    "    if not os.path.exists(filename):\n",
    "        from urllib.request import urlretrieve\n",
    "        local, _ = urlretrieve(url, filename)\n",
    "        print('Downloaded ' + local)\n",
    "\n",
    "data_url = 'https://github.com/spatialthoughts/geopython-tutorials/releases/download/data/'\n",
    "\n",
    "download(data_url + 'Bars_and_pubs__with_patron_capacity.csv')\n",
    "download(data_url + 'metro_stations_accessbility.zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GNO6nOET1_NO"
   },
   "source": [
    "## Procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GCC7mjMJ1_NP"
   },
   "source": [
    "Read the `Bars_and_pubs__with_patron_capacity.csv` file and convert it to a GeoDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "mjhj_ZJO1_NP",
    "outputId": "25f2f804-63c8-4ea2-a91a-5869d777955b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Census year</th>\n",
       "      <th>Block ID</th>\n",
       "      <th>Property ID</th>\n",
       "      <th>Base property ID</th>\n",
       "      <th>Street address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>247</td>\n",
       "      <td>106238</td>\n",
       "      <td>106238</td>\n",
       "      <td>192-202 Lygon Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>252</td>\n",
       "      <td>106244</td>\n",
       "      <td>106244</td>\n",
       "      <td>160-162 Lygon Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>16</td>\n",
       "      <td>104018</td>\n",
       "      <td>104018</td>\n",
       "      <td>172-192 Flinders Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>214</td>\n",
       "      <td>106186</td>\n",
       "      <td>106186</td>\n",
       "      <td>414-422 Lygon Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>76</td>\n",
       "      <td>589841</td>\n",
       "      <td>105749</td>\n",
       "      <td>221 Little Lonsdale Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3333</th>\n",
       "      <td>2017</td>\n",
       "      <td>85</td>\n",
       "      <td>105746</td>\n",
       "      <td>105746</td>\n",
       "      <td>183-265 La Trobe Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3334</th>\n",
       "      <td>2017</td>\n",
       "      <td>74</td>\n",
       "      <td>104662</td>\n",
       "      <td>104662</td>\n",
       "      <td>106-112 Hardware Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3335</th>\n",
       "      <td>2017</td>\n",
       "      <td>268</td>\n",
       "      <td>108154</td>\n",
       "      <td>108153</td>\n",
       "      <td>49 Rathdowne Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3336</th>\n",
       "      <td>2017</td>\n",
       "      <td>104</td>\n",
       "      <td>104085</td>\n",
       "      <td>104085</td>\n",
       "      <td>167-175 Franklin Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3337</th>\n",
       "      <td>2017</td>\n",
       "      <td>33</td>\n",
       "      <td>100730</td>\n",
       "      <td>100730</td>\n",
       "      <td>12-16 Bank Place</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3338 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Census year  Block ID  Property ID  Base property ID  \\\n",
       "0            2002       247       106238            106238   \n",
       "1            2002       252       106244            106244   \n",
       "2            2006        16       104018            104018   \n",
       "3            2002       214       106186            106186   \n",
       "4            2008        76       589841            105749   \n",
       "...           ...       ...          ...               ...   \n",
       "3333         2017        85       105746            105746   \n",
       "3334         2017        74       104662            104662   \n",
       "3335         2017       268       108154            108153   \n",
       "3336         2017       104       104085            104085   \n",
       "3337         2017        33       100730            100730   \n",
       "\n",
       "                  Street address  \n",
       "0           192-202 Lygon Street  \n",
       "1           160-162 Lygon Street  \n",
       "2        172-192 Flinders Street  \n",
       "3           414-422 Lygon Street  \n",
       "4     221 Little Lonsdale Street  \n",
       "...                          ...  \n",
       "3333     183-265 La Trobe Street  \n",
       "3334     106-112 Hardware Street  \n",
       "3335         49 Rathdowne Street  \n",
       "3336     167-175 Franklin Street  \n",
       "3337            12-16 Bank Place  \n",
       "\n",
       "[3338 rows x 5 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_file = 'Bars_and_pubs__with_patron_capacity.csv'\n",
    "csv_path = os.path.join(data_folder, csv_file)\n",
    "barspubs_df = pd.read_csv(csv_path) \n",
    "barspubs_df.iloc[:, :5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V8j2wbI-1_NP"
   },
   "source": [
    "You will note that the data contains multiple features for each establishment from different years. Let's sort by *Property ID* and check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "pvn-wF5U1_NP",
    "outputId": "21e5655e-ef01-42e3-8919-7f4394928c0c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Census year</th>\n",
       "      <th>Block ID</th>\n",
       "      <th>Property ID</th>\n",
       "      <th>Base property ID</th>\n",
       "      <th>Street address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>2017</td>\n",
       "      <td>103</td>\n",
       "      <td>100160</td>\n",
       "      <td>100160</td>\n",
       "      <td>196-200 A'Beckett Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>2011</td>\n",
       "      <td>103</td>\n",
       "      <td>100160</td>\n",
       "      <td>100160</td>\n",
       "      <td>196-200 A'Beckett Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>2015</td>\n",
       "      <td>103</td>\n",
       "      <td>100160</td>\n",
       "      <td>100160</td>\n",
       "      <td>196-200 A'Beckett Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2008</td>\n",
       "      <td>103</td>\n",
       "      <td>100160</td>\n",
       "      <td>100160</td>\n",
       "      <td>196-200 A'Beckett Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>2010</td>\n",
       "      <td>103</td>\n",
       "      <td>100160</td>\n",
       "      <td>100160</td>\n",
       "      <td>196-200 A'Beckett Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>2015</td>\n",
       "      <td>1105</td>\n",
       "      <td>628712</td>\n",
       "      <td>628712</td>\n",
       "      <td>717-731 Collins Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>2016</td>\n",
       "      <td>66</td>\n",
       "      <td>635138</td>\n",
       "      <td>635138</td>\n",
       "      <td>13 Heffernan Lane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>2017</td>\n",
       "      <td>66</td>\n",
       "      <td>635138</td>\n",
       "      <td>635138</td>\n",
       "      <td>13 Heffernan Lane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>2016</td>\n",
       "      <td>270</td>\n",
       "      <td>664626</td>\n",
       "      <td>104468</td>\n",
       "      <td>230 Grattan Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3228</th>\n",
       "      <td>2017</td>\n",
       "      <td>270</td>\n",
       "      <td>664626</td>\n",
       "      <td>104468</td>\n",
       "      <td>230 Grattan Street</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3338 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Census year  Block ID  Property ID  Base property ID  \\\n",
       "3129         2017       103       100160            100160   \n",
       "709          2011       103       100160            100160   \n",
       "2553         2015       103       100160            100160   \n",
       "247          2008       103       100160            100160   \n",
       "2201         2010       103       100160            100160   \n",
       "...           ...       ...          ...               ...   \n",
       "2579         2015      1105       628712            628712   \n",
       "2832         2016        66       635138            635138   \n",
       "3069         2017        66       635138            635138   \n",
       "3058         2016       270       664626            104468   \n",
       "3228         2017       270       664626            104468   \n",
       "\n",
       "                Street address  \n",
       "3129  196-200 A'Beckett Street  \n",
       "709   196-200 A'Beckett Street  \n",
       "2553  196-200 A'Beckett Street  \n",
       "247   196-200 A'Beckett Street  \n",
       "2201  196-200 A'Beckett Street  \n",
       "...                        ...  \n",
       "2579    717-731 Collins Street  \n",
       "2832         13 Heffernan Lane  \n",
       "3069         13 Heffernan Lane  \n",
       "3058        230 Grattan Street  \n",
       "3228        230 Grattan Street  \n",
       "\n",
       "[3338 rows x 5 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barspubs_df = barspubs_df.sort_values('Property ID')\n",
    "barspubs_df.iloc[:, :5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1jXJ9cnk1_NQ"
   },
   "source": [
    "We are interested only in the location of the establishment. So we can de-duplicate the dataframe and keep only 1 record per unique *Property ID*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "pBvpXcCP1_NR",
    "outputId": "5d231198-aec9-4d15-f114-c3308ac57acd"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Census year</th>\n",
       "      <th>Block ID</th>\n",
       "      <th>Property ID</th>\n",
       "      <th>Base property ID</th>\n",
       "      <th>Street address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>2017</td>\n",
       "      <td>103</td>\n",
       "      <td>100160</td>\n",
       "      <td>100160</td>\n",
       "      <td>196-200 A'Beckett Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>2011</td>\n",
       "      <td>409</td>\n",
       "      <td>100441</td>\n",
       "      <td>100441</td>\n",
       "      <td>118-126 Ireland Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2004</td>\n",
       "      <td>315</td>\n",
       "      <td>100514</td>\n",
       "      <td>100514</td>\n",
       "      <td>204-206 Arden Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>2009</td>\n",
       "      <td>33</td>\n",
       "      <td>100727</td>\n",
       "      <td>100727</td>\n",
       "      <td>5-9 Bank Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>2013</td>\n",
       "      <td>33</td>\n",
       "      <td>100730</td>\n",
       "      <td>100730</td>\n",
       "      <td>12-16 Bank Place</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>2009</td>\n",
       "      <td>2391</td>\n",
       "      <td>616966</td>\n",
       "      <td>616966</td>\n",
       "      <td>2 Boundary Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>2013</td>\n",
       "      <td>1110</td>\n",
       "      <td>620312</td>\n",
       "      <td>593737</td>\n",
       "      <td>23-37 Star Crescent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>2016</td>\n",
       "      <td>1105</td>\n",
       "      <td>628712</td>\n",
       "      <td>628712</td>\n",
       "      <td>717-731 Collins Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>2016</td>\n",
       "      <td>66</td>\n",
       "      <td>635138</td>\n",
       "      <td>635138</td>\n",
       "      <td>13 Heffernan Lane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>2016</td>\n",
       "      <td>270</td>\n",
       "      <td>664626</td>\n",
       "      <td>104468</td>\n",
       "      <td>230 Grattan Street</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>308 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Census year  Block ID  Property ID  Base property ID  \\\n",
       "3129         2017       103       100160            100160   \n",
       "1087         2011       409       100441            100441   \n",
       "410          2004       315       100514            100514   \n",
       "1501         2009        33       100727            100727   \n",
       "1159         2013        33       100730            100730   \n",
       "...           ...       ...          ...               ...   \n",
       "1601         2009      2391       616966            616966   \n",
       "1182         2013      1110       620312            593737   \n",
       "3028         2016      1105       628712            628712   \n",
       "2832         2016        66       635138            635138   \n",
       "3058         2016       270       664626            104468   \n",
       "\n",
       "                Street address  \n",
       "3129  196-200 A'Beckett Street  \n",
       "1087    118-126 Ireland Street  \n",
       "410       204-206 Arden Street  \n",
       "1501            5-9 Bank Place  \n",
       "1159          12-16 Bank Place  \n",
       "...                        ...  \n",
       "1601           2 Boundary Road  \n",
       "1182       23-37 Star Crescent  \n",
       "3028    717-731 Collins Street  \n",
       "2832         13 Heffernan Lane  \n",
       "3058        230 Grattan Street  \n",
       "\n",
       "[308 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barspubs_df = barspubs_df.drop_duplicates(subset=['Property ID'], keep='first')\n",
    "barspubs_df.iloc[:, :5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FW5K7obK1_NR"
   },
   "source": [
    "Now that we are done with data cleaning, let's turn this dataframe in to a spatial layer. The location of the establishment is defined using the *x coordinate* and *y coordinate* columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trading name</th>\n",
       "      <th>Number of patrons</th>\n",
       "      <th>x coordinate</th>\n",
       "      <th>y coordinate</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>Nomads Industry Backpackers</td>\n",
       "      <td>200</td>\n",
       "      <td>144.957445</td>\n",
       "      <td>-37.809985</td>\n",
       "      <td>(-37.80998494, 144.9574447)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>Railway Hotel</td>\n",
       "      <td>241</td>\n",
       "      <td>144.942118</td>\n",
       "      <td>-37.806054</td>\n",
       "      <td>(-37.80605366, 144.9421177)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>North Melbourne Football Club Social Club</td>\n",
       "      <td>210</td>\n",
       "      <td>144.941312</td>\n",
       "      <td>-37.799055</td>\n",
       "      <td>(-37.79905531, 144.9413118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>Mitre Tavern</td>\n",
       "      <td>300</td>\n",
       "      <td>144.960311</td>\n",
       "      <td>-37.816805</td>\n",
       "      <td>(-37.81680457, 144.9603112)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>Melbourne Savage Club</td>\n",
       "      <td>90</td>\n",
       "      <td>144.960577</td>\n",
       "      <td>-37.816545</td>\n",
       "      <td>(-37.81654467, 144.9605765)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>Vodka Locka &amp; Red Leaf Restaurant</td>\n",
       "      <td>40</td>\n",
       "      <td>144.939233</td>\n",
       "      <td>-37.795318</td>\n",
       "      <td>(-37.79531826, 144.9392333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>Harbour Town Hotel</td>\n",
       "      <td>498</td>\n",
       "      <td>144.937830</td>\n",
       "      <td>-37.813150</td>\n",
       "      <td>(-37.81315031, 144.9378299)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>Bar Nicional</td>\n",
       "      <td>75</td>\n",
       "      <td>144.950093</td>\n",
       "      <td>-37.820687</td>\n",
       "      <td>(-37.82068699, 144.9500934)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>Union Electric</td>\n",
       "      <td>72</td>\n",
       "      <td>144.966591</td>\n",
       "      <td>-37.811802</td>\n",
       "      <td>(-37.81180178, 144.9665913)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>HJC Bar</td>\n",
       "      <td>200</td>\n",
       "      <td>144.961247</td>\n",
       "      <td>-37.797398</td>\n",
       "      <td>(-37.79739836, 144.9612468)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>308 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Trading name  Number of patrons  \\\n",
       "3129                Nomads Industry Backpackers                200   \n",
       "1087                              Railway Hotel                241   \n",
       "410   North Melbourne Football Club Social Club                210   \n",
       "1501                               Mitre Tavern                300   \n",
       "1159                      Melbourne Savage Club                 90   \n",
       "...                                         ...                ...   \n",
       "1601          Vodka Locka & Red Leaf Restaurant                 40   \n",
       "1182                         Harbour Town Hotel                498   \n",
       "3028                               Bar Nicional                 75   \n",
       "2832                             Union Electric                 72   \n",
       "3058                                    HJC Bar                200   \n",
       "\n",
       "      x coordinate  y coordinate                     Location  \n",
       "3129    144.957445    -37.809985  (-37.80998494, 144.9574447)  \n",
       "1087    144.942118    -37.806054  (-37.80605366, 144.9421177)  \n",
       "410     144.941312    -37.799055  (-37.79905531, 144.9413118)  \n",
       "1501    144.960311    -37.816805  (-37.81680457, 144.9603112)  \n",
       "1159    144.960577    -37.816545  (-37.81654467, 144.9605765)  \n",
       "...            ...           ...                          ...  \n",
       "1601    144.939233    -37.795318  (-37.79531826, 144.9392333)  \n",
       "1182    144.937830    -37.813150  (-37.81315031, 144.9378299)  \n",
       "3028    144.950093    -37.820687  (-37.82068699, 144.9500934)  \n",
       "2832    144.966591    -37.811802  (-37.81180178, 144.9665913)  \n",
       "3058    144.961247    -37.797398  (-37.79739836, 144.9612468)  \n",
       "\n",
       "[308 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barspubs_df.iloc[:, -5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a geometry column and use it to define a new GeoDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "TfValzpT1_NR",
    "outputId": "702e8a30-79c2-4dd7-e8ea-b12ce3fef81d"
   },
   "outputs": [],
   "source": [
    "geometry = gpd.points_from_xy(barspubs_df['x coordinate'],barspubs_df['y coordinate'])\n",
    "barspubs_gdf = gpd.GeoDataFrame(barspubs_df, crs='EPSG:4326', geometry=geometry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UUJZCI2J1_NS"
   },
   "source": [
    "Next, we will read the zipped shapefile of metro stations as a GeoDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "OD7WF-G31_NS",
    "outputId": "5a67741e-9941-499d-ae51-57409937c15c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>pids</th>\n",
       "      <th>he_loop</th>\n",
       "      <th>lift</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alamein</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>POINT (145.07956 -37.86884)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albion</td>\n",
       "      <td>Dot Matrix</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>POINT (144.82471 -37.77766)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alphington</td>\n",
       "      <td>Dot Matrix</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>POINT (145.03125 -37.7784)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Altona</td>\n",
       "      <td>LCD</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>POINT (144.8306 -37.86725)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anstey</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>POINT (144.96056 -37.7619)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Williams Landing</td>\n",
       "      <td>Dot Matrix</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>POINT (144.74719 -37.8701)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Aircraft</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>POINT (144.76081 -37.8666)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Flemington Racecourse</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>POINT (144.9072 -37.78759)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Showgrounds</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>POINT (144.91498 -37.78355)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Southland</td>\n",
       "      <td>LCD</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>POINT (145.04867 -37.95818)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   station        pids he_loop lift  \\\n",
       "0                  Alamein          No      No   No   \n",
       "1                   Albion  Dot Matrix      No   No   \n",
       "2               Alphington  Dot Matrix      No   No   \n",
       "3                   Altona         LCD      No   No   \n",
       "4                   Anstey          No      No   No   \n",
       "..                     ...         ...     ...  ...   \n",
       "214       Williams Landing  Dot Matrix     Yes  Yes   \n",
       "215               Aircraft          No      No   No   \n",
       "216  Flemington Racecourse          No      No   No   \n",
       "217            Showgrounds          No      No   No   \n",
       "218              Southland         LCD     Yes  Yes   \n",
       "\n",
       "                        geometry  \n",
       "0    POINT (145.07956 -37.86884)  \n",
       "1    POINT (144.82471 -37.77766)  \n",
       "2     POINT (145.03125 -37.7784)  \n",
       "3     POINT (144.8306 -37.86725)  \n",
       "4     POINT (144.96056 -37.7619)  \n",
       "..                           ...  \n",
       "214   POINT (144.74719 -37.8701)  \n",
       "215   POINT (144.76081 -37.8666)  \n",
       "216   POINT (144.9072 -37.78759)  \n",
       "217  POINT (144.91498 -37.78355)  \n",
       "218  POINT (145.04867 -37.95818)  \n",
       "\n",
       "[219 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip_file = 'metro_stations_accessbility.zip'\n",
    "zip_path = os.path.join('zip://', data_folder, zip_file)\n",
    "metrostations_gdf = gpd.read_file(zip_path)\n",
    "metrostations_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2UBXdIJQ1_NS"
   },
   "source": [
    "Visualize the layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "toXX_L261_NS",
    "outputId": "c77ef7e0-67a2-47f5-e87c-10dcb4609360"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "fig.set_size_inches(15,7)\n",
    "barspubs_gdf.plot(ax=ax, color='red', alpha=0.5, marker='+')\n",
    "metrostations_gdf.plot(ax=ax, color='blue', alpha=1, marker='o')\n",
    "\n",
    "minx, miny, maxx, maxy = barspubs_gdf.total_bounds\n",
    "ax.set_xlim(minx, maxx)\n",
    "ax.set_ylim(miny, maxy)\n",
    "\n",
    "legend_elements = [\n",
    "    plt.plot([],[], color='red', alpha=0.5, marker='+', label='Bars and Pubs', ls='')[0],\n",
    "    plt.plot([],[], color='blue', alpha=1, marker='o', label='Metro Stations', ls='')[0]]\n",
    "ax.legend(handles=legend_elements, loc='upper right')\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t6Gkn4_f1_NS"
   },
   "source": [
    "For performing any analysis, we must use a Projected CRS. Let's reproject to a local CRS for Melbourne. *GDA 2020 / MGA zone 55 EPSG:7855*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "q2X9LCgc1_NS"
   },
   "outputs": [],
   "source": [
    "metrostations_gdf_reprojected = metrostations_gdf.to_crs('EPSG:7855')\n",
    "barspubs_gdf_reprojected = barspubs_gdf.to_crs('EPSG:7855')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CBly8M1p1_NS"
   },
   "source": [
    "We now buffer the metro stations to 500 meters. GeoPandas uses shapely for buffer operation. We specify [buffer parameters](https://shapely.readthedocs.io/en/latest/manual.html#constructive-methods) to match QGIS's default style."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "NsFyBAE11_NS"
   },
   "outputs": [],
   "source": [
    "radius = 500\n",
    "\n",
    "buffer_geometry = metrostations_gdf_reprojected.buffer(\n",
    "    radius, resolution=5, cap_style=1, join_style=1, mitre_limit=2)\n",
    "metrostations_gdf_reprojected['geometry'] = buffer_geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aMauMEUQ1_NS"
   },
   "source": [
    "Visualize the buffers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "0SF3v8-01_NS",
    "outputId": "f2c43fb8-c92e-441b-c719-73da05aee7c3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "fig.set_size_inches(15,7)\n",
    "barspubs_gdf_reprojected.plot(ax=ax, color='red', alpha=0.5, marker='+')\n",
    "metrostations_gdf_reprojected.plot(ax=ax, facecolor='none', edgecolor='blue',  alpha=1, marker='o')\n",
    "\n",
    "minx, miny, maxx, maxy = barspubs_gdf_reprojected.total_bounds\n",
    "ax.set_xlim(minx, maxx)\n",
    "ax.set_ylim(miny, maxy)\n",
    "\n",
    "legend_elements = [\n",
    "    plt.plot([],[], color='red', alpha=0.5, marker='+', label='Bars and Pubs', ls='')[0],\n",
    "    plt.plot([],[], color='blue', alpha=1, marker='o', label='Metro Station Buffers', ls='')[0]]\n",
    "ax.legend(handles=legend_elements, loc='upper right')\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sl9rU0mR1_NS"
   },
   "source": [
    "As we want to do a spatial join, we need to dissolve the resulting buffers. Otherwise we will get 1 feature for every intersecting buffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "6FzPQXPl1_NT"
   },
   "outputs": [],
   "source": [
    "metrostations_gdf_reprojected['dissolvefield'] = 1\n",
    "dissolved_buffers = metrostations_gdf_reprojected.dissolve(by='dissolvefield')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "aOytUkBO1_NT",
    "outputId": "d4e32d8d-f73b-46a9-fc54-83e4e3265a4d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "fig.set_size_inches(15,7)\n",
    "barspubs_gdf_reprojected.plot(ax=ax, color='red', alpha=0.5, marker='+')\n",
    "dissolved_buffers.plot(ax=ax, facecolor='none', edgecolor='blue',  alpha=1)\n",
    "\n",
    "minx, miny, maxx, maxy = barspubs_gdf_reprojected.total_bounds\n",
    "ax.set_xlim(minx, maxx)\n",
    "ax.set_ylim(miny, maxy)\n",
    "\n",
    "legend_elements = [\n",
    "    plt.plot([],[], color='red', alpha=0.5, marker='+', label='Bars and Pubs', ls='')[0],\n",
    "    plt.plot([],[], color='blue', alpha=1, marker='o', label='Metro Station Buffers (Dissolved)', ls='')[0]]\n",
    "ax.legend(handles=legend_elements, loc='upper right')\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c48hNWfo1_NT"
   },
   "source": [
    "Now do a spatial join to select all bars and pubs within the dissolved buffer region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "p40FGRqr1_NT",
    "outputId": "ef52707b-659f-4275-f476-68dc3a18e67f"
   },
   "outputs": [],
   "source": [
    "selected = barspubs_gdf_reprojected.sjoin(\n",
    "    dissolved_buffers, how='inner', predicate='within')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IA3kXo7P1_NT"
   },
   "source": [
    "For our final visualization, we show the selected establishments within the buffer zone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "QcV2Qe2s1_NT",
    "outputId": "868b67fd-ce7a-45b9-ed9f-3ae226bd55da"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "fig.set_size_inches(15,7)\n",
    "barspubs_gdf_reprojected.plot(ax=ax, color='red', alpha=0.5, marker='+')\n",
    "dissolved_buffers.plot(ax=ax, facecolor='none', edgecolor='blue',  alpha=1)\n",
    "selected.plot(ax=ax, color='yellow', alpha=0.5, marker='+')\n",
    "\n",
    "minx, miny, maxx, maxy = barspubs_gdf_reprojected.total_bounds\n",
    "ax.set_xlim(minx, maxx)\n",
    "ax.set_ylim(miny, maxy)\n",
    "\n",
    "legend_elements = [\n",
    "    plt.plot([],[], color='red', alpha=0.5, marker='+', label='Bars and Pubs', ls='')[0],\n",
    "    plt.plot([],[], color='blue', alpha=1, marker='o', label='Metro Station Buffers', ls='')[0],\n",
    "    plt.plot([],[], color='yellow', alpha=1, marker='+', label='Selected Bars and Pubs', ls='')[0]\n",
    "]\n",
    "\n",
    "ax.legend(handles=legend_elements, loc='upper right')\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "24EFSvb_1_NT"
   },
   "source": [
    "Finally, we save all our layers into a output geopackage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "MC5MRTU91_NT"
   },
   "outputs": [],
   "source": [
    "output_file = 'spatial_query.gpkg'\n",
    "output_path = os.path.join(output_folder, output_file)\n",
    "\n",
    "barspubs_gdf_reprojected.to_file(filename=output_path, layer='bars_and_pubs')\n",
    "metrostations_gdf_reprojected.to_file(filename=output_path, layer='metro_stations')\n",
    "selected.to_file(filename=output_path, layer='selected_locations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "If you want to give feedback or share your experience with this tutorial, please comment below. (requires GitHub account)\n",
    "\n",
    "\n",
    "<script src='https://utteranc.es/client.js'\n",
    "    repo='spatialthoughts/geopython-tutorials'\n",
    "    issue-term='pathname'\n",
    "    theme='github-light'\n",
    "    crossorigin='anonymous'\n",
    "    async>\n",
    "</script>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}