src/run.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b95b5eda-b698-411a-94c8-d46eb6357d1f",
   "metadata": {},
   "source": [
    "# Run\n",
    "\n",
    "This notebook creates the neighbourhood aggregates from the preprocessed data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946803e8-17df-45ba-b553-e0fc81a9dbfc",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "27df3bad-c48d-4662-8949-54bcfa59f234",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dist_folder = '../tmp/dist' # stores outputs\n",
    "data_sources_file = 'data.csv'\n",
    "study_area_dataset = 'toronto' # bbox will be used as study area\n",
    "force = False # whether to force processing\n",
    "genstats = ['sum', 'mean', 'min', 'max', 'median', 'var', 'skew', 'std', 'sem', 'mad'] # name of gen stats from pandas to agg numeric values\n",
    "genstats_quantiles = True # whether to include quantiles in gen stats or not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a9371d8-7414-45a4-8c26-228b856d0445",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2a2cff74-3fc7-4bbe-8e57-7ee553997eea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "from IPython.display import display, HTML\n",
    "from pathlib import Path\n",
    "from shapely.geometry import box, Point\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ed5167-86fd-44d4-8bda-e599361da4f7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f33a00e0-f8a0-4450-a6d0-a0ad21a86ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For timing processes and displaying processing messages\n",
    "class Timer:\n",
    "    \n",
    "    def __init__(self, name, process_name=None, start_prefix=None, end_prefix=None):\n",
    "        self.name = name\n",
    "        self.process_name = process_name\n",
    "        self.start_prefix = start_prefix if start_prefix else 'Loading' if process_name.startswith('already') else 'Processing'\n",
    "        self.end_prefix = end_prefix if end_prefix else 'Loaded' if process_name.startswith('already') else 'Processed'\n",
    "    \n",
    "    def start(self):\n",
    "        self.start = time()\n",
    "        self.start_dt = datetime.now()\n",
    "        print(f'{self.start_prefix} {self.name} - {self.process_name} ({self.start_dt})...')\n",
    "    \n",
    "    def end(self):\n",
    "        self.end = time()\n",
    "        self.end_dt = datetime.now()\n",
    "        self.elapsed = str(timedelta(seconds=self.end - self.start))\n",
    "        print(f'{self.end_prefix} {self.name} - {self.process_name} ({self.end_dt}) [Elapsed: {self.elapsed}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab38c65-24b7-4953-a3b9-a6d50fa5e7ea",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf78533-65c2-4334-9c29-96477375ab31",
   "metadata": {},
   "source": [
    "### Data Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f4a68a18-ac90-4685-837a-681da0dad85e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formats human readable sizes in bytes\n",
    "# https://stackoverflow.com/questions/1094841/get-human-readable-version-of-file-size\n",
    "def format_size(num, suffix=\"B\"):\n",
    "    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n",
    "        if abs(num) < 1024.0:\n",
    "            return f\"{num:3.1f} {unit}{suffix}\"\n",
    "        num /= 1024.0\n",
    "    return f\"{num:.1f} Yi{suffix}\"\n",
    "\n",
    "# Format cols to be lowercase with underscores and alphanumerics only\n",
    "def format_cols(df):\n",
    "    out = df\n",
    "    out = out.rename(str.lower, axis='columns') # lowercase\n",
    "    out.columns = [re.sub('[^A-Za-z0-9 ]+', '', c) for c in out.columns] # remove non alphanumeric\n",
    "    out.columns = [' '.join(c.split()).replace(' ', '_') for c in out.columns] # remove extra whitespace, replace w/ underscore\n",
    "    return out\n",
    "\n",
    "# Extracts file info for summary\n",
    "def file_info(file):\n",
    "    stats = Path(file).stat()\n",
    "    modified = datetime.fromtimestamp(stats.st_mtime)\n",
    "    created = datetime.fromtimestamp(stats.st_birthtime)\n",
    "    size = format_size(stats.st_size)\n",
    "    out = {'created': created, 'modified': modified, 'size': size}\n",
    "    return out\n",
    "\n",
    "# Get basic info on a pandas dataframe\n",
    "def df_info(df, name):\n",
    "    memory = format_size(df.memory_usage().sum())\n",
    "    cols = len(df.columns)\n",
    "    rows = len(df)\n",
    "    geometry = str(pd.unique(df.geometry.geom_type)) if 'geometry' in df else None\n",
    "    out = {\n",
    "        'dataset': name,\n",
    "        'columns': cols,\n",
    "        'rows': rows,\n",
    "        'memory_usage': memory,\n",
    "        'geometry': geometry\n",
    "    }\n",
    "    return out\n",
    "\n",
    "# Read csv, shp, or geojson file into pandas or geopandas\n",
    "def read_data(file, *args, **kwargs):\n",
    "    path = Path(file)\n",
    "    ext = path.suffix if path.suffix != '.zip' else path.suffixes[0]\n",
    "    ext = ext.lower()\n",
    "    if ext == '.csv':\n",
    "        out = pd.read_csv(file, *args, **kwargs)\n",
    "    elif ext in ['.geojson', '.shp']:\n",
    "        out = gpd.read_file(file, *args, **kwargs)\n",
    "    else:\n",
    "        raise ValueError('File format not supported')\n",
    "    return out\n",
    "\n",
    "# Read processed dataset in dist folder\n",
    "def read_clean_dataset(dataset, folder='../dist', *args, **kwargs):\n",
    "    file = Path(f'{folder}/clean-datasets/{dataset}.geojson')\n",
    "    out = read_data(file, *args, **kwargs)\n",
    "    return out\n",
    "\n",
    "# Check if dataset is processed in dist folder\n",
    "def dataset_processed(dataset, folder='../dist'):\n",
    "    file = Path(f'{folder}/clean-datasets/{dataset}.geojson')\n",
    "    out = file.is_file()\n",
    "    return out\n",
    "\n",
    "# Save variable to csv files\n",
    "def save_vars(gdf, dist_folder=dist_folder):\n",
    "    for c in gdf.columns:\n",
    "        if c not in ['neighbourhood_id', 'neighbourhood']:\n",
    "            v = gdf[c]\n",
    "            v.to_csv(f'{dist_folder}/by-variable/{c}.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435abfbf-234e-4af7-88bf-f27f7d56de9e",
   "metadata": {},
   "source": [
    "### Aggregation Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f15f3d2c-7552-4051-abdb-6eeb741cb0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculates the mode for a series\n",
    "def mode(x):\n",
    "    out = x.map(str).value_counts().index[0]\n",
    "    return out\n",
    "\n",
    "# 25 per quantile\n",
    "def quantile25(df):\n",
    "    out = df.quantile(0.25)\n",
    "    return out\n",
    "\n",
    "# 50 per quantile\n",
    "def quantile50(df):\n",
    "    out = df.quantile(0.5)\n",
    "    return out\n",
    "\n",
    "# 75 per quantile\n",
    "def quantile75(df):\n",
    "    out = df.quantile(0.75)\n",
    "    return out\n",
    "\n",
    "# Return a func for getting geom length aggs\n",
    "def glen(agg):\n",
    "    def out(x):\n",
    "        return getattr(x.length, agg)()\n",
    "    return out\n",
    "\n",
    "# Calculates the counts of unique values given all possible values\n",
    "def ucounts(df, c, possible):\n",
    "    possible = [str(k) for k in possible]\n",
    "    counts = df[c].value_counts().to_dict()\n",
    "    out = {k:[0] for k in possible}\n",
    "    for k, v in counts.items():\n",
    "        out[str(k)] = [v]\n",
    "    out = pd.DataFrame(out)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6fa496b-3013-4ca0-a983-8bc2f3b6eabb",
   "metadata": {},
   "source": [
    "### Metric Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a605a12a-d381-4be8-b6b2-f152d47ee3f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate line sinuosity (how curved lines are)\n",
    "def lsin(geom):\n",
    "    \n",
    "    # Calculate straight length\n",
    "    start = gpd.GeoSeries([Point(g.coords[0]) for g in geom])\n",
    "    end = gpd.GeoSeries([Point(g.coords[-1]) for g in geom])\n",
    "    straight_len = start.distance(end).reset_index(drop=True)\n",
    "    \n",
    "    # Calculate sin = actual / straight\n",
    "    actual_len = geom.length.reset_index(drop=True)\n",
    "    out = actual_len / straight_len\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79537ca3-f8ee-4c88-90f3-9db29413cd64",
   "metadata": {},
   "source": [
    "## Run\n",
    "\n",
    "Create folders to store processed data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5b613f5e-b582-4b59-8838-4e437cbb916e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create folders for clean and output data\n",
    "Path(dist_folder).mkdir(parents=True, exist_ok=True)\n",
    "for f in ['by-dataset', 'by-variable']:\n",
    "    Path(f'{dist_folder}/{f}').mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b45500a-417d-4c0f-a25a-87cf465655fc",
   "metadata": {},
   "source": [
    "Track processing time for all datasets and procedures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "892d7861-4052-4bf3-ba87-d7c4ab03622d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing all datasets - preprocessing (2022-02-17 21:37:45.327887)...\n"
     ]
    }
   ],
   "source": [
    "gtimer = Timer('all datasets', 'preprocessing')\n",
    "gtimer.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80707a52-ed0d-4459-8354-ef511af8519d",
   "metadata": {},
   "source": [
    "Include quantiles in general statistics for aggregating numeric values during spatial binning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "082f9672-b0a0-4678-adf1-6cc1588b22d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "if genstats_quantiles:\n",
    "    genstats = genstats + [quantile25, quantile50, quantile75]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae03904f-9f99-498b-b520-18342f8376ed",
   "metadata": {},
   "source": [
    "Load neighbourhood polygons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "084c6bfa-a756-426b-a7f5-93e2a8a1f04f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 100,
     "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": [
    "nbhoods = read_data(f'{dist_folder}/nbhoods.geojson')\n",
    "nbhoods.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8d0dbd2-2ac8-4187-852c-779116af551a",
   "metadata": {},
   "source": [
    "### ID Joins\n",
    "\n",
    "Join neighbourhood level datasets by neighbourhood ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "cd0f9b6d-6d9f-484b-8aae-0a6a6e99cc77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing crime - join (2022-02-17 21:37:45.617989)...\n",
      "Processed crime - join (2022-02-17 21:37:46.657139) [Elapsed: 0:00:01.039149]\n",
      "Processing education - join (2022-02-17 21:37:46.657398)...\n",
      "Processed education - join (2022-02-17 21:37:47.093440) [Elapsed: 0:00:00.436040]\n",
      "Processing environment - join (2022-02-17 21:37:47.093877)...\n",
      "Processed environment - join (2022-02-17 21:37:47.557985) [Elapsed: 0:00:00.464104]\n",
      "Processing demographics - join (2022-02-17 21:37:47.558301)...\n",
      "Processed demographics - join (2022-02-17 21:37:48.015471) [Elapsed: 0:00:00.457168]\n",
      "Processing housing - join (2022-02-17 21:37:48.015742)...\n",
      "Processed housing - join (2022-02-17 21:37:48.408109) [Elapsed: 0:00:00.392365]\n",
      "Processing economics - join (2022-02-17 21:37:48.408363)...\n",
      "Processed economics - join (2022-02-17 21:37:48.795148) [Elapsed: 0:00:00.386782]\n",
      "Processing civics - join (2022-02-17 21:37:48.795406)...\n",
      "Processed civics - join (2022-02-17 21:37:49.185909) [Elapsed: 0:00:00.390502]\n",
      "Processing health - join (2022-02-17 21:37:49.186155)...\n",
      "Processed health - join (2022-02-17 21:37:49.599773) [Elapsed: 0:00:00.413616]\n"
     ]
    }
   ],
   "source": [
    "to_join = [\n",
    "    'crime',\n",
    "    'education',\n",
    "    'environment',\n",
    "    'demographics',\n",
    "    'housing',\n",
    "    'economics',\n",
    "    'civics',\n",
    "    'health'\n",
    "]\n",
    "\n",
    "for name in to_join:\n",
    "    \n",
    "    file = Path(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson')\n",
    "    if not file.is_file() or force:\n",
    "        \n",
    "        # Start processing\n",
    "        timer = Timer(name, 'join')\n",
    "        timer.start()\n",
    "\n",
    "        # Read and prepare data\n",
    "        data = read_data(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "        data = data.drop('geometry', axis=1)\n",
    "\n",
    "        # Add neighbourhood and remove from dataset\n",
    "        if 'neighbourhood' in data:\n",
    "            nbhoods['neighbourhood'] = data['neighbourhood']\n",
    "            data = data.drop('neighbourhood', axis=1)\n",
    "\n",
    "        # Join by nbhood id\n",
    "        gdf = pd.merge(\n",
    "            nbhoods,\n",
    "            data,\n",
    "            on='neighbourhood_id',\n",
    "            how='left'\n",
    "        )\n",
    "        gdf = gdf.set_index('neighbourhood_id')\n",
    "\n",
    "        # Save by dataset\n",
    "        gdf.to_file(file)\n",
    "\n",
    "        # Save by variable\n",
    "        save_vars(gdf)\n",
    "\n",
    "        # End processing\n",
    "        timer.end()\n",
    "        \n",
    "    else:\n",
    "        print(f'Skipped {name} - already joined ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3fa0ceb-0bdf-4be9-b5bc-fcb7608fdee0",
   "metadata": {},
   "source": [
    "Preview results of id joins:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "606e4a5d-42e2-440b-b553-4cfc8c4b212b",
   "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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>demographics_pop2016_2008</th>\n",
       "      <th>demographics_pop2011_2008</th>\n",
       "      <th>demographics_popchg11t16_2008</th>\n",
       "      <th>demographics_0_to_04_years_2016</th>\n",
       "      <th>demographics_0_to_14_years_2016</th>\n",
       "      <th>demographics_05_to_09_years_2016</th>\n",
       "      <th>demographics_10_to_14_years_2016</th>\n",
       "      <th>demographics_100_years_and_over_2016</th>\n",
       "      <th>...</th>\n",
       "      <th>demographics_65_years_and_over_2016</th>\n",
       "      <th>demographics_70_to_74_years_2016</th>\n",
       "      <th>demographics_75_to_79_years_2016</th>\n",
       "      <th>demographics_80_to_84_years_2016</th>\n",
       "      <th>demographics_85_to_89_years_2016</th>\n",
       "      <th>demographics_85_years_and_over_2016</th>\n",
       "      <th>demographics_90_to_94_years_2016</th>\n",
       "      <th>demographics_95_to_99_years_2016</th>\n",
       "      <th>demographics_all_ages_2016</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>10968</td>\n",
       "      <td>10487</td>\n",
       "      <td>0.045866</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2675.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10965.0</td>\n",
       "      <td>POLYGON ((627795.397 4836876.867, 627781.902 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>30526</td>\n",
       "      <td>29177</td>\n",
       "      <td>0.046235</td>\n",
       "      <td>925.0</td>\n",
       "      <td>2360.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5910.0</td>\n",
       "      <td>1335.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>30530.0</td>\n",
       "      <td>POLYGON ((629463.304 4836332.300, 629324.313 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>9955</td>\n",
       "      <td>9851</td>\n",
       "      <td>0.010557</td>\n",
       "      <td>505.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9955.0</td>\n",
       "      <td>POLYGON ((624110.361 4837657.166, 624091.125 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Mimico (includes Humber Bay Shores)</td>\n",
       "      <td>12541</td>\n",
       "      <td>11703</td>\n",
       "      <td>0.071606</td>\n",
       "      <td>905.0</td>\n",
       "      <td>2325.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12540.0</td>\n",
       "      <td>POLYGON ((635860.212 4836239.139, 635719.852 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>15179</td>\n",
       "      <td>15070</td>\n",
       "      <td>0.007233</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2235.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15170.0</td>\n",
       "      <td>POLYGON ((628032.302 4840233.689, 627977.197 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>126</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>25003</td>\n",
       "      <td>24363</td>\n",
       "      <td>0.026269</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>POLYGON ((637264.757 4848044.207, 637331.396 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>133</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>13362</td>\n",
       "      <td>13093</td>\n",
       "      <td>0.020545</td>\n",
       "      <td>655.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2385.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13365.0</td>\n",
       "      <td>POLYGON ((649297.450 4847596.814, 649298.794 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>22</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>15545</td>\n",
       "      <td>15853</td>\n",
       "      <td>-0.019428</td>\n",
       "      <td>995.0</td>\n",
       "      <td>2945.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15545.0</td>\n",
       "      <td>POLYGON ((618676.633 4843651.507, 618660.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>37</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>16936</td>\n",
       "      <td>15004</td>\n",
       "      <td>0.128766</td>\n",
       "      <td>695.0</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3370.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>16935.0</td>\n",
       "      <td>POLYGON ((625533.338 4846776.345, 625525.472 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>50434</td>\n",
       "      <td>45041</td>\n",
       "      <td>0.119735</td>\n",
       "      <td>2290.0</td>\n",
       "      <td>5920.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6270.0</td>\n",
       "      <td>1445.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50435.0</td>\n",
       "      <td>POLYGON ((627795.153 4847184.892, 627788.963 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id                        neighbourhood  \\\n",
       "0                  96                          Long Branch   \n",
       "1                  95                          New Toronto   \n",
       "2                 109                            Alderwood   \n",
       "3                  64  Mimico (includes Humber Bay Shores)   \n",
       "4                 103           Islington-City Centre West   \n",
       "..                ...                                  ...   \n",
       "135               126                              Malvern   \n",
       "136               133                      Agincourt North   \n",
       "137                22                              Steeles   \n",
       "138                37                                Rouge   \n",
       "139                51                             Milliken   \n",
       "\n",
       "     demographics_pop2016_2008  demographics_pop2011_2008  \\\n",
       "0                        10968                      10487   \n",
       "1                        30526                      29177   \n",
       "2                         9955                       9851   \n",
       "3                        12541                      11703   \n",
       "4                        15179                      15070   \n",
       "..                         ...                        ...   \n",
       "135                      25003                      24363   \n",
       "136                      13362                      13093   \n",
       "137                      15545                      15853   \n",
       "138                      16936                      15004   \n",
       "139                      50434                      45041   \n",
       "\n",
       "     demographics_popchg11t16_2008  demographics_0_to_04_years_2016  \\\n",
       "0                         0.045866                            355.0   \n",
       "1                         0.046235                            925.0   \n",
       "2                         0.010557                            505.0   \n",
       "3                         0.071606                            905.0   \n",
       "4                         0.007233                            675.0   \n",
       "..                             ...                              ...   \n",
       "135                       0.026269                           1365.0   \n",
       "136                       0.020545                            655.0   \n",
       "137                      -0.019428                            995.0   \n",
       "138                       0.128766                            695.0   \n",
       "139                       0.119735                           2290.0   \n",
       "\n",
       "     demographics_0_to_14_years_2016  demographics_05_to_09_years_2016  \\\n",
       "0                             1200.0                             400.0   \n",
       "1                             2360.0                             760.0   \n",
       "2                             1490.0                             500.0   \n",
       "3                             2325.0                             820.0   \n",
       "4                             2845.0                             990.0   \n",
       "..                               ...                               ...   \n",
       "135                           4250.0                            1470.0   \n",
       "136                           2150.0                             720.0   \n",
       "137                           2945.0                            1035.0   \n",
       "138                           1785.0                             600.0   \n",
       "139                           5920.0                            1930.0   \n",
       "\n",
       "     demographics_10_to_14_years_2016  demographics_100_years_and_over_2016  \\\n",
       "0                               445.0                                   0.0   \n",
       "1                               675.0                                  20.0   \n",
       "2                               490.0                                   0.0   \n",
       "3                               600.0                                   0.0   \n",
       "4                              1175.0                                   0.0   \n",
       "..                                ...                                   ...   \n",
       "135                            1415.0                                   5.0   \n",
       "136                             780.0                                   5.0   \n",
       "137                             920.0                                   0.0   \n",
       "138                             500.0                                  10.0   \n",
       "139                            1710.0                                   5.0   \n",
       "\n",
       "     ...  demographics_65_years_and_over_2016  \\\n",
       "0    ...                               2675.0   \n",
       "1    ...                               5910.0   \n",
       "2    ...                               1325.0   \n",
       "3    ...                               1380.0   \n",
       "4    ...                               2235.0   \n",
       "..   ...                                  ...   \n",
       "135  ...                               3780.0   \n",
       "136  ...                               2385.0   \n",
       "137  ...                               2160.0   \n",
       "138  ...                               3370.0   \n",
       "139  ...                               6270.0   \n",
       "\n",
       "     demographics_70_to_74_years_2016  demographics_75_to_79_years_2016  \\\n",
       "0                               665.0                             470.0   \n",
       "1                              1335.0                            1030.0   \n",
       "2                               320.0                             240.0   \n",
       "3                               310.0                             225.0   \n",
       "4                               560.0                             335.0   \n",
       "..                                ...                               ...   \n",
       "135                             735.0                             590.0   \n",
       "136                             665.0                             475.0   \n",
       "137                             440.0                             455.0   \n",
       "138                             665.0                             600.0   \n",
       "139                            1445.0                            1130.0   \n",
       "\n",
       "     demographics_80_to_84_years_2016  demographics_85_to_89_years_2016  \\\n",
       "0                               305.0                             255.0   \n",
       "1                               750.0                             570.0   \n",
       "2                               175.0                             105.0   \n",
       "3                               165.0                             125.0   \n",
       "4                               275.0                             205.0   \n",
       "..                                ...                               ...   \n",
       "135                             570.0                             440.0   \n",
       "136                             240.0                             115.0   \n",
       "137                             320.0                             180.0   \n",
       "138                             525.0                             405.0   \n",
       "139                             780.0                             555.0   \n",
       "\n",
       "     demographics_85_years_and_over_2016  demographics_90_to_94_years_2016  \\\n",
       "0                                  395.0                             100.0   \n",
       "1                                 1040.0                             340.0   \n",
       "2                                  165.0                              45.0   \n",
       "3                                  170.0                              45.0   \n",
       "4                                  315.0                             100.0   \n",
       "..                                   ...                               ...   \n",
       "135                                790.0                             265.0   \n",
       "136                                160.0                              35.0   \n",
       "137                                260.0                              70.0   \n",
       "138                                655.0                             205.0   \n",
       "139                                830.0                             230.0   \n",
       "\n",
       "     demographics_95_to_99_years_2016  demographics_all_ages_2016  \\\n",
       "0                                35.0                     10965.0   \n",
       "1                               110.0                     30530.0   \n",
       "2                                10.0                      9955.0   \n",
       "3                                 5.0                     12540.0   \n",
       "4                                20.0                     15170.0   \n",
       "..                                ...                         ...   \n",
       "135                              75.0                     25000.0   \n",
       "136                              10.0                     13365.0   \n",
       "137                              10.0                     15545.0   \n",
       "138                              40.0                     16935.0   \n",
       "139                              40.0                     50435.0   \n",
       "\n",
       "                                              geometry  \n",
       "0    POLYGON ((627795.397 4836876.867, 627781.902 4...  \n",
       "1    POLYGON ((629463.304 4836332.300, 629324.313 4...  \n",
       "2    POLYGON ((624110.361 4837657.166, 624091.125 4...  \n",
       "3    POLYGON ((635860.212 4836239.139, 635719.852 4...  \n",
       "4    POLYGON ((628032.302 4840233.689, 627977.197 4...  \n",
       "..                                                 ...  \n",
       "135  POLYGON ((637264.757 4848044.207, 637331.396 4...  \n",
       "136  POLYGON ((649297.450 4847596.814, 649298.794 4...  \n",
       "137  POLYGON ((618676.633 4843651.507, 618660.054 4...  \n",
       "138  POLYGON ((625533.338 4846776.345, 625525.472 4...  \n",
       "139  POLYGON ((627795.153 4847184.892, 627788.963 4...  \n",
       "\n",
       "[140 rows x 31 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = read_data(f'{dist_folder}/by-dataset/geohoods_to_demographics.geojson')\n",
    "gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "391a93c3-fc29-49d0-b3aa-f8eefc3d1e75",
   "metadata": {},
   "source": [
    "### Spatial Binning (Counts Only)\n",
    "\n",
    "Spatially bin counted column values into each neighbourhood using the cleaned datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b28aed67-268d-4310-a465-833786d5d078",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing autospeed_enforcement - spatial binning (2022-02-17 21:37:49.800641)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed autospeed_enforcement - spatial binning (2022-02-17 21:37:50.068664) [Elapsed: 0:00:00.268023]\n",
      "Processing red_light_cams - spatial binning (2022-02-17 21:37:50.078157)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed red_light_cams - spatial binning (2022-02-17 21:37:50.339099) [Elapsed: 0:00:00.260941]\n",
      "Processing police - spatial binning (2022-02-17 21:37:50.344476)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed police - spatial binning (2022-02-17 21:37:50.572902) [Elapsed: 0:00:00.228425]\n",
      "Processing ambulance - spatial binning (2022-02-17 21:37:50.578392)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed ambulance - spatial binning (2022-02-17 21:37:50.811492) [Elapsed: 0:00:00.233098]\n",
      "Processing fire_hydrants - spatial binning (2022-02-17 21:37:51.706959)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed fire_hydrants - spatial binning (2022-02-17 21:37:51.992696) [Elapsed: 0:00:00.285735]\n",
      "Processing fire_stations - spatial binning (2022-02-17 21:37:52.009405)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed fire_stations - spatial binning (2022-02-17 21:37:52.303681) [Elapsed: 0:00:00.294277]\n",
      "Processing renewables - spatial binning (2022-02-17 21:37:52.311176)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed renewables - spatial binning (2022-02-17 21:37:52.560293) [Elapsed: 0:00:00.249116]\n",
      "Processing bicycle_parking - spatial binning (2022-02-17 21:37:53.086852)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed bicycle_parking - spatial binning (2022-02-17 21:37:53.332774) [Elapsed: 0:00:00.245921]\n",
      "Processing transit_shelters - spatial binning (2022-02-17 21:37:53.472118)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed transit_shelters - spatial binning (2022-02-17 21:37:53.716833) [Elapsed: 0:00:00.244711]\n",
      "Processing wayfind - spatial binning (2022-02-17 21:37:53.731085)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed wayfind - spatial binning (2022-02-17 21:37:53.983837) [Elapsed: 0:00:00.252753]\n",
      "Processing litter - spatial binning (2022-02-17 21:37:54.200657)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed litter - spatial binning (2022-02-17 21:37:54.444512) [Elapsed: 0:00:00.243855]\n",
      "Processing art - spatial binning (2022-02-17 21:37:54.462257)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed art - spatial binning (2022-02-17 21:37:54.719078) [Elapsed: 0:00:00.256820]\n",
      "Processing culture - spatial binning (2022-02-17 21:37:54.742498)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed culture - spatial binning (2022-02-17 21:37:54.973334) [Elapsed: 0:00:00.230835]\n",
      "Processing religious - spatial binning (2022-02-17 21:37:55.007398)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed religious - spatial binning (2022-02-17 21:37:55.249479) [Elapsed: 0:00:00.242080]\n"
     ]
    }
   ],
   "source": [
    "to_count = [\n",
    "    'autospeed_enforcement',\n",
    "    'red_light_cams',\n",
    "    'police',\n",
    "    'ambulance',\n",
    "    'fire_hydrants',\n",
    "    'fire_stations',\n",
    "    'renewables',\n",
    "    'bicycle_parking',\n",
    "    'transit_shelters',\n",
    "    'wayfind',\n",
    "    'litter',\n",
    "    'art',\n",
    "    'culture',\n",
    "    'religious'\n",
    "]\n",
    "for name in to_count:\n",
    "    \n",
    "    # Read data\n",
    "    data = read_data(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "\n",
    "    # Only process if dataset file not created\n",
    "    file = Path(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson')\n",
    "    if not file.is_file() or force:\n",
    "\n",
    "        # Start processing\n",
    "        timer = Timer(name, 'spatial binning')\n",
    "        timer.start()\n",
    "\n",
    "        # Spatial join to grid\n",
    "        join = nbhoods.sjoin(data, predicate='intersects')\n",
    "\n",
    "        # Agg counts\n",
    "        counts = join.groupby(join.index).size()\n",
    "        counts_col = f'{name}_count'\n",
    "        counts.name = counts_col\n",
    "\n",
    "        # Add counts to nbhoods\n",
    "        gdf = nbhoods.join(counts)\n",
    "        gdf[counts_col] = gdf[counts_col].fillna(0)\n",
    "\n",
    "        # Save by dataset\n",
    "        gdf.to_file(file)\n",
    "\n",
    "        # Save by variable\n",
    "        save_vars(gdf)\n",
    "\n",
    "        # End processing\n",
    "        timer.end()\n",
    "\n",
    "    else:\n",
    "        print(f'Skipping {file} - already binned ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a13c5a-301a-45eb-8402-cd43ef5b9d67",
   "metadata": {},
   "source": [
    "Preview a spatially binned count dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3c9dd57e-bab8-4b17-a7d7-f3a3e41ac344",
   "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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>autospeed_enforcement_count</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((627795.397 4836876.867, 627781.902 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>1.0</td>\n",
       "      <td>POLYGON ((629463.304 4836332.300, 629324.313 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>1.0</td>\n",
       "      <td>POLYGON ((624110.361 4837657.166, 624091.125 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((635860.212 4836239.139, 635719.852 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((628032.302 4840233.689, 627977.197 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>126</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>1.0</td>\n",
       "      <td>POLYGON ((637264.757 4848044.207, 637331.396 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>133</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((649297.450 4847596.814, 649298.794 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>22</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>1.0</td>\n",
       "      <td>POLYGON ((618676.633 4843651.507, 618660.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>37</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>2.0</td>\n",
       "      <td>POLYGON ((625533.338 4846776.345, 625525.472 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((627795.153 4847184.892, 627788.963 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  \\\n",
       "0                  96                 Long Branch   \n",
       "1                  95                 New Toronto   \n",
       "2                 109                   Alderwood   \n",
       "3                  64                      Mimico   \n",
       "4                 103  Islington-City Centre West   \n",
       "..                ...                         ...   \n",
       "135               126                     Malvern   \n",
       "136               133             Agincourt North   \n",
       "137                22                     Steeles   \n",
       "138                37                       Rouge   \n",
       "139                51                    Milliken   \n",
       "\n",
       "     autospeed_enforcement_count  \\\n",
       "0                            0.0   \n",
       "1                            1.0   \n",
       "2                            1.0   \n",
       "3                            0.0   \n",
       "4                            0.0   \n",
       "..                           ...   \n",
       "135                          1.0   \n",
       "136                          0.0   \n",
       "137                          1.0   \n",
       "138                          2.0   \n",
       "139                          0.0   \n",
       "\n",
       "                                              geometry  \n",
       "0    POLYGON ((627795.397 4836876.867, 627781.902 4...  \n",
       "1    POLYGON ((629463.304 4836332.300, 629324.313 4...  \n",
       "2    POLYGON ((624110.361 4837657.166, 624091.125 4...  \n",
       "3    POLYGON ((635860.212 4836239.139, 635719.852 4...  \n",
       "4    POLYGON ((628032.302 4840233.689, 627977.197 4...  \n",
       "..                                                 ...  \n",
       "135  POLYGON ((637264.757 4848044.207, 637331.396 4...  \n",
       "136  POLYGON ((649297.450 4847596.814, 649298.794 4...  \n",
       "137  POLYGON ((618676.633 4843651.507, 618660.054 4...  \n",
       "138  POLYGON ((625533.338 4846776.345, 625525.472 4...  \n",
       "139  POLYGON ((627795.153 4847184.892, 627788.963 4...  \n",
       "\n",
       "[140 rows x 4 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preview = gpd.read_file(f'{dist_folder}/by-dataset/geohoods_to_autospeed_enforcement.geojson')\n",
    "preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0bc6ce59-d20f-4065-8f8c-d440a09ee6b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview.plot(\n",
    "    column='autospeed_enforcement_count',\n",
    "    edgecolor='gray',\n",
    "    legend=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9b1e35-1eda-4c8f-a74d-70467b225e11",
   "metadata": {},
   "source": [
    "### Spatial Binning (Aggregates)\n",
    "\n",
    "Spatially bin aggregate column values into each neighbourhood using the cleaned datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322950ed-581b-4050-942c-da29757ae00d",
   "metadata": {},
   "source": [
    "#### centrelines\n",
    "\n",
    "Bin centreline geometry calculations and features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8c90ff67-9da5-47d2-a70c-f7ea451a54fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing centrelines - spatial binning (2022-02-17 21:37:58.690525)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed centrelines - spatial binning (2022-02-17 21:38:14.242541) [Elapsed: 0:00:15.552016]\n"
     ]
    }
   ],
   "source": [
    "name = 'centrelines'\n",
    "data = read_data(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "\n",
    "# Only process if dataset file not created\n",
    "file = Path(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "\n",
    "    # Start processing\n",
    "    timer = Timer(name, 'spatial binning')\n",
    "    timer.start()\n",
    "\n",
    "    # Split lines at polygons using overlay\n",
    "    overlay = gpd.overlay(\n",
    "        data,\n",
    "        nbhoods,\n",
    "        how='intersection'\n",
    "    ).explode(index_parts=False)\n",
    "\n",
    "    # Add sinuosity metric (how curved lines are)\n",
    "    overlay.insert(\n",
    "        len(overlay.columns) - 2, # insert before geometry\n",
    "        'sin',\n",
    "        lsin(overlay.geometry)\n",
    "    )\n",
    "\n",
    "    # Spatial join lines to each nbhood\n",
    "    join = overlay.sjoin(nbhoods, predicate='intersects')\n",
    "\n",
    "    # Aggregate cols by nbhood\n",
    "    group = join.groupby(join['index_right'])\n",
    "    agg = group.agg({\n",
    "        'centrelines_feature_code': [mode],\n",
    "        'centrelines_oneway_dir_code': [mode],\n",
    "        'geometry': [\n",
    "            ('len_sum', glen('sum')),\n",
    "            ('len_mean', glen('mean')),\n",
    "            ('len_median', glen('median')),\n",
    "            ('len_min', glen('min')),\n",
    "            ('len_max', glen('max'))\n",
    "        ],\n",
    "        'sin': genstats\n",
    "    })\n",
    "    \n",
    "    # Rename agg cols\n",
    "    agg.columns = ['_'.join(c).strip().replace('geometry_', '') for c in agg.columns]\n",
    "    agg.columns = [f'centrelines_{c}' if not c.startswith('centrelines_') else c for c in agg.columns]\n",
    "    \n",
    "    # Count freq\n",
    "    freq = []\n",
    "    for c in ['centrelines_feature_code', 'centrelines_oneway_dir_code']:\n",
    "        \n",
    "        # Get possible unique values in col\n",
    "        possible = data[c].unique()\n",
    "        \n",
    "        # Count freq for each unique value\n",
    "        f = group.apply(ucounts, c=c, possible=possible)\n",
    "        \n",
    "        # Rename freq cols\n",
    "        f = format_cols(f)\n",
    "        prefix = c.replace(f'{name}_', '')\n",
    "        f.columns = [f'centrelines_{prefix}_{c}_count' for c in f.columns]\n",
    "        freq.append(f)\n",
    "    \n",
    "    # Combine count freq\n",
    "    freq = pd.concat(freq, axis=1)\n",
    "    freq.index = freq.index.get_level_values(0)\n",
    "\n",
    "    # Add agg and freq to nbhoods\n",
    "    gdf = nbhoods.join(agg)\n",
    "    gdf = gdf.join(freq)\n",
    "\n",
    "    # Save by dataset\n",
    "    gdf.to_file(file)\n",
    "\n",
    "    # Save by variable\n",
    "    save_vars(gdf)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    print(f'Skipping {file} - already binned ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8196dad-6ab2-45ba-8151-a5b14a14ee70",
   "metadata": {},
   "source": [
    "Preview results of spatial binning `centrelines`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8f7611c7-9f65-484e-9dbf-c7c87daf1d7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>centrelines_feature_code_mode</th>\n",
       "      <th>centrelines_oneway_dir_code_mode</th>\n",
       "      <th>centrelines_len_sum</th>\n",
       "      <th>centrelines_len_mean</th>\n",
       "      <th>centrelines_len_median</th>\n",
       "      <th>centrelines_len_min</th>\n",
       "      <th>centrelines_len_max</th>\n",
       "      <th>centrelines_sin_sum</th>\n",
       "      <th>...</th>\n",
       "      <th>centrelines_feature_code_collector_ramp_count</th>\n",
       "      <th>centrelines_feature_code_minor_arterial_ramp_count</th>\n",
       "      <th>centrelines_feature_code_minor_railway_count</th>\n",
       "      <th>centrelines_feature_code_other_ramp_count</th>\n",
       "      <th>centrelines_feature_code_busway_count</th>\n",
       "      <th>centrelines_feature_code_creektributary_count</th>\n",
       "      <th>centrelines_oneway_dir_code_not_oneway_count</th>\n",
       "      <th>centrelines_oneway_dir_code_againstdigitization_count</th>\n",
       "      <th>centrelines_oneway_dir_code_followdigitization_count</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>35051.442648</td>\n",
       "      <td>81.137599</td>\n",
       "      <td>53.692545</td>\n",
       "      <td>3.053540e-07</td>\n",
       "      <td>566.490593</td>\n",
       "      <td>4.607221e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>POLYGON ((627795.397 4836876.867, 627781.902 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>56681.766327</td>\n",
       "      <td>70.675519</td>\n",
       "      <td>50.433971</td>\n",
       "      <td>5.388018e-08</td>\n",
       "      <td>371.531523</td>\n",
       "      <td>8.174940e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>648</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "      <td>POLYGON ((629463.304 4836332.300, 629324.313 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>29269.318647</td>\n",
       "      <td>70.358939</td>\n",
       "      <td>51.057950</td>\n",
       "      <td>5.534725e-08</td>\n",
       "      <td>687.716232</td>\n",
       "      <td>4.406520e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>377</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>POLYGON ((624110.361 4837657.166, 624091.125 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>28677.530209</td>\n",
       "      <td>72.418006</td>\n",
       "      <td>61.771523</td>\n",
       "      <td>3.140699e-07</td>\n",
       "      <td>716.821208</td>\n",
       "      <td>4.054116e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>389</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((635860.212 4836239.139, 635719.852 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>51390.629974</td>\n",
       "      <td>90.956867</td>\n",
       "      <td>62.125844</td>\n",
       "      <td>1.794472e-07</td>\n",
       "      <td>494.744502</td>\n",
       "      <td>6.143607e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>558</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((628032.302 4840233.689, 627977.197 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>126</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>76548.683393</td>\n",
       "      <td>126.317959</td>\n",
       "      <td>92.400608</td>\n",
       "      <td>3.671919e-07</td>\n",
       "      <td>1522.464650</td>\n",
       "      <td>6.113254e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>573</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>POLYGON ((637264.757 4848044.207, 637331.396 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>133</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>83447.795992</td>\n",
       "      <td>100.539513</td>\n",
       "      <td>51.111622</td>\n",
       "      <td>1.442228e-07</td>\n",
       "      <td>1010.397879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>757</td>\n",
       "      <td>25</td>\n",
       "      <td>48</td>\n",
       "      <td>POLYGON ((649297.450 4847596.814, 649298.794 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>22</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>48156.722725</td>\n",
       "      <td>105.375761</td>\n",
       "      <td>60.938086</td>\n",
       "      <td>6.060086e-08</td>\n",
       "      <td>1363.843161</td>\n",
       "      <td>4.709698e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>442</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON ((618676.633 4843651.507, 618660.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>37</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>44449.734692</td>\n",
       "      <td>92.411091</td>\n",
       "      <td>70.146460</td>\n",
       "      <td>8.175638e-09</td>\n",
       "      <td>794.301394</td>\n",
       "      <td>1.571978e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>481</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((625533.338 4846776.345, 625525.472 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>Local</td>\n",
       "      <td>Not One-Way</td>\n",
       "      <td>85224.212079</td>\n",
       "      <td>87.679231</td>\n",
       "      <td>56.912154</td>\n",
       "      <td>8.175638e-09</td>\n",
       "      <td>1877.720821</td>\n",
       "      <td>1.011452e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>908</td>\n",
       "      <td>13</td>\n",
       "      <td>51</td>\n",
       "      <td>POLYGON ((627795.153 4847184.892, 627788.963 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  \\\n",
       "0                  96                 Long Branch   \n",
       "1                  95                 New Toronto   \n",
       "2                 109                   Alderwood   \n",
       "3                  64                      Mimico   \n",
       "4                 103  Islington-City Centre West   \n",
       "..                ...                         ...   \n",
       "135               126                     Malvern   \n",
       "136               133             Agincourt North   \n",
       "137                22                     Steeles   \n",
       "138                37                       Rouge   \n",
       "139                51                    Milliken   \n",
       "\n",
       "    centrelines_feature_code_mode centrelines_oneway_dir_code_mode  \\\n",
       "0                           Local                      Not One-Way   \n",
       "1                           Local                      Not One-Way   \n",
       "2                           Local                      Not One-Way   \n",
       "3                           Local                      Not One-Way   \n",
       "4                           Local                      Not One-Way   \n",
       "..                            ...                              ...   \n",
       "135                         Local                      Not One-Way   \n",
       "136                         Local                      Not One-Way   \n",
       "137                         Local                      Not One-Way   \n",
       "138                         Local                      Not One-Way   \n",
       "139                         Local                      Not One-Way   \n",
       "\n",
       "     centrelines_len_sum  centrelines_len_mean  centrelines_len_median  \\\n",
       "0           35051.442648             81.137599               53.692545   \n",
       "1           56681.766327             70.675519               50.433971   \n",
       "2           29269.318647             70.358939               51.057950   \n",
       "3           28677.530209             72.418006               61.771523   \n",
       "4           51390.629974             90.956867               62.125844   \n",
       "..                   ...                   ...                     ...   \n",
       "135         76548.683393            126.317959               92.400608   \n",
       "136         83447.795992            100.539513               51.111622   \n",
       "137         48156.722725            105.375761               60.938086   \n",
       "138         44449.734692             92.411091               70.146460   \n",
       "139         85224.212079             87.679231               56.912154   \n",
       "\n",
       "     centrelines_len_min  centrelines_len_max  centrelines_sin_sum  ...  \\\n",
       "0           3.053540e-07           566.490593         4.607221e+02  ...   \n",
       "1           5.388018e-08           371.531523         8.174940e+02  ...   \n",
       "2           5.534725e-08           687.716232         4.406520e+02  ...   \n",
       "3           3.140699e-07           716.821208         4.054116e+02  ...   \n",
       "4           1.794472e-07           494.744502         6.143607e+02  ...   \n",
       "..                   ...                  ...                  ...  ...   \n",
       "135         3.671919e-07          1522.464650         6.113254e+02  ...   \n",
       "136         1.442228e-07          1010.397879                  NaN  ...   \n",
       "137         6.060086e-08          1363.843161         4.709698e+02  ...   \n",
       "138         8.175638e-09           794.301394         1.571978e+08  ...   \n",
       "139         8.175638e-09          1877.720821         1.011452e+03  ...   \n",
       "\n",
       "     centrelines_feature_code_collector_ramp_count  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "..                                             ...   \n",
       "135                                              0   \n",
       "136                                              3   \n",
       "137                                              0   \n",
       "138                                              0   \n",
       "139                                              0   \n",
       "\n",
       "     centrelines_feature_code_minor_arterial_ramp_count  \\\n",
       "0                                                    0    \n",
       "1                                                    0    \n",
       "2                                                    0    \n",
       "3                                                    0    \n",
       "4                                                    0    \n",
       "..                                                 ...    \n",
       "135                                                  0    \n",
       "136                                                  0    \n",
       "137                                                  0    \n",
       "138                                                  0    \n",
       "139                                                  0    \n",
       "\n",
       "     centrelines_feature_code_minor_railway_count  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "..                                            ...   \n",
       "135                                             0   \n",
       "136                                             0   \n",
       "137                                             0   \n",
       "138                                             0   \n",
       "139                                             0   \n",
       "\n",
       "     centrelines_feature_code_other_ramp_count  \\\n",
       "0                                            0   \n",
       "1                                            0   \n",
       "2                                            0   \n",
       "3                                            0   \n",
       "4                                            0   \n",
       "..                                         ...   \n",
       "135                                          0   \n",
       "136                                          0   \n",
       "137                                          0   \n",
       "138                                          0   \n",
       "139                                          0   \n",
       "\n",
       "     centrelines_feature_code_busway_count  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "..                                     ...   \n",
       "135                                      0   \n",
       "136                                      0   \n",
       "137                                      0   \n",
       "138                                      0   \n",
       "139                                      0   \n",
       "\n",
       "     centrelines_feature_code_creektributary_count  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "..                                             ...   \n",
       "135                                              0   \n",
       "136                                              0   \n",
       "137                                              0   \n",
       "138                                              0   \n",
       "139                                              0   \n",
       "\n",
       "     centrelines_oneway_dir_code_not_oneway_count  \\\n",
       "0                                             390   \n",
       "1                                             648   \n",
       "2                                             377   \n",
       "3                                             389   \n",
       "4                                             558   \n",
       "..                                            ...   \n",
       "135                                           573   \n",
       "136                                           757   \n",
       "137                                           442   \n",
       "138                                           481   \n",
       "139                                           908   \n",
       "\n",
       "     centrelines_oneway_dir_code_againstdigitization_count  \\\n",
       "0                                                   17       \n",
       "1                                                   78       \n",
       "2                                                   16       \n",
       "3                                                    7       \n",
       "4                                                    1       \n",
       "..                                                 ...       \n",
       "135                                                  9       \n",
       "136                                                 25       \n",
       "137                                                  6       \n",
       "138                                                  0       \n",
       "139                                                 13       \n",
       "\n",
       "     centrelines_oneway_dir_code_followdigitization_count  \\\n",
       "0                                                   25      \n",
       "1                                                   76      \n",
       "2                                                   23      \n",
       "3                                                    0      \n",
       "4                                                    6      \n",
       "..                                                 ...      \n",
       "135                                                 24      \n",
       "136                                                 48      \n",
       "137                                                  9      \n",
       "138                                                  0      \n",
       "139                                                 51      \n",
       "\n",
       "                                              geometry  \n",
       "0    POLYGON ((627795.397 4836876.867, 627781.902 4...  \n",
       "1    POLYGON ((629463.304 4836332.300, 629324.313 4...  \n",
       "2    POLYGON ((624110.361 4837657.166, 624091.125 4...  \n",
       "3    POLYGON ((635860.212 4836239.139, 635719.852 4...  \n",
       "4    POLYGON ((628032.302 4840233.689, 627977.197 4...  \n",
       "..                                                 ...  \n",
       "135  POLYGON ((637264.757 4848044.207, 637331.396 4...  \n",
       "136  POLYGON ((649297.450 4847596.814, 649298.794 4...  \n",
       "137  POLYGON ((618676.633 4843651.507, 618660.054 4...  \n",
       "138  POLYGON ((625533.338 4846776.345, 625525.472 4...  \n",
       "139  POLYGON ((627795.153 4847184.892, 627788.963 4...  \n",
       "\n",
       "[140 rows x 52 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preview = gpd.read_file(f'{dist_folder}/by-dataset/geohoods_to_centrelines.geojson')\n",
    "preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "af6f17dc-2bf0-4541-9013-3f35885c4874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview.plot(\n",
    "    column='centrelines_feature_code_major_arterial_ramp_count',\n",
    "    edgecolor='gray',\n",
    "    legend=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97eb14b7-75d2-4682-886a-c7de982f5c4a",
   "metadata": {},
   "source": [
    "#### collisions\n",
    "\n",
    "Bin collision features and counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3d4ce3e7-6887-4b1d-8c18-de808e31a873",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing collisions - spatial binning (2022-02-17 21:38:30.958450)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed collisions - spatial binning (2022-02-17 21:38:35.770315) [Elapsed: 0:00:04.811864]\n"
     ]
    }
   ],
   "source": [
    "name = 'collisions'\n",
    "data = read_data(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "    \n",
    "# Only process if dataset file not created\n",
    "file = Path(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "\n",
    "    # Start processing\n",
    "    timer = Timer(name, 'spatial binning')\n",
    "    timer.start()\n",
    "    \n",
    "    # Spatial join to nbhood\n",
    "    join = nbhoods.sjoin(data, predicate='intersects')\n",
    "\n",
    "    # Agg cols by nbhood\n",
    "    group = join.groupby(join.index)\n",
    "    dtstats = [s for s in genstats if s != 'sum']\n",
    "    agg = group.agg({\n",
    "        'collisions_month': [mode],\n",
    "        'collisions_day_of_month': [mode] + dtstats,\n",
    "        'collisions_day_of_week': [mode],\n",
    "        'collisions_year': [mode] + dtstats,\n",
    "        'collisions_hour': [mode] + dtstats,\n",
    "        'collisions_fatalities': genstats,\n",
    "        'collisions_injury': genstats,\n",
    "        'collisions_ftr': genstats,\n",
    "        'collisions_pd': genstats,\n",
    "    })\n",
    "    \n",
    "    # Rename agg cols\n",
    "    agg.columns = ['_'.join(c).strip() for c in agg.columns]\n",
    "    \n",
    "    # Count freq\n",
    "    freq = []\n",
    "    for c in ['collisions_month', 'collisions_day_of_month', 'collisions_day_of_week', 'collisions_year', 'collisions_hour']:\n",
    "        \n",
    "        # Get possible unique values in col\n",
    "        if c == 'collisions_month':\n",
    "            possible = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
    "        elif c == 'collisions_day_of_month':\n",
    "            possible = [*range(1,32)]\n",
    "        elif c == 'collisions_day_of_week':\n",
    "            possible = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "        elif c == 'collisions_hour':\n",
    "            possible = [*range(0,24)]\n",
    "        else:\n",
    "            possible = data[c].unique()\n",
    "        \n",
    "        # Count freq for each unique value\n",
    "        f = group.apply(ucounts, c=c, possible=possible)\n",
    "        \n",
    "        # Rename freq cols\n",
    "        f = format_cols(f)\n",
    "        prefix = c.replace(f'{name}_', '')\n",
    "        f.columns = [f'collisions_{prefix}_{c}_count' for c in f.columns]\n",
    "        freq.append(f)\n",
    "    \n",
    "    # Combine count freq\n",
    "    freq = pd.concat(freq, axis=1)\n",
    "    freq.index = freq.index.get_level_values(0)\n",
    "    \n",
    "    # Agg counts\n",
    "    counts = group.size()\n",
    "    counts.name = 'collisions_count'\n",
    "\n",
    "    # Add agg, freq, and counts to nbhoods\n",
    "    gdf = nbhoods.join(agg)\n",
    "    gdf = gdf.join(freq)\n",
    "    gdf = gdf.join(counts)\n",
    "    gdf['collisions_count'] = gdf['collisions_count'].fillna(0)\n",
    "\n",
    "    # Save by dataset\n",
    "    gdf.to_file(file)\n",
    "\n",
    "    # Save by variable\n",
    "    save_vars(gdf)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    print(f'Skipping {file} - already binned ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb748006-a555-4b64-89bd-74ff216c462e",
   "metadata": {},
   "source": [
    "Preview results of spatial binning `collisions`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8f7004fe-7da1-4a5b-8697-6c9d69ae6788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>collisions_month_mode</th>\n",
       "      <th>collisions_day_of_month_mode</th>\n",
       "      <th>collisions_day_of_month_mean</th>\n",
       "      <th>collisions_day_of_month_min</th>\n",
       "      <th>collisions_day_of_month_max</th>\n",
       "      <th>collisions_day_of_month_median</th>\n",
       "      <th>collisions_day_of_month_var</th>\n",
       "      <th>collisions_day_of_month_skew</th>\n",
       "      <th>...</th>\n",
       "      <th>collisions_hour_16_count</th>\n",
       "      <th>collisions_hour_17_count</th>\n",
       "      <th>collisions_hour_18_count</th>\n",
       "      <th>collisions_hour_19_count</th>\n",
       "      <th>collisions_hour_20_count</th>\n",
       "      <th>collisions_hour_21_count</th>\n",
       "      <th>collisions_hour_22_count</th>\n",
       "      <th>collisions_hour_23_count</th>\n",
       "      <th>collisions_count</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>June</td>\n",
       "      <td>13</td>\n",
       "      <td>15.654795</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>15.0</td>\n",
       "      <td>77.810014</td>\n",
       "      <td>0.047411</td>\n",
       "      <td>...</td>\n",
       "      <td>320</td>\n",
       "      <td>317</td>\n",
       "      <td>292</td>\n",
       "      <td>188</td>\n",
       "      <td>153</td>\n",
       "      <td>128</td>\n",
       "      <td>111</td>\n",
       "      <td>77</td>\n",
       "      <td>4380</td>\n",
       "      <td>POLYGON ((629463.304 4836332.300, 629324.313 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>June</td>\n",
       "      <td>3</td>\n",
       "      <td>15.305447</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>15.0</td>\n",
       "      <td>76.666105</td>\n",
       "      <td>0.087921</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>1028</td>\n",
       "      <td>POLYGON ((624110.361 4837657.166, 624091.125 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>November</td>\n",
       "      <td>17</td>\n",
       "      <td>15.597163</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>16.0</td>\n",
       "      <td>76.204722</td>\n",
       "      <td>0.043858</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>136</td>\n",
       "      <td>137</td>\n",
       "      <td>72</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>37</td>\n",
       "      <td>25</td>\n",
       "      <td>2115</td>\n",
       "      <td>POLYGON ((628032.302 4840233.689, 627977.197 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Markland Wood</td>\n",
       "      <td>January</td>\n",
       "      <td>2</td>\n",
       "      <td>15.622478</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>16.0</td>\n",
       "      <td>77.615771</td>\n",
       "      <td>0.014517</td>\n",
       "      <td>...</td>\n",
       "      <td>438</td>\n",
       "      <td>544</td>\n",
       "      <td>504</td>\n",
       "      <td>387</td>\n",
       "      <td>259</td>\n",
       "      <td>218</td>\n",
       "      <td>183</td>\n",
       "      <td>118</td>\n",
       "      <td>6344</td>\n",
       "      <td>POLYGON ((641302.472 4852600.387, 641202.087 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26</td>\n",
       "      <td>Etobicoke West Mall</td>\n",
       "      <td>June</td>\n",
       "      <td>18</td>\n",
       "      <td>15.527235</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>15.0</td>\n",
       "      <td>78.801776</td>\n",
       "      <td>0.043512</td>\n",
       "      <td>...</td>\n",
       "      <td>602</td>\n",
       "      <td>621</td>\n",
       "      <td>495</td>\n",
       "      <td>307</td>\n",
       "      <td>236</td>\n",
       "      <td>179</td>\n",
       "      <td>148</td>\n",
       "      <td>126</td>\n",
       "      <td>7270</td>\n",
       "      <td>POLYGON ((620199.686 4841607.150, 620142.908 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>126</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>January</td>\n",
       "      <td>10</td>\n",
       "      <td>15.859771</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>16.0</td>\n",
       "      <td>77.724418</td>\n",
       "      <td>-0.014282</td>\n",
       "      <td>...</td>\n",
       "      <td>581</td>\n",
       "      <td>589</td>\n",
       "      <td>470</td>\n",
       "      <td>324</td>\n",
       "      <td>207</td>\n",
       "      <td>167</td>\n",
       "      <td>118</td>\n",
       "      <td>102</td>\n",
       "      <td>6468</td>\n",
       "      <td>POLYGON ((637264.757 4848044.207, 637331.396 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>133</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>November</td>\n",
       "      <td>5</td>\n",
       "      <td>15.602081</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>15.0</td>\n",
       "      <td>75.159162</td>\n",
       "      <td>0.032884</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>57</td>\n",
       "      <td>46</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>769</td>\n",
       "      <td>POLYGON ((649297.450 4847596.814, 649298.794 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>22</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>June</td>\n",
       "      <td>24</td>\n",
       "      <td>15.842645</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>16.0</td>\n",
       "      <td>76.322538</td>\n",
       "      <td>-0.019580</td>\n",
       "      <td>...</td>\n",
       "      <td>244</td>\n",
       "      <td>293</td>\n",
       "      <td>208</td>\n",
       "      <td>135</td>\n",
       "      <td>121</td>\n",
       "      <td>83</td>\n",
       "      <td>73</td>\n",
       "      <td>71</td>\n",
       "      <td>3508</td>\n",
       "      <td>POLYGON ((618676.633 4843651.507, 618660.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>37</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>January</td>\n",
       "      <td>10</td>\n",
       "      <td>15.985240</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>16.0</td>\n",
       "      <td>77.186244</td>\n",
       "      <td>-0.030662</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>127</td>\n",
       "      <td>125</td>\n",
       "      <td>73</td>\n",
       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>18</td>\n",
       "      <td>1626</td>\n",
       "      <td>POLYGON ((625533.338 4846776.345, 625525.472 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>February</td>\n",
       "      <td>19</td>\n",
       "      <td>15.762701</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>16.0</td>\n",
       "      <td>76.893972</td>\n",
       "      <td>0.014323</td>\n",
       "      <td>...</td>\n",
       "      <td>516</td>\n",
       "      <td>543</td>\n",
       "      <td>477</td>\n",
       "      <td>313</td>\n",
       "      <td>254</td>\n",
       "      <td>245</td>\n",
       "      <td>201</td>\n",
       "      <td>126</td>\n",
       "      <td>6338</td>\n",
       "      <td>POLYGON ((627795.153 4847184.892, 627788.963 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 179 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood collisions_month_mode  \\\n",
       "1                  95                 New Toronto                  June   \n",
       "2                 109                   Alderwood                  June   \n",
       "4                 103  Islington-City Centre West              November   \n",
       "5                 130               Markland Wood               January   \n",
       "7                  26         Etobicoke West Mall                  June   \n",
       "..                ...                         ...                   ...   \n",
       "135               126                     Malvern               January   \n",
       "136               133             Agincourt North              November   \n",
       "137                22                     Steeles                  June   \n",
       "138                37                       Rouge               January   \n",
       "139                51                    Milliken              February   \n",
       "\n",
       "    collisions_day_of_month_mode  collisions_day_of_month_mean  \\\n",
       "1                             13                     15.654795   \n",
       "2                              3                     15.305447   \n",
       "4                             17                     15.597163   \n",
       "5                              2                     15.622478   \n",
       "7                             18                     15.527235   \n",
       "..                           ...                           ...   \n",
       "135                           10                     15.859771   \n",
       "136                            5                     15.602081   \n",
       "137                           24                     15.842645   \n",
       "138                           10                     15.985240   \n",
       "139                           19                     15.762701   \n",
       "\n",
       "     collisions_day_of_month_min  collisions_day_of_month_max  \\\n",
       "1                              1                           31   \n",
       "2                              1                           31   \n",
       "4                              1                           31   \n",
       "5                              1                           31   \n",
       "7                              1                           31   \n",
       "..                           ...                          ...   \n",
       "135                            1                           31   \n",
       "136                            1                           31   \n",
       "137                            1                           31   \n",
       "138                            1                           31   \n",
       "139                            1                           31   \n",
       "\n",
       "     collisions_day_of_month_median  collisions_day_of_month_var  \\\n",
       "1                              15.0                    77.810014   \n",
       "2                              15.0                    76.666105   \n",
       "4                              16.0                    76.204722   \n",
       "5                              16.0                    77.615771   \n",
       "7                              15.0                    78.801776   \n",
       "..                              ...                          ...   \n",
       "135                            16.0                    77.724418   \n",
       "136                            15.0                    75.159162   \n",
       "137                            16.0                    76.322538   \n",
       "138                            16.0                    77.186244   \n",
       "139                            16.0                    76.893972   \n",
       "\n",
       "     collisions_day_of_month_skew  ...  collisions_hour_16_count  \\\n",
       "1                        0.047411  ...                       320   \n",
       "2                        0.087921  ...                        86   \n",
       "4                        0.043858  ...                       192   \n",
       "5                        0.014517  ...                       438   \n",
       "7                        0.043512  ...                       602   \n",
       "..                            ...  ...                       ...   \n",
       "135                     -0.014282  ...                       581   \n",
       "136                      0.032884  ...                        54   \n",
       "137                     -0.019580  ...                       244   \n",
       "138                     -0.030662  ...                       145   \n",
       "139                      0.014323  ...                       516   \n",
       "\n",
       "     collisions_hour_17_count  collisions_hour_18_count  \\\n",
       "1                         317                       292   \n",
       "2                         100                        77   \n",
       "4                         136                       137   \n",
       "5                         544                       504   \n",
       "7                         621                       495   \n",
       "..                        ...                       ...   \n",
       "135                       589                       470   \n",
       "136                        57                        46   \n",
       "137                       293                       208   \n",
       "138                       127                       125   \n",
       "139                       543                       477   \n",
       "\n",
       "     collisions_hour_19_count  collisions_hour_20_count  \\\n",
       "1                         188                       153   \n",
       "2                          44                        30   \n",
       "4                          72                        42   \n",
       "5                         387                       259   \n",
       "7                         307                       236   \n",
       "..                        ...                       ...   \n",
       "135                       324                       207   \n",
       "136                        38                        31   \n",
       "137                       135                       121   \n",
       "138                        73                        49   \n",
       "139                       313                       254   \n",
       "\n",
       "     collisions_hour_21_count collisions_hour_22_count  \\\n",
       "1                         128                      111   \n",
       "2                          27                       23   \n",
       "4                          44                       37   \n",
       "5                         218                      183   \n",
       "7                         179                      148   \n",
       "..                        ...                      ...   \n",
       "135                       167                      118   \n",
       "136                        31                       23   \n",
       "137                        83                       73   \n",
       "138                        38                       37   \n",
       "139                       245                      201   \n",
       "\n",
       "    collisions_hour_23_count  collisions_count  \\\n",
       "1                         77              4380   \n",
       "2                         20              1028   \n",
       "4                         25              2115   \n",
       "5                        118              6344   \n",
       "7                        126              7270   \n",
       "..                       ...               ...   \n",
       "135                      102              6468   \n",
       "136                       21               769   \n",
       "137                       71              3508   \n",
       "138                       18              1626   \n",
       "139                      126              6338   \n",
       "\n",
       "                                              geometry  \n",
       "1    POLYGON ((629463.304 4836332.300, 629324.313 4...  \n",
       "2    POLYGON ((624110.361 4837657.166, 624091.125 4...  \n",
       "4    POLYGON ((628032.302 4840233.689, 627977.197 4...  \n",
       "5    POLYGON ((641302.472 4852600.387, 641202.087 4...  \n",
       "7    POLYGON ((620199.686 4841607.150, 620142.908 4...  \n",
       "..                                                 ...  \n",
       "135  POLYGON ((637264.757 4848044.207, 637331.396 4...  \n",
       "136  POLYGON ((649297.450 4847596.814, 649298.794 4...  \n",
       "137  POLYGON ((618676.633 4843651.507, 618660.054 4...  \n",
       "138  POLYGON ((625533.338 4846776.345, 625525.472 4...  \n",
       "139  POLYGON ((627795.153 4847184.892, 627788.963 4...  \n",
       "\n",
       "[119 rows x 179 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preview = gpd.read_file(f'{dist_folder}/by-dataset/geohoods_to_collisions.geojson')\n",
    "preview = preview[preview['collisions_fatalities_mean'] > 0]\n",
    "preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "2fc61ba3-660b-43da-ae6f-2f79bcc172a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview.plot(\n",
    "    column='collisions_count',\n",
    "    edgecolor='gray',\n",
    "    legend=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57fb3ecb-175d-4784-8e67-118ec2ce7c1e",
   "metadata": {},
   "source": [
    "#### traffic\n",
    "\n",
    "Bin counts of traffic features and counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "bd7c43cc-d845-4882-866f-a7735e24a911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing traffic - spatial binning (2022-02-17 21:39:06.542712)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed traffic - spatial binning (2022-02-17 21:39:28.499273) [Elapsed: 0:00:21.956560]\n"
     ]
    }
   ],
   "source": [
    "name = 'traffic'\n",
    "data = read_data(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "    \n",
    "# Only process if dataset file not created\n",
    "file = Path(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "\n",
    "    # Start processing\n",
    "    timer = Timer(name, 'spatial binning')\n",
    "    timer.start()\n",
    "    \n",
    "    # Spatial join to nbhood\n",
    "    join = nbhoods.sjoin(data, predicate='intersects')\n",
    "\n",
    "    # Agg cols by nbhood\n",
    "    group = join.groupby(join.index)\n",
    "    agg = group.agg({\n",
    "        c: genstats for c in data.columns if c not in ['geometry', 'traffic_month', 'traffic_day_of_month', 'traffic_year']\n",
    "    })\n",
    "    \n",
    "    # Rename agg cols\n",
    "    agg.columns = ['_'.join(c).strip() for c in agg.columns]\n",
    "    \n",
    "    # Count freq\n",
    "    freq = []\n",
    "    for c in ['traffic_month', 'traffic_day_of_month', 'traffic_year']:\n",
    "        \n",
    "        # Get possible unique values in col\n",
    "        if c == 'traffic_month':\n",
    "            possible = [*range(1,32)]\n",
    "        elif c == 'traffic_day_of_month':\n",
    "            possible = [*range(1,32)]\n",
    "        else:\n",
    "            possible = data[c].unique()\n",
    "        \n",
    "        # Count freq for each unique value\n",
    "        f = group.apply(ucounts, c=c, possible=possible)\n",
    "        \n",
    "        # Rename freq cols\n",
    "        f = format_cols(f)\n",
    "        prefix = c.replace(f'{name}_', '')\n",
    "        f.columns = [f'traffic_{prefix}_{c}_count' for c in f.columns]\n",
    "        freq.append(f)\n",
    "        \n",
    "    # Combine count freq\n",
    "    freq = pd.concat(freq, axis=1)\n",
    "    freq.index = freq.index.get_level_values(0)\n",
    "    \n",
    "    # Agg counts\n",
    "    counts = group.size()\n",
    "    counts.name = 'traffic_count'\n",
    "\n",
    "    # Add agg, freq, and counts to nbhoods\n",
    "    gdf = nbhoods.join(agg)\n",
    "    gdf = gdf.join(freq)\n",
    "    gdf = gdf.join(counts)\n",
    "    gdf['traffic_count'] = gdf['traffic_count'].fillna(0)\n",
    "\n",
    "    # Save by dataset\n",
    "    gdf.to_file(file)\n",
    "\n",
    "    # Save by variable\n",
    "    save_vars(gdf)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    print(f'Skipping {file} - already binned ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f8a345-2578-4e2e-a8b2-77e5215ca417",
   "metadata": {},
   "source": [
    "Preview results of spatial binning `traffic`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8e80412f-4a9c-4424-9b77-a4346b603a31",
   "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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>traffic_sb_cars_r_sum</th>\n",
       "      <th>traffic_sb_cars_r_mean</th>\n",
       "      <th>traffic_sb_cars_r_min</th>\n",
       "      <th>traffic_sb_cars_r_max</th>\n",
       "      <th>traffic_sb_cars_r_median</th>\n",
       "      <th>traffic_sb_cars_r_var</th>\n",
       "      <th>traffic_sb_cars_r_skew</th>\n",
       "      <th>traffic_sb_cars_r_std</th>\n",
       "      <th>...</th>\n",
       "      <th>traffic_year_2015_count</th>\n",
       "      <th>traffic_year_2010_count</th>\n",
       "      <th>traffic_year_2013_count</th>\n",
       "      <th>traffic_year_2019_count</th>\n",
       "      <th>traffic_year_2017_count</th>\n",
       "      <th>traffic_year_2014_count</th>\n",
       "      <th>traffic_year_2011_count</th>\n",
       "      <th>traffic_year_2018_count</th>\n",
       "      <th>traffic_count</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>10654.0</td>\n",
       "      <td>6.882429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.581514</td>\n",
       "      <td>1.451757</td>\n",
       "      <td>8.693763</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>224</td>\n",
       "      <td>288</td>\n",
       "      <td>96</td>\n",
       "      <td>416</td>\n",
       "      <td>1548</td>\n",
       "      <td>POLYGON ((627795.397 4836876.867, 627781.902 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>26982.0</td>\n",
       "      <td>7.852736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>121.818773</td>\n",
       "      <td>2.067719</td>\n",
       "      <td>11.037154</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>416</td>\n",
       "      <td>128</td>\n",
       "      <td>364</td>\n",
       "      <td>384</td>\n",
       "      <td>224</td>\n",
       "      <td>256</td>\n",
       "      <td>608</td>\n",
       "      <td>3436</td>\n",
       "      <td>POLYGON ((629463.304 4836332.300, 629324.313 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>7684.0</td>\n",
       "      <td>9.235577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.071994</td>\n",
       "      <td>1.737992</td>\n",
       "      <td>10.299126</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>832</td>\n",
       "      <td>POLYGON ((624110.361 4837657.166, 624091.125 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>18757.0</td>\n",
       "      <td>18.317383</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>546.212953</td>\n",
       "      <td>1.724445</td>\n",
       "      <td>23.371199</td>\n",
       "      <td>...</td>\n",
       "      <td>288</td>\n",
       "      <td>96</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>96</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>192</td>\n",
       "      <td>1024</td>\n",
       "      <td>POLYGON ((635860.212 4836239.139, 635719.852 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>11307.0</td>\n",
       "      <td>6.877737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>71.194416</td>\n",
       "      <td>2.297610</td>\n",
       "      <td>8.437678</td>\n",
       "      <td>...</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>160</td>\n",
       "      <td>32</td>\n",
       "      <td>448</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>256</td>\n",
       "      <td>1644</td>\n",
       "      <td>POLYGON ((628032.302 4840233.689, 627977.197 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>126</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>36104.0</td>\n",
       "      <td>26.863095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>711.781690</td>\n",
       "      <td>2.203624</td>\n",
       "      <td>26.679237</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>160</td>\n",
       "      <td>96</td>\n",
       "      <td>160</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>192</td>\n",
       "      <td>1344</td>\n",
       "      <td>POLYGON ((637264.757 4848044.207, 637331.396 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>133</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>9849.0</td>\n",
       "      <td>11.837740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>150.237178</td>\n",
       "      <td>1.750145</td>\n",
       "      <td>12.257128</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>32</td>\n",
       "      <td>160</td>\n",
       "      <td>832</td>\n",
       "      <td>POLYGON ((649297.450 4847596.814, 649298.794 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>22</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>14583.0</td>\n",
       "      <td>20.714489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1576.138851</td>\n",
       "      <td>3.039113</td>\n",
       "      <td>39.700615</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>96</td>\n",
       "      <td>32</td>\n",
       "      <td>704</td>\n",
       "      <td>POLYGON ((618676.633 4843651.507, 618660.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>37</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>22043.0</td>\n",
       "      <td>20.260110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>733.259787</td>\n",
       "      <td>1.663488</td>\n",
       "      <td>27.078770</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>64</td>\n",
       "      <td>384</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>32</td>\n",
       "      <td>1088</td>\n",
       "      <td>POLYGON ((625533.338 4846776.345, 625525.472 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>53920.0</td>\n",
       "      <td>21.320680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2155.600842</td>\n",
       "      <td>5.364825</td>\n",
       "      <td>46.428449</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>352</td>\n",
       "      <td>705</td>\n",
       "      <td>192</td>\n",
       "      <td>352</td>\n",
       "      <td>128</td>\n",
       "      <td>160</td>\n",
       "      <td>224</td>\n",
       "      <td>2529</td>\n",
       "      <td>POLYGON ((627795.153 4847184.892, 627788.963 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 700 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood  traffic_sb_cars_r_sum  \\\n",
       "0                  96                 Long Branch                10654.0   \n",
       "1                  95                 New Toronto                26982.0   \n",
       "2                 109                   Alderwood                 7684.0   \n",
       "3                  64                      Mimico                18757.0   \n",
       "4                 103  Islington-City Centre West                11307.0   \n",
       "..                ...                         ...                    ...   \n",
       "135               126                     Malvern                36104.0   \n",
       "136               133             Agincourt North                 9849.0   \n",
       "137                22                     Steeles                14583.0   \n",
       "138                37                       Rouge                22043.0   \n",
       "139                51                    Milliken                53920.0   \n",
       "\n",
       "     traffic_sb_cars_r_mean  traffic_sb_cars_r_min  traffic_sb_cars_r_max  \\\n",
       "0                  6.882429                    0.0                   46.0   \n",
       "1                  7.852736                    0.0                   75.0   \n",
       "2                  9.235577                    0.0                   69.0   \n",
       "3                 18.317383                    0.0                  121.0   \n",
       "4                  6.877737                    0.0                   64.0   \n",
       "..                      ...                    ...                    ...   \n",
       "135               26.863095                    0.0                  222.0   \n",
       "136               11.837740                    0.0                   70.0   \n",
       "137               20.714489                    0.0                  242.0   \n",
       "138               20.260110                    0.0                  119.0   \n",
       "139               21.320680                    0.0                  407.0   \n",
       "\n",
       "     traffic_sb_cars_r_median  traffic_sb_cars_r_var  traffic_sb_cars_r_skew  \\\n",
       "0                         4.0              75.581514                1.451757   \n",
       "1                         3.0             121.818773                2.067719   \n",
       "2                         5.0             106.071994                1.737992   \n",
       "3                        10.0             546.212953                1.724445   \n",
       "4                         4.0              71.194416                2.297610   \n",
       "..                        ...                    ...                     ...   \n",
       "135                      21.0             711.781690                2.203624   \n",
       "136                       8.0             150.237178                1.750145   \n",
       "137                       6.0            1576.138851                3.039113   \n",
       "138                       8.0             733.259787                1.663488   \n",
       "139                      10.0            2155.600842                5.364825   \n",
       "\n",
       "     traffic_sb_cars_r_std  ...  traffic_year_2015_count  \\\n",
       "0                 8.693763  ...                       32   \n",
       "1                11.037154  ...                      192   \n",
       "2                10.299126  ...                       32   \n",
       "3                23.371199  ...                      288   \n",
       "4                 8.437678  ...                       64   \n",
       "..                     ...  ...                      ...   \n",
       "135              26.679237  ...                      128   \n",
       "136              12.257128  ...                        0   \n",
       "137              39.700615  ...                      128   \n",
       "138              27.078770  ...                      128   \n",
       "139              46.428449  ...                       96   \n",
       "\n",
       "     traffic_year_2010_count  traffic_year_2013_count  \\\n",
       "0                        128                        0   \n",
       "1                        416                      128   \n",
       "2                        128                       64   \n",
       "3                         96                       64   \n",
       "4                         96                      160   \n",
       "..                       ...                      ...   \n",
       "135                      160                       96   \n",
       "136                       64                       96   \n",
       "137                      128                       64   \n",
       "138                      128                       64   \n",
       "139                      352                      705   \n",
       "\n",
       "     traffic_year_2019_count  traffic_year_2017_count  \\\n",
       "0                        172                      224   \n",
       "1                        364                      384   \n",
       "2                         32                       64   \n",
       "3                         32                       96   \n",
       "4                         32                      448   \n",
       "..                       ...                      ...   \n",
       "135                      160                       64   \n",
       "136                       64                      128   \n",
       "137                       64                       32   \n",
       "138                      384                       64   \n",
       "139                      192                      352   \n",
       "\n",
       "     traffic_year_2014_count  traffic_year_2011_count  \\\n",
       "0                        288                       96   \n",
       "1                        224                      256   \n",
       "2                         32                      128   \n",
       "3                         32                       32   \n",
       "4                         64                      128   \n",
       "..                       ...                      ...   \n",
       "135                        0                      128   \n",
       "136                      128                       32   \n",
       "137                       32                       96   \n",
       "138                       64                       64   \n",
       "139                      128                      160   \n",
       "\n",
       "     traffic_year_2018_count  traffic_count  \\\n",
       "0                        416           1548   \n",
       "1                        608           3436   \n",
       "2                        128            832   \n",
       "3                        192           1024   \n",
       "4                        256           1644   \n",
       "..                       ...            ...   \n",
       "135                      192           1344   \n",
       "136                      160            832   \n",
       "137                       32            704   \n",
       "138                       32           1088   \n",
       "139                      224           2529   \n",
       "\n",
       "                                              geometry  \n",
       "0    POLYGON ((627795.397 4836876.867, 627781.902 4...  \n",
       "1    POLYGON ((629463.304 4836332.300, 629324.313 4...  \n",
       "2    POLYGON ((624110.361 4837657.166, 624091.125 4...  \n",
       "3    POLYGON ((635860.212 4836239.139, 635719.852 4...  \n",
       "4    POLYGON ((628032.302 4840233.689, 627977.197 4...  \n",
       "..                                                 ...  \n",
       "135  POLYGON ((637264.757 4848044.207, 637331.396 4...  \n",
       "136  POLYGON ((649297.450 4847596.814, 649298.794 4...  \n",
       "137  POLYGON ((618676.633 4843651.507, 618660.054 4...  \n",
       "138  POLYGON ((625533.338 4846776.345, 625525.472 4...  \n",
       "139  POLYGON ((627795.153 4847184.892, 627788.963 4...  \n",
       "\n",
       "[140 rows x 700 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preview = gpd.read_file(f'{dist_folder}/by-dataset/geohoods_to_traffic.geojson')\n",
    "preview = preview[preview['traffic_nb_cars_t_mean'] >= 0]\n",
    "preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b3c82741-81fd-4d23-85f6-c94aa593d797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview.plot(\n",
    "    column='traffic_nb_cars_t_mean',\n",
    "    edgecolor='gray',\n",
    "    legend=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af05bff-9222-4ded-8ff5-0a6b7f6dd11a",
   "metadata": {},
   "source": [
    "#### watch_your_speed\n",
    "\n",
    "Bin watch your speed program location counts and features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6492b13f-4db1-489b-84d8-d4bec8720bc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing watch_your_speed - spatial binning (2022-02-17 21:39:29.577418)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed watch_your_speed - spatial binning (2022-02-17 21:39:30.372307) [Elapsed: 0:00:00.794889]\n"
     ]
    }
   ],
   "source": [
    "name = 'watch_your_speed'\n",
    "data = read_data(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "    \n",
    "# Only process if dataset file not created\n",
    "file = Path(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "\n",
    "    # Start processing\n",
    "    timer = Timer(name, 'spatial binning')\n",
    "    timer.start()\n",
    "    \n",
    "    # Spatial join to nbhood\n",
    "    join = nbhoods.sjoin(data, predicate='intersects')\n",
    "\n",
    "    # Agg cols by nbhood\n",
    "    group = join.groupby(join.index)\n",
    "    agg = group.agg({\n",
    "        'wys_dir': [mode],\n",
    "        'wys_min_speed': [mode] + genstats,\n",
    "        'wys_speed_limit': [mode] + genstats,\n",
    "    })\n",
    "    \n",
    "    # Rename agg cols\n",
    "    agg.columns = ['_'.join(c).strip() for c in agg.columns]\n",
    "    \n",
    "    # Count freq\n",
    "    freq = []\n",
    "    for c in ['wys_dir', 'wys_min_speed', 'wys_speed_limit']:\n",
    "        \n",
    "        # Get possible unique values in col\n",
    "        possible = data[c].unique()\n",
    "        \n",
    "        # Count freq for each unique value\n",
    "        f = group.apply(ucounts, c=c, possible=possible)\n",
    "        \n",
    "        # Rename freq cols\n",
    "        f = format_cols(f)\n",
    "        prefix = c.replace(f'wys_', '')\n",
    "        f.columns = [f'wys_{prefix}_{c}_count' for c in f.columns]\n",
    "        freq.append(f)\n",
    "        \n",
    "    # Combine count freq\n",
    "    freq = pd.concat(freq, axis=1)\n",
    "    freq.index = freq.index.get_level_values(0)\n",
    "    \n",
    "    # Agg counts\n",
    "    counts = group.size()\n",
    "    counts.name = 'wys_count'\n",
    "\n",
    "    # Add agg, freq, and counts to nbhoods\n",
    "    gdf = nbhoods.join(agg)\n",
    "    gdf = gdf.join(freq)\n",
    "    gdf = gdf.join(counts)\n",
    "    gdf['wys_count'] = gdf['wys_count'].fillna(0)\n",
    "\n",
    "    # Save by dataset\n",
    "    gdf.to_file(file)\n",
    "\n",
    "    # Save by variable\n",
    "    save_vars(gdf)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    print(f'Skipping {file} - already binned ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d496a6-488e-477a-8069-f8a9039c0f01",
   "metadata": {},
   "source": [
    "Preview results of spatial binning `watch_your_speed`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "e9f79556-5419-4bf4-9f9a-4d04dc80e059",
   "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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>wys_dir_mode</th>\n",
       "      <th>wys_min_speed_mode</th>\n",
       "      <th>wys_min_speed_sum</th>\n",
       "      <th>wys_min_speed_mean</th>\n",
       "      <th>wys_min_speed_min</th>\n",
       "      <th>wys_min_speed_max</th>\n",
       "      <th>wys_min_speed_median</th>\n",
       "      <th>wys_min_speed_var</th>\n",
       "      <th>...</th>\n",
       "      <th>wys_dir_none_count</th>\n",
       "      <th>wys_min_speed_5_count</th>\n",
       "      <th>wys_min_speed_30_count</th>\n",
       "      <th>wys_min_speed_20_count</th>\n",
       "      <th>wys_speed_limit_40_count</th>\n",
       "      <th>wys_speed_limit_50_count</th>\n",
       "      <th>wys_speed_limit_30_count</th>\n",
       "      <th>wys_speed_limit_60_count</th>\n",
       "      <th>wys_count</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>NB</td>\n",
       "      <td>20</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>POLYGON ((629463.304 4836332.300, 629324.313 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>SB</td>\n",
       "      <td>30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>187.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>POLYGON ((624110.361 4837657.166, 624091.125 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>NB</td>\n",
       "      <td>5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>14.166667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>114.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>POLYGON ((635860.212 4836239.139, 635719.852 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>EB</td>\n",
       "      <td>5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((628032.302 4840233.689, 627977.197 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>Markland Wood</td>\n",
       "      <td>SB</td>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>POLYGON ((641302.472 4852600.387, 641202.087 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>94</td>\n",
       "      <td>Bayview Woods-Steeles</td>\n",
       "      <td>EB</td>\n",
       "      <td>20</td>\n",
       "      <td>55.0</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>158.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>POLYGON ((626071.503 4837537.756, 626151.708 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>97</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "      <td>NB</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>POLYGON ((629674.232 4837709.743, 629657.497 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>22</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>SB</td>\n",
       "      <td>30</td>\n",
       "      <td>140.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>POLYGON ((618676.633 4843651.507, 618660.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>37</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>WB</td>\n",
       "      <td>30</td>\n",
       "      <td>225.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>POLYGON ((625533.338 4846776.345, 625525.472 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>EB</td>\n",
       "      <td>20</td>\n",
       "      <td>325.0</td>\n",
       "      <td>18.055556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>85.702614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>POLYGON ((627795.153 4847184.892, 627788.963 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood wys_dir_mode  \\\n",
       "1                  95                 New Toronto           NB   \n",
       "2                 109                   Alderwood           SB   \n",
       "3                  64                      Mimico           NB   \n",
       "4                 103  Islington-City Centre West           EB   \n",
       "5                 130               Markland Wood           SB   \n",
       "..                ...                         ...          ...   \n",
       "133                94       Bayview Woods-Steeles           EB   \n",
       "134                97           Hillcrest Village           NB   \n",
       "137                22                     Steeles           SB   \n",
       "138                37                       Rouge           WB   \n",
       "139                51                    Milliken           EB   \n",
       "\n",
       "    wys_min_speed_mode  wys_min_speed_sum  wys_min_speed_mean  \\\n",
       "1                   20              100.0           20.000000   \n",
       "2                   30              100.0           20.000000   \n",
       "3                    5               85.0           14.166667   \n",
       "4                    5               35.0            8.750000   \n",
       "5                    5               20.0            5.000000   \n",
       "..                 ...                ...                 ...   \n",
       "133                 20               55.0           18.333333   \n",
       "134                  5                5.0            5.000000   \n",
       "137                 30              140.0           28.000000   \n",
       "138                 30              225.0           25.000000   \n",
       "139                 20              325.0           18.055556   \n",
       "\n",
       "     wys_min_speed_min  wys_min_speed_max  wys_min_speed_median  \\\n",
       "1                 20.0               20.0                  20.0   \n",
       "2                  5.0               30.0                  30.0   \n",
       "3                  5.0               30.0                  12.5   \n",
       "4                  5.0               20.0                   5.0   \n",
       "5                  5.0                5.0                   5.0   \n",
       "..                 ...                ...                   ...   \n",
       "133                5.0               30.0                  20.0   \n",
       "134                5.0                5.0                   5.0   \n",
       "137               20.0               30.0                  30.0   \n",
       "138                5.0               30.0                  30.0   \n",
       "139                5.0               30.0                  20.0   \n",
       "\n",
       "     wys_min_speed_var  ...  wys_dir_none_count  wys_min_speed_5_count  \\\n",
       "1             0.000000  ...                 0.0                    0.0   \n",
       "2           187.500000  ...                 0.0                    2.0   \n",
       "3           114.166667  ...                 0.0                    3.0   \n",
       "4            56.250000  ...                 0.0                    3.0   \n",
       "5             0.000000  ...                 0.0                    4.0   \n",
       "..                 ...  ...                 ...                    ...   \n",
       "133         158.333333  ...                 0.0                    1.0   \n",
       "134                NaN  ...                 0.0                    1.0   \n",
       "137          20.000000  ...                 0.0                    0.0   \n",
       "138          75.000000  ...                 0.0                    1.0   \n",
       "139          85.702614  ...                 0.0                    5.0   \n",
       "\n",
       "     wys_min_speed_30_count  wys_min_speed_20_count  wys_speed_limit_40_count  \\\n",
       "1                       0.0                     5.0                       0.0   \n",
       "2                       3.0                     0.0                       5.0   \n",
       "3                       1.0                     2.0                       4.0   \n",
       "4                       0.0                     1.0                       3.0   \n",
       "5                       0.0                     0.0                       4.0   \n",
       "..                      ...                     ...                       ...   \n",
       "133                     1.0                     1.0                       2.0   \n",
       "134                     0.0                     0.0                       1.0   \n",
       "137                     4.0                     1.0                       2.0   \n",
       "138                     6.0                     2.0                       6.0   \n",
       "139                     4.0                     9.0                       9.0   \n",
       "\n",
       "     wys_speed_limit_50_count  wys_speed_limit_30_count  \\\n",
       "1                         0.0                       5.0   \n",
       "2                         0.0                       0.0   \n",
       "3                         0.0                       2.0   \n",
       "4                         0.0                       1.0   \n",
       "5                         0.0                       0.0   \n",
       "..                        ...                       ...   \n",
       "133                       0.0                       1.0   \n",
       "134                       0.0                       0.0   \n",
       "137                       2.0                       1.0   \n",
       "138                       1.0                       2.0   \n",
       "139                       0.0                       9.0   \n",
       "\n",
       "    wys_speed_limit_60_count  wys_count  \\\n",
       "1                        0.0        5.0   \n",
       "2                        0.0        5.0   \n",
       "3                        0.0        6.0   \n",
       "4                        0.0        4.0   \n",
       "5                        0.0        4.0   \n",
       "..                       ...        ...   \n",
       "133                      0.0        3.0   \n",
       "134                      0.0        1.0   \n",
       "137                      0.0        5.0   \n",
       "138                      0.0        9.0   \n",
       "139                      0.0       18.0   \n",
       "\n",
       "                                              geometry  \n",
       "1    POLYGON ((629463.304 4836332.300, 629324.313 4...  \n",
       "2    POLYGON ((624110.361 4837657.166, 624091.125 4...  \n",
       "3    POLYGON ((635860.212 4836239.139, 635719.852 4...  \n",
       "4    POLYGON ((628032.302 4840233.689, 627977.197 4...  \n",
       "5    POLYGON ((641302.472 4852600.387, 641202.087 4...  \n",
       "..                                                 ...  \n",
       "133  POLYGON ((626071.503 4837537.756, 626151.708 4...  \n",
       "134  POLYGON ((629674.232 4837709.743, 629657.497 4...  \n",
       "137  POLYGON ((618676.633 4843651.507, 618660.054 4...  \n",
       "138  POLYGON ((625533.338 4846776.345, 625525.472 4...  \n",
       "139  POLYGON ((627795.153 4847184.892, 627788.963 4...  \n",
       "\n",
       "[118 rows x 45 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preview = gpd.read_file(f'{dist_folder}/by-dataset/geohoods_to_watch_your_speed.geojson')\n",
    "preview = preview[preview['wys_count'] > 0]\n",
    "preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2d3fb49b-9e73-4f4b-afac-1bafb8a2241b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview.plot(\n",
    "    column='wys_count',\n",
    "    edgecolor='gray',\n",
    "    legend=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf64269-5d9f-43ba-8ff8-a7e410b88b3e",
   "metadata": {},
   "source": [
    "#### schools\n",
    "\n",
    "Bin school location counts and features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9b25aec4-562d-444f-9f91-6b09faeb1202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing schools - spatial binning (2022-02-17 21:39:30.746059)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed schools - spatial binning (2022-02-17 21:39:31.271086) [Elapsed: 0:00:00.525026]\n"
     ]
    }
   ],
   "source": [
    "name = 'schools'\n",
    "data = read_data(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "    \n",
    "# Only process if dataset file not created\n",
    "file = Path(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "\n",
    "    # Start processing\n",
    "    timer = Timer(name, 'spatial binning')\n",
    "    timer.start()\n",
    "    \n",
    "    # Spatial join to nbhood\n",
    "    join = nbhoods.sjoin(data, predicate='intersects')\n",
    "\n",
    "    # Agg cols by nbhood\n",
    "    group = join.groupby(join.index)\n",
    "    agg = group.agg({\n",
    "        'schools_level': [mode],\n",
    "        'schools_type': [mode]\n",
    "    })\n",
    "    \n",
    "    # Rename agg cols\n",
    "    agg.columns = ['_'.join(c).strip() for c in agg.columns]\n",
    "    \n",
    "    # Count freq\n",
    "    freq = []\n",
    "    for c in ['schools_level', 'schools_type']:\n",
    "        \n",
    "        # Get possible unique values in col\n",
    "        possible = data[c].unique()\n",
    "        \n",
    "        # Count freq for each unique value\n",
    "        f = group.apply(ucounts, c=c, possible=possible)\n",
    "        \n",
    "        # Rename freq cols\n",
    "        f = format_cols(f)\n",
    "        prefix = c.replace(f'{name}_', '')\n",
    "        f.columns = [f'schools_{prefix}_{c}_count' for c in f.columns]\n",
    "        freq.append(f)\n",
    "        \n",
    "    # Combine count freq\n",
    "    freq = pd.concat(freq, axis=1)\n",
    "    freq.index = freq.index.get_level_values(0)\n",
    "    \n",
    "    # Agg counts\n",
    "    counts = group.size()\n",
    "    counts.name = 'schools_count'\n",
    "\n",
    "    # Add agg, freq, and counts to nbhoods\n",
    "    gdf = nbhoods.join(agg)\n",
    "    gdf = gdf.join(freq)\n",
    "    gdf = gdf.join(counts)\n",
    "    gdf['schools_count'] = gdf['schools_count'].fillna(0)\n",
    "\n",
    "    # Save by dataset\n",
    "    gdf.to_file(file)\n",
    "\n",
    "    # Save by variable\n",
    "    save_vars(gdf)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    print(f'Skipping {file} - already binned ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96fbef32-22b4-4add-885e-53527af3c293",
   "metadata": {},
   "source": [
    "Preview results of spatial binning `watch_your_speed`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f285d5f0-e8ea-4eac-918d-5bb79049ca3e",
   "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>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>schools_level_mode</th>\n",
       "      <th>schools_type_mode</th>\n",
       "      <th>schools_level_none_count</th>\n",
       "      <th>schools_type_private_count</th>\n",
       "      <th>schools_type_english_public_count</th>\n",
       "      <th>schools_type_english_separate_count</th>\n",
       "      <th>schools_type_none_count</th>\n",
       "      <th>schools_type_french_public_count</th>\n",
       "      <th>schools_type_french_separate_count</th>\n",
       "      <th>schools_type_university_count</th>\n",
       "      <th>schools_count</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>None</td>\n",
       "      <td>Private</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>POLYGON ((627795.397 4836876.867, 627781.902 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>New Toronto</td>\n",
       "      <td>None</td>\n",
       "      <td>Private</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>POLYGON ((629463.304 4836332.300, 629324.313 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>None</td>\n",
       "      <td>English Separate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>POLYGON ((624110.361 4837657.166, 624091.125 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>None</td>\n",
       "      <td>English Public</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>POLYGON ((635860.212 4836239.139, 635719.852 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>None</td>\n",
       "      <td>English Public</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>POLYGON ((628032.302 4840233.689, 627977.197 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>126</td>\n",
       "      <td>Malvern</td>\n",
       "      <td>None</td>\n",
       "      <td>English Public</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>POLYGON ((637264.757 4848044.207, 637331.396 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>133</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>None</td>\n",
       "      <td>English Public</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>POLYGON ((649297.450 4847596.814, 649298.794 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>22</td>\n",
       "      <td>Steeles</td>\n",
       "      <td>None</td>\n",
       "      <td>English Public</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>POLYGON ((618676.633 4843651.507, 618660.054 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>37</td>\n",
       "      <td>Rouge</td>\n",
       "      <td>None</td>\n",
       "      <td>Private</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>POLYGON ((625533.338 4846776.345, 625525.472 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>None</td>\n",
       "      <td>Private</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>POLYGON ((627795.153 4847184.892, 627788.963 4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood schools_level_mode  \\\n",
       "0                  96                 Long Branch               None   \n",
       "1                  95                 New Toronto               None   \n",
       "2                 109                   Alderwood               None   \n",
       "3                  64                      Mimico               None   \n",
       "4                 103  Islington-City Centre West               None   \n",
       "..                ...                         ...                ...   \n",
       "135               126                     Malvern               None   \n",
       "136               133             Agincourt North               None   \n",
       "137                22                     Steeles               None   \n",
       "138                37                       Rouge               None   \n",
       "139                51                    Milliken               None   \n",
       "\n",
       "    schools_type_mode  schools_level_none_count  schools_type_private_count  \\\n",
       "0             Private                       0.0                         4.0   \n",
       "1             Private                       0.0                        10.0   \n",
       "2    English Separate                       0.0                         1.0   \n",
       "3      English Public                       0.0                         0.0   \n",
       "4      English Public                       0.0                         2.0   \n",
       "..                ...                       ...                         ...   \n",
       "135    English Public                       0.0                         1.0   \n",
       "136    English Public                       0.0                         0.0   \n",
       "137    English Public                       0.0                         3.0   \n",
       "138           Private                       0.0                         4.0   \n",
       "139           Private                       0.0                        10.0   \n",
       "\n",
       "     schools_type_english_public_count  schools_type_english_separate_count  \\\n",
       "0                                  2.0                                  0.0   \n",
       "1                                  3.0                                  1.0   \n",
       "2                                  2.0                                  2.0   \n",
       "3                                  2.0                                  0.0   \n",
       "4                                  5.0                                  0.0   \n",
       "..                                 ...                                  ...   \n",
       "135                                5.0                                  1.0   \n",
       "136                                3.0                                  1.0   \n",
       "137                                4.0                                  1.0   \n",
       "138                                3.0                                  1.0   \n",
       "139                                7.0                                  2.0   \n",
       "\n",
       "     schools_type_none_count  schools_type_french_public_count  \\\n",
       "0                        0.0                               0.0   \n",
       "1                        0.0                               0.0   \n",
       "2                        0.0                               0.0   \n",
       "3                        0.0                               0.0   \n",
       "4                        0.0                               0.0   \n",
       "..                       ...                               ...   \n",
       "135                      0.0                               0.0   \n",
       "136                      0.0                               0.0   \n",
       "137                      0.0                               0.0   \n",
       "138                      0.0                               0.0   \n",
       "139                      0.0                               0.0   \n",
       "\n",
       "     schools_type_french_separate_count  schools_type_university_count  \\\n",
       "0                                   0.0                            0.0   \n",
       "1                                   0.0                            0.0   \n",
       "2                                   0.0                            0.0   \n",
       "3                                   1.0                            0.0   \n",
       "4                                   0.0                            0.0   \n",
       "..                                  ...                            ...   \n",
       "135                                 0.0                            0.0   \n",
       "136                                 1.0                            0.0   \n",
       "137                                 0.0                            0.0   \n",
       "138                                 0.0                            0.0   \n",
       "139                                 0.0                            0.0   \n",
       "\n",
       "     schools_count                                           geometry  \n",
       "0              7.0  POLYGON ((627795.397 4836876.867, 627781.902 4...  \n",
       "1             14.0  POLYGON ((629463.304 4836332.300, 629324.313 4...  \n",
       "2              5.0  POLYGON ((624110.361 4837657.166, 624091.125 4...  \n",
       "3              3.0  POLYGON ((635860.212 4836239.139, 635719.852 4...  \n",
       "4              7.0  POLYGON ((628032.302 4840233.689, 627977.197 4...  \n",
       "..             ...                                                ...  \n",
       "135            8.0  POLYGON ((637264.757 4848044.207, 637331.396 4...  \n",
       "136            5.0  POLYGON ((649297.450 4847596.814, 649298.794 4...  \n",
       "137            8.0  POLYGON ((618676.633 4843651.507, 618660.054 4...  \n",
       "138            8.0  POLYGON ((625533.338 4846776.345, 625525.472 4...  \n",
       "139           19.0  POLYGON ((627795.153 4847184.892, 627788.963 4...  \n",
       "\n",
       "[139 rows x 14 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preview = gpd.read_file(f'{dist_folder}/by-dataset/geohoods_to_schools.geojson')\n",
    "preview = preview[preview['schools_count'] > 0]\n",
    "preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "d71a588b-b8db-4eb7-b5e5-443b45722221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview.plot(\n",
    "    column='schools_count',\n",
    "    edgecolor='gray',\n",
    "    legend=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f8022e-4b2d-4bfc-a83b-78cda6a2d109",
   "metadata": {},
   "source": [
    "#### childcare\n",
    "\n",
    "Bin childcare location counts and features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "d782c906-5335-4f84-9995-1d598373e0a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing childcare - spatial binning (2022-02-17 21:39:31.651475)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed childcare - spatial binning (2022-02-17 21:39:33.858091) [Elapsed: 0:00:02.206615]\n"
     ]
    }
   ],
   "source": [
    "name = 'childcare'\n",
    "data = read_data(f'{dist_folder}/clean-datasets/{name}.geojson')\n",
    "    \n",
    "# Only process if dataset file not created\n",
    "file = Path(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson')\n",
    "if not file.is_file() or force:\n",
    "\n",
    "    # Start processing\n",
    "    timer = Timer(name, 'spatial binning')\n",
    "    timer.start()\n",
    "    \n",
    "    # Spatial join to nbhood\n",
    "    join = nbhoods.sjoin(data, predicate='intersects')\n",
    "\n",
    "    # Agg cols by nbhood\n",
    "    group = join.groupby(join.index)\n",
    "    agg = group.agg({\n",
    "        'childcare_auspice': [mode],\n",
    "        'childcare_bldg_type': [mode],\n",
    "        'childcare_igspace': genstats,\n",
    "        'childcare_tgspace': genstats,\n",
    "        'childcare_pgspace': genstats,\n",
    "        'childcare_kgspace': genstats,\n",
    "        'childcare_sgspace': genstats,\n",
    "        'childcare_totspace': genstats,\n",
    "        'childcare_subsidy': genstats\n",
    "    })\n",
    "    \n",
    "    # Rename agg cols\n",
    "    agg.columns = ['_'.join(c).strip() for c in agg.columns]\n",
    "    \n",
    "    # Count freq\n",
    "    freq = []\n",
    "    for c in ['childcare_auspice', 'childcare_bldg_type']:\n",
    "        \n",
    "        # Get possible unique values in col\n",
    "        possible = data[c].unique()\n",
    "        \n",
    "        # Count freq for each unique value\n",
    "        f = group.apply(ucounts, c=c, possible=possible)\n",
    "        \n",
    "        # Rename freq cols\n",
    "        f = format_cols(f)\n",
    "        prefix = c.replace(f'{name}_', '')\n",
    "        f.columns = [f'childcare_{prefix}_{c}_count' for c in f.columns]\n",
    "        freq.append(f)\n",
    "        \n",
    "    # Combine count freq\n",
    "    freq = pd.concat(freq, axis=1)\n",
    "    freq.index = freq.index.get_level_values(0)\n",
    "    \n",
    "    # Agg counts\n",
    "    counts = group.size()\n",
    "    counts.name = 'childcare_count'\n",
    "\n",
    "    # Add agg, freq, and counts to nbhoods\n",
    "    gdf = nbhoods.join(agg)\n",
    "    gdf = gdf.join(freq)\n",
    "    gdf = gdf.join(counts)\n",
    "    gdf['childcare_count'] = gdf['childcare_count'].fillna(0)\n",
    "\n",
    "    # Save by dataset\n",
    "    gdf.to_file(file)\n",
    "\n",
    "    # Save by variable\n",
    "    save_vars(gdf)\n",
    "\n",
    "    # End processing\n",
    "    timer.end()\n",
    "\n",
    "else:\n",
    "    print(f'Skipping {file} - already binned ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8238c57-144b-42ff-8fe2-b4a33885eaa9",
   "metadata": {},
   "source": [
    "Preview results of spatial binning `watch_your_speed`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "bb43e8e4-556d-405b-a82b-e62787896587",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_id</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>childcare_auspice_mode</th>\n",
       "      <th>childcare_bldg_type_mode</th>\n",
       "      <th>childcare_igspace_sum</th>\n",
       "      <th>childcare_igspace_mean</th>\n",
       "      <th>childcare_igspace_min</th>\n",
       "      <th>childcare_igspace_max</th>\n",
       "      <th>childcare_igspace_median</th>\n",
       "      <th>childcare_igspace_var</th>\n",
       "      <th>...</th>\n",
       "      <th>childcare_bldg_type_multi_human_services_facility_count</th>\n",
       "      <th>childcare_bldg_type_low_rise_apartment_count</th>\n",
       "      <th>childcare_bldg_type_synagogue_count</th>\n",
       "      <th>childcare_bldg_type_private_elementary_school_count</th>\n",
       "      <th>childcare_bldg_type_communityrec_centre_aocc_count</th>\n",
       "      <th>childcare_bldg_type_communityrec_centre_private_count</th>\n",
       "      <th>childcare_bldg_type_hospitalhealth_centre_count</th>\n",
       "      <th>childcare_bldg_type_community_health_centre_count</th>\n",
       "      <th>childcare_count</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96</td>\n",
       "      <td>Long Branch</td>\n",
       "      <td>Non Profit Agency</td>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95</td>\n",
       "      <td>New Toronto</td>\n",
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       "      <td>Public Elementary School</td>\n",
       "      <td>10</td>\n",
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       "      <td>109</td>\n",
       "      <td>Alderwood</td>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Elementary School</td>\n",
       "      <td>10</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.000000</td>\n",
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       "      <td>POLYGON ((624110.361 4837657.166, 624091.125 4...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Mimico</td>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Elementary School</td>\n",
       "      <td>26</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Islington-City Centre West</td>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Elementary School</td>\n",
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       "      <td>Commercial Agency</td>\n",
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       "      <th>136</th>\n",
       "      <td>133</td>\n",
       "      <td>Agincourt North</td>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Elementary (French)</td>\n",
       "      <td>6</td>\n",
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       "      <td>Public Elementary School</td>\n",
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       "    <tr>\n",
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       "      <td>Rouge</td>\n",
       "      <td>Non Profit Agency</td>\n",
       "      <td>Public Elementary School</td>\n",
       "      <td>40</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>Milliken</td>\n",
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       "      <td>Other</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood_id               neighbourhood childcare_auspice_mode  \\\n",
       "0                  96                 Long Branch      Non Profit Agency   \n",
       "1                  95                 New Toronto      Non Profit Agency   \n",
       "2                 109                   Alderwood      Non Profit Agency   \n",
       "3                  64                      Mimico      Non Profit Agency   \n",
       "4                 103  Islington-City Centre West      Non Profit Agency   \n",
       "..                ...                         ...                    ...   \n",
       "135               126                     Malvern      Commercial Agency   \n",
       "136               133             Agincourt North      Non Profit Agency   \n",
       "137                22                     Steeles      Non Profit Agency   \n",
       "138                37                       Rouge      Non Profit Agency   \n",
       "139                51                    Milliken      Non Profit Agency   \n",
       "\n",
       "       childcare_bldg_type_mode  childcare_igspace_sum  \\\n",
       "0                         Other                     10   \n",
       "1      Public Elementary School                     10   \n",
       "2      Public Elementary School                     10   \n",
       "3      Public Elementary School                     26   \n",
       "4      Public Elementary School                      0   \n",
       "..                          ...                    ...   \n",
       "135            Place of Worship                     10   \n",
       "136  Public Elementary (French)                      6   \n",
       "137    Public Elementary School                     30   \n",
       "138    Public Elementary School                     40   \n",
       "139                       Other                     29   \n",
       "\n",
       "     childcare_igspace_mean  childcare_igspace_min  childcare_igspace_max  \\\n",
       "0                  2.000000                      0                     10   \n",
       "1                  1.000000                      0                     10   \n",
       "2                  5.000000                      0                     10   \n",
       "3                  5.200000                      0                     10   \n",
       "4                  0.000000                      0                      0   \n",
       "..                      ...                    ...                    ...   \n",
       "135                2.500000                      0                     10   \n",
       "136                1.200000                      0                      6   \n",
       "137                6.000000                      0                     10   \n",
       "138                6.666667                      0                     20   \n",
       "139                2.416667                      0                     10   \n",
       "\n",
       "     childcare_igspace_median  childcare_igspace_var  ...  \\\n",
       "0                         0.0              20.000000  ...   \n",
       "1                         0.0              10.000000  ...   \n",
       "2                         5.0              50.000000  ...   \n",
       "3                         6.0              25.200000  ...   \n",
       "4                         0.0               0.000000  ...   \n",
       "..                        ...                    ...  ...   \n",
       "135                       0.0              25.000000  ...   \n",
       "136                       0.0               7.200000  ...   \n",
       "137                      10.0              30.000000  ...   \n",
       "138                       5.0              66.666667  ...   \n",
       "139                       0.0              19.174242  ...   \n",
       "\n",
       "     childcare_bldg_type_multi_human_services_facility_count  \\\n",
       "0                                                    0         \n",
       "1                                                    0         \n",
       "2                                                    0         \n",
       "3                                                    0         \n",
       "4                                                    0         \n",
       "..                                                 ...         \n",
       "135                                                  0         \n",
       "136                                                  0         \n",
       "137                                                  0         \n",
       "138                                                  0         \n",
       "139                                                  0         \n",
       "\n",
       "     childcare_bldg_type_low_rise_apartment_count  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               1   \n",
       "4                                               0   \n",
       "..                                            ...   \n",
       "135                                             0   \n",
       "136                                             1   \n",
       "137                                             0   \n",
       "138                                             0   \n",
       "139                                             0   \n",
       "\n",
       "     childcare_bldg_type_synagogue_count  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "..                                   ...   \n",
       "135                                    0   \n",
       "136                                    0   \n",
       "137                                    0   \n",
       "138                                    0   \n",
       "139                                    0   \n",
       "\n",
       "     childcare_bldg_type_private_elementary_school_count  \\\n",
       "0                                                    0     \n",
       "1                                                    0     \n",
       "2                                                    0     \n",
       "3                                                    0     \n",
       "4                                                    0     \n",
       "..                                                 ...     \n",
       "135                                                  1     \n",
       "136                                                  0     \n",
       "137                                                  0     \n",
       "138                                                  0     \n",
       "139                                                  0     \n",
       "\n",
       "     childcare_bldg_type_communityrec_centre_aocc_count  \\\n",
       "0                                                    0    \n",
       "1                                                    0    \n",
       "2                                                    0    \n",
       "3                                                    0    \n",
       "4                                                    0    \n",
       "..                                                 ...    \n",
       "135                                                  0    \n",
       "136                                                  0    \n",
       "137                                                  0    \n",
       "138                                                  0    \n",
       "139                                                  0    \n",
       "\n",
       "     childcare_bldg_type_communityrec_centre_private_count  \\\n",
       "0                                                    0       \n",
       "1                                                    0       \n",
       "2                                                    0       \n",
       "3                                                    0       \n",
       "4                                                    0       \n",
       "..                                                 ...       \n",
       "135                                                  0       \n",
       "136                                                  0       \n",
       "137                                                  0       \n",
       "138                                                  0       \n",
       "139                                                  0       \n",
       "\n",
       "     childcare_bldg_type_hospitalhealth_centre_count  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "..                                               ...   \n",
       "135                                                0   \n",
       "136                                                0   \n",
       "137                                                0   \n",
       "138                                                0   \n",
       "139                                                0   \n",
       "\n",
       "     childcare_bldg_type_community_health_centre_count  childcare_count  \\\n",
       "0                                                    0                5   \n",
       "1                                                    0               10   \n",
       "2                                                    0                2   \n",
       "3                                                    0                5   \n",
       "4                                                    0                4   \n",
       "..                                                 ...              ...   \n",
       "135                                                  0                4   \n",
       "136                                                  0                5   \n",
       "137                                                  0                5   \n",
       "138                                                  0                6   \n",
       "139                                                  0               12   \n",
       "\n",
       "                                              geometry  \n",
       "0    POLYGON ((627795.397 4836876.867, 627781.902 4...  \n",
       "1    POLYGON ((629463.304 4836332.300, 629324.313 4...  \n",
       "2    POLYGON ((624110.361 4837657.166, 624091.125 4...  \n",
       "3    POLYGON ((635860.212 4836239.139, 635719.852 4...  \n",
       "4    POLYGON ((628032.302 4840233.689, 627977.197 4...  \n",
       "..                                                 ...  \n",
       "135  POLYGON ((637264.757 4848044.207, 637331.396 4...  \n",
       "136  POLYGON ((649297.450 4847596.814, 649298.794 4...  \n",
       "137  POLYGON ((618676.633 4843651.507, 618660.054 4...  \n",
       "138  POLYGON ((625533.338 4846776.345, 625525.472 4...  \n",
       "139  POLYGON ((627795.153 4847184.892, 627788.963 4...  \n",
       "\n",
       "[140 rows x 130 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preview = gpd.read_file(f'{dist_folder}/by-dataset/geohoods_to_childcare.geojson')\n",
    "preview = preview[preview['childcare_count'] > 0]\n",
    "preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "6b66db15-a07d-4dea-bc2d-b8d23f4c2548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preview.plot(\n",
    "    column='childcare_count',\n",
    "    edgecolor='gray',\n",
    "    legend=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95cb797-9cf3-4bc1-8eec-0c230fa916f8",
   "metadata": {},
   "source": [
    "### Combine Datasets\n",
    "\n",
    "Combine processed datasets into one final dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "49f08808-d1f7-4ec4-a143-df8c55e4e459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ../tmp/dist/by-dataset/geohoods_to_autospeed_enforcement.geojson - combining (2022-02-17 21:39:34.487886)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_autospeed_enforcement.geojson - combining (2022-02-17 21:39:34.622335) [Elapsed: 0:00:00.134450]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_centrelines.geojson - combining (2022-02-17 21:39:34.622410)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_centrelines.geojson - combining (2022-02-17 21:39:34.778736) [Elapsed: 0:00:00.156325]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_wayfind.geojson - combining (2022-02-17 21:39:34.779013)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_wayfind.geojson - combining (2022-02-17 21:39:34.924277) [Elapsed: 0:00:00.145263]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_crime.geojson - combining (2022-02-17 21:39:34.924435)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_crime.geojson - combining (2022-02-17 21:39:35.116819) [Elapsed: 0:00:00.192383]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_ambulance.geojson - combining (2022-02-17 21:39:35.116913)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_ambulance.geojson - combining (2022-02-17 21:39:35.261203) [Elapsed: 0:00:00.144289]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_litter.geojson - combining (2022-02-17 21:39:35.261279)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_litter.geojson - combining (2022-02-17 21:39:35.401699) [Elapsed: 0:00:00.140419]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_watch_your_speed.geojson - combining (2022-02-17 21:39:35.401798)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_watch_your_speed.geojson - combining (2022-02-17 21:39:35.586027) [Elapsed: 0:00:00.184227]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_red_light_cams.geojson - combining (2022-02-17 21:39:35.586135)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_red_light_cams.geojson - combining (2022-02-17 21:39:35.735045) [Elapsed: 0:00:00.148910]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_fire_stations.geojson - combining (2022-02-17 21:39:35.735173)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_fire_stations.geojson - combining (2022-02-17 21:39:35.887425) [Elapsed: 0:00:00.152250]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_civics.geojson - combining (2022-02-17 21:39:35.887525)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_civics.geojson - combining (2022-02-17 21:39:36.050264) [Elapsed: 0:00:00.162737]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_health.geojson - combining (2022-02-17 21:39:36.050355)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_health.geojson - combining (2022-02-17 21:39:36.215377) [Elapsed: 0:00:00.165020]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_transit_shelters.geojson - combining (2022-02-17 21:39:36.215480)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_transit_shelters.geojson - combining (2022-02-17 21:39:36.359414) [Elapsed: 0:00:00.143932]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_culture.geojson - combining (2022-02-17 21:39:36.359549)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_culture.geojson - combining (2022-02-17 21:39:36.505535) [Elapsed: 0:00:00.145984]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_housing.geojson - combining (2022-02-17 21:39:36.505663)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_housing.geojson - combining (2022-02-17 21:39:36.655295) [Elapsed: 0:00:00.149632]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_schools.geojson - combining (2022-02-17 21:39:36.655536)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_schools.geojson - combining (2022-02-17 21:39:36.797211) [Elapsed: 0:00:00.141673]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_police.geojson - combining (2022-02-17 21:39:36.797310)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_police.geojson - combining (2022-02-17 21:39:36.943366) [Elapsed: 0:00:00.146055]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_art.geojson - combining (2022-02-17 21:39:36.943497)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_art.geojson - combining (2022-02-17 21:39:37.092737) [Elapsed: 0:00:00.149238]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_childcare.geojson - combining (2022-02-17 21:39:37.092834)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_childcare.geojson - combining (2022-02-17 21:39:37.307737) [Elapsed: 0:00:00.214902]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_religious.geojson - combining (2022-02-17 21:39:37.308015)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_religious.geojson - combining (2022-02-17 21:39:37.452602) [Elapsed: 0:00:00.144585]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_fire_hydrants.geojson - combining (2022-02-17 21:39:37.452698)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_fire_hydrants.geojson - combining (2022-02-17 21:39:37.608427) [Elapsed: 0:00:00.155727]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_demographics.geojson - combining (2022-02-17 21:39:37.608508)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_demographics.geojson - combining (2022-02-17 21:39:37.761579) [Elapsed: 0:00:00.153069]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_education.geojson - combining (2022-02-17 21:39:37.761661)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_education.geojson - combining (2022-02-17 21:39:37.912624) [Elapsed: 0:00:00.150962]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_traffic.geojson - combining (2022-02-17 21:39:37.912806)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_traffic.geojson - combining (2022-02-17 21:39:38.805212) [Elapsed: 0:00:00.892405]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_collisions.geojson - combining (2022-02-17 21:39:38.805321)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_collisions.geojson - combining (2022-02-17 21:39:39.038723) [Elapsed: 0:00:00.233400]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_environment.geojson - combining (2022-02-17 21:39:39.038819)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_environment.geojson - combining (2022-02-17 21:39:39.179109) [Elapsed: 0:00:00.140290]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_bicycle_parking.geojson - combining (2022-02-17 21:39:39.179188)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_bicycle_parking.geojson - combining (2022-02-17 21:39:39.318647) [Elapsed: 0:00:00.139457]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_renewables.geojson - combining (2022-02-17 21:39:39.318749)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_renewables.geojson - combining (2022-02-17 21:39:39.457040) [Elapsed: 0:00:00.138291]\n",
      "Processing ../tmp/dist/by-dataset/geohoods_to_economics.geojson - combining (2022-02-17 21:39:39.457111)...\n",
      "Processed ../tmp/dist/by-dataset/geohoods_to_economics.geojson - combining (2022-02-17 21:39:39.602087) [Elapsed: 0:00:00.144975]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rrwen/Desktop/geohoods-to/tmp/linux-env/lib/python3.9/site-packages/geopandas/io/file.py:362: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  pd.Int64Index,\n"
     ]
    }
   ],
   "source": [
    "# Get dataset files\n",
    "dfiles = Path(f'{dist_folder}/by-dataset').glob('*')\n",
    "dfiles = [f for f in dfiles if f.is_file() and f.suffix == '.geojson']\n",
    "\n",
    "# Combine if not exists\n",
    "file = Path(f'{dist_folder}/geohoods_to.geojson')\n",
    "if not file.is_file() or force:\n",
    "    \n",
    "    # Read each file and combine to nbhoods\n",
    "    out = read_data(f'{dist_folder}/nbhoods.geojson')\n",
    "    for f in dfiles:\n",
    "        \n",
    "        # Start processing\n",
    "        timer = Timer(str(f), 'combining')\n",
    "        timer.start()\n",
    "        \n",
    "        # Read dataset and drop unneeded columns\n",
    "        data = read_data(f).drop('geometry', axis=1)\n",
    "        if 'neighbourhood' in data:\n",
    "            data = data.drop('neighbourhood', axis=1)\n",
    "        \n",
    "        # Combine dataset\n",
    "        out = pd.merge(\n",
    "            out,\n",
    "            data,\n",
    "            on='neighbourhood_id',\n",
    "            how='left'\n",
    "        )\n",
    "        \n",
    "        # End processing\n",
    "        timer.end()\n",
    "    \n",
    "    # Save combined datasets\n",
    "    out.to_file(file)\n",
    "    \n",
    "else:\n",
    "    print(f'Skipping {file} - already combined ({datetime.now()})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "929b8018-8c83-468f-8430-e74f5ddc6a20",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9b98ee26-ec2e-4e09-99bf-763db3877ea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed all datasets - preprocessing (2022-02-17 21:40:25.449313) [Elapsed: 0:02:40.121426]\n"
     ]
    }
   ],
   "source": [
    "gtimer.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f6442f-4165-4393-8da8-81c2bfc0432d",
   "metadata": {},
   "source": [
    "Information for the final dataset `geohoods_to.geojson`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "489f27ab-9b85-46c1-a9fc-c82f1f43deb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file: ../tmp/dist/geohoods_to.geojson\n",
      "dataset: geohoods_to\n",
      "columns: 1335\n",
      "rows: 140\n",
      "memory_usage: 1.4 MiB\n",
      "geometry: ['Polygon']\n",
      "created: 2022-02-17 21:39:39.615690\n",
      "modified: 2022-02-17 21:40:25.446136\n",
      "size: 9.0 MiB\n"
     ]
    }
   ],
   "source": [
    "out = read_data(f'{dist_folder}/geohoods_to.geojson')\n",
    "geohoods_info = dict(file=f'{dist_folder}/geohoods_to.geojson')\n",
    "geohoods_info = dict(geohoods_info, **df_info(out, 'geohoods_to'))\n",
    "geohoods_info = dict(geohoods_info, **file_info(f'{dist_folder}/geohoods_to.geojson'))\n",
    "for k, v in geohoods_info.items():\n",
    "    print(f'{k}: {v}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b583bb-8c7e-49ae-ab5f-c5fa2d69e416",
   "metadata": {},
   "source": [
    "The final dataset is also split by datasets for convenience:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "b1144bb7-965c-4ce2-b499-e4df046a889b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>file</th>\n",
       "      <th>created</th>\n",
       "      <th>modified</th>\n",
       "      <th>columns</th>\n",
       "      <th>rows</th>\n",
       "      <th>geometry</th>\n",
       "      <th>memory_usage</th>\n",
       "      <th>size</th>\n",
       "      <th>source</th>\n",
       "      <th>source_url</th>\n",
       "      <th>raw_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>centrelines</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_centrelines.geojson</td>\n",
       "      <td>2022-02-17 21:38:13.885275</td>\n",
       "      <td>2022-02-17 21:38:14.187795</td>\n",
       "      <td>52</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>57.0 KiB</td>\n",
       "      <td>2.5 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/toronto-centreline-tcl/\" target=\"_blank\">https://open.toronto.ca/dataset/toronto-centreline-tcl/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/ba71b78e-464b-46dd-8185-a0d249fe4857\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/ba71b78e-464b-46dd-8185-a0d249fe4857</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>collisions</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_collisions.geojson</td>\n",
       "      <td>2022-02-17 21:38:34.505792</td>\n",
       "      <td>2022-02-17 21:38:35.641519</td>\n",
       "      <td>179</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>195.9 KiB</td>\n",
       "      <td>3.1 MiB</td>\n",
       "      <td>Toronto Police Service Public Safety Data Portal</td>\n",
       "      <td><a href=\"https://data.torontopolice.on.ca/datasets/TorontoPS::traffic-collisions-asr-t-tbl-001/about\" target=\"_blank\">https://data.torontopolice.on.ca/datasets/TorontoPS::traffic-collisions-asr-t-tbl-001/about</a></td>\n",
       "      <td><a href=\"https://opendata.arcgis.com/api/v3/datasets/2a36d61277a84c3eb9d0beebef4c023d_0/downloads/data?format=geojson&spatialRefId=4326\" target=\"_blank\">https://opendata.arcgis.com/api/v3/datasets/2a36d61277a84c3eb9d0beebef4c023d_0/downloads/data?format=geojson&amp;spatialRefId=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>traffic</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_traffic.geojson</td>\n",
       "      <td>2022-02-17 21:39:15.604954</td>\n",
       "      <td>2022-02-17 21:39:28.097846</td>\n",
       "      <td>700</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>765.8 KiB</td>\n",
       "      <td>5.7 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/traffic-volumes-at-intersections-for-all-modes/\" target=\"_blank\">https://open.toronto.ca/dataset/traffic-volumes-at-intersections-for-all-modes/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/0ffadfc9-b017-44df-a1b1-905591e54caa\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/0ffadfc9-b017-44df-a1b1-905591e54caa</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>autospeed_enforcement</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_autospeed_enforcement.geojson</td>\n",
       "      <td>2022-02-17 21:37:49.816782</td>\n",
       "      <td>2022-02-17 21:37:50.035766</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/automated-speed-enforcement-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/automated-speed-enforcement-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e25e9460-a0e8-469c-b9fb-9a4837ac6c1c?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e25e9460-a0e8-469c-b9fb-9a4837ac6c1c?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>watch_your_speed</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_watch_your_speed.geojson</td>\n",
       "      <td>2022-02-17 21:39:30.059304</td>\n",
       "      <td>2022-02-17 21:39:30.318456</td>\n",
       "      <td>45</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>49.3 KiB</td>\n",
       "      <td>2.4 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/school-safety-zone-watch-your-speed-program-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/school-safety-zone-watch-your-speed-program-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4e2221b9-da3a-4ef8-b8eb-17e95b7abaa0?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4e2221b9-da3a-4ef8-b8eb-17e95b7abaa0?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>red_light_cams</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_red_light_cams.geojson</td>\n",
       "      <td>2022-02-17 21:37:50.089174</td>\n",
       "      <td>2022-02-17 21:37:50.306010</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/red-light-cameras/\" target=\"_blank\">https://open.toronto.ca/dataset/red-light-cameras/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/b57a31a1-5ee6-43e3-bfb9-206ebe93066d?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/b57a31a1-5ee6-43e3-bfb9-206ebe93066d?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>police</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_police.geojson</td>\n",
       "      <td>2022-02-17 21:37:50.352911</td>\n",
       "      <td>2022-02-17 21:37:50.542683</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/police-facility-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/police-facility-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4afc3c66-5614-466a-b714-e8d6336fc6d3?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4afc3c66-5614-466a-b714-e8d6336fc6d3?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_ambulance.geojson</td>\n",
       "      <td>2022-02-17 21:37:50.587017</td>\n",
       "      <td>2022-02-17 21:37:50.780715</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/ambulance-station-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/ambulance-station-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e49245ba-395c-46bf-bcf8-22fc7024d649?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e49245ba-395c-46bf-bcf8-22fc7024d649?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fire_hydrants</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_fire_hydrants.geojson</td>\n",
       "      <td>2022-02-17 21:37:51.754606</td>\n",
       "      <td>2022-02-17 21:37:51.959267</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/fire-hydrants/\" target=\"_blank\">https://open.toronto.ca/dataset/fire-hydrants/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/beaaa552-6338-4c81-95be-411e6cef6b89?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/beaaa552-6338-4c81-95be-411e6cef6b89?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fire_stations</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_fire_stations.geojson</td>\n",
       "      <td>2022-02-17 21:37:52.021106</td>\n",
       "      <td>2022-02-17 21:37:52.271250</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/fire-station-locations/\" target=\"_blank\">https://open.toronto.ca/dataset/fire-station-locations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/9d1b7352-32ce-4af2-8681-595ce9e47b6e?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/9d1b7352-32ce-4af2-8681-595ce9e47b6e?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>renewables</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_renewables.geojson</td>\n",
       "      <td>2022-02-17 21:37:52.321905</td>\n",
       "      <td>2022-02-17 21:37:52.529716</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/renewable-energy-installations/\" target=\"_blank\">https://open.toronto.ca/dataset/renewable-energy-installations/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/05a84a1c-a707-4792-9e89-0d8176f1fd7b?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/05a84a1c-a707-4792-9e89-0d8176f1fd7b?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bicycle_parking</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_bicycle_parking.geojson</td>\n",
       "      <td>2022-02-17 21:37:53.111157</td>\n",
       "      <td>2022-02-17 21:37:53.300727</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/street-furniture-bicycle-parking/\" target=\"_blank\">https://open.toronto.ca/dataset/street-furniture-bicycle-parking/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e0700d94-0c4d-4058-9354-a4b397c0b249?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/e0700d94-0c4d-4058-9354-a4b397c0b249?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>transit_shelters</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_transit_shelters.geojson</td>\n",
       "      <td>2022-02-17 21:37:53.487235</td>\n",
       "      <td>2022-02-17 21:37:53.684364</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/street-furniture-transit-shelter/\" target=\"_blank\">https://open.toronto.ca/dataset/street-furniture-transit-shelter/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/b9214fd7-60d1-45f3-8463-a6bd9828f8bf?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/b9214fd7-60d1-45f3-8463-a6bd9828f8bf?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>wayfind</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_wayfind.geojson</td>\n",
       "      <td>2022-02-17 21:37:53.740783</td>\n",
       "      <td>2022-02-17 21:37:53.934421</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/street-furniture-information-pillar-wayfinding-structure/\" target=\"_blank\">https://open.toronto.ca/dataset/street-furniture-information-pillar-wayfinding-structure/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2ee41858-5bbd-4073-98bf-7d46430bb1b9?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2ee41858-5bbd-4073-98bf-7d46430bb1b9?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>litter</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_litter.geojson</td>\n",
       "      <td>2022-02-17 21:37:54.218928</td>\n",
       "      <td>2022-02-17 21:37:54.411215</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/street-furniture-litter-receptacle/\" target=\"_blank\">https://open.toronto.ca/dataset/street-furniture-litter-receptacle/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/6c74cc93-3814-4970-84ab-0755e845b25f?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/6c74cc93-3814-4970-84ab-0755e845b25f?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>schools</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_schools.geojson</td>\n",
       "      <td>2022-02-17 21:39:30.995468</td>\n",
       "      <td>2022-02-17 21:39:31.233472</td>\n",
       "      <td>14</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>15.4 KiB</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/school-locations-all-types/\" target=\"_blank\">https://open.toronto.ca/dataset/school-locations-all-types/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/02ef7447-54d9-4aa7-b76d-8ef8138ac546?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/02ef7447-54d9-4aa7-b76d-8ef8138ac546?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>childcare</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_childcare.geojson</td>\n",
       "      <td>2022-02-17 21:39:32.962260</td>\n",
       "      <td>2022-02-17 21:39:33.758416</td>\n",
       "      <td>130</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>142.3 KiB</td>\n",
       "      <td>2.9 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/licensed-child-care-centres/\" target=\"_blank\">https://open.toronto.ca/dataset/licensed-child-care-centres/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/69403dde-a0b3-491d-9451-2338806a3bf0?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/69403dde-a0b3-491d-9451-2338806a3bf0?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>art</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_art.geojson</td>\n",
       "      <td>2022-02-17 21:37:54.472700</td>\n",
       "      <td>2022-02-17 21:37:54.688664</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/public-art/\" target=\"_blank\">https://open.toronto.ca/dataset/public-art/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/978ac0af-4bbb-4485-98b8-451b8e5e5229?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/978ac0af-4bbb-4485-98b8-451b8e5e5229?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>culture</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_culture.geojson</td>\n",
       "      <td>2022-02-17 21:37:54.752570</td>\n",
       "      <td>2022-02-17 21:37:54.942546</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/cultural-hotspot-points-of-interest/\" target=\"_blank\">https://open.toronto.ca/dataset/cultural-hotspot-points-of-interest/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/a95fcc72-a575-4826-aadd-6eef52774e50?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/a95fcc72-a575-4826-aadd-6eef52774e50?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>religious</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_religious.geojson</td>\n",
       "      <td>2022-02-17 21:37:55.018474</td>\n",
       "      <td>2022-02-17 21:37:55.217485</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>4.5 KiB</td>\n",
       "      <td>2.2 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/places-of-worship/\" target=\"_blank\">https://open.toronto.ca/dataset/places-of-worship/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/eafc9b0f-ba16-4be1-a196-304e35751b45?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/eafc9b0f-ba16-4be1-a196-304e35751b45?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>crime</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_crime.geojson</td>\n",
       "      <td>2022-02-17 21:37:45.873492</td>\n",
       "      <td>2022-02-17 21:37:46.563622</td>\n",
       "      <td>115</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>125.9 KiB</td>\n",
       "      <td>2.7 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/neighbourhood-crime-rates/\" target=\"_blank\">https://open.toronto.ca/dataset/neighbourhood-crime-rates/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4754baf5-3715-4166-a347-eda9813521de?format=geojson&projection=4326\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/4754baf5-3715-4166-a347-eda9813521de?format=geojson&amp;projection=4326</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>education</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_education.geojson</td>\n",
       "      <td>2022-02-17 21:37:46.811022</td>\n",
       "      <td>2022-02-17 21:37:47.045402</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>14.3 KiB</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-education/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-education/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/64a5b3dd-5ce3-4c98-83a5-11986eb54e34\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/64a5b3dd-5ce3-4c98-83a5-11986eb54e34</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>environment</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_environment.geojson</td>\n",
       "      <td>2022-02-17 21:37:47.265920</td>\n",
       "      <td>2022-02-17 21:37:47.496674</td>\n",
       "      <td>14</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>15.4 KiB</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-environment/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-environment/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/87cb3a08-0929-4c60-bd87-1dac04e41689\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/87cb3a08-0929-4c60-bd87-1dac04e41689</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>demographics</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_demographics.geojson</td>\n",
       "      <td>2022-02-17 21:37:47.736129</td>\n",
       "      <td>2022-02-17 21:37:47.968989</td>\n",
       "      <td>31</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>34.0 KiB</td>\n",
       "      <td>2.4 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-demographics/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-demographics/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2fd345a6-4107-49c1-b439-5ba0abe011d7\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2fd345a6-4107-49c1-b439-5ba0abe011d7</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>housing</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_housing.geojson</td>\n",
       "      <td>2022-02-17 21:37:48.164413</td>\n",
       "      <td>2022-02-17 21:37:48.371270</td>\n",
       "      <td>17</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>18.7 KiB</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-housing/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-housing/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2fc77a12-fadc-447a-9ab9-1b170a508937\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/2fc77a12-fadc-447a-9ab9-1b170a508937</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>economics</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_economics.geojson</td>\n",
       "      <td>2022-02-17 21:37:48.550708</td>\n",
       "      <td>2022-02-17 21:37:48.759403</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>17.6 KiB</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-economics/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-economics/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/78cf2678-8cd9-442b-a8e7-ed9ae6e82e01\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/78cf2678-8cd9-442b-a8e7-ed9ae6e82e01</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>civics</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_civics.geojson</td>\n",
       "      <td>2022-02-17 21:37:48.938304</td>\n",
       "      <td>2022-02-17 21:37:49.148222</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>14.3 KiB</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-civics-equity-indicators/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-civics-equity-indicators/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/f62b0d1d-dc2d-4e0e-a9d3-aee112b9c400\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/f62b0d1d-dc2d-4e0e-a9d3-aee112b9c400</a></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>health</td>\n",
       "      <td>../tmp/dist/by-dataset/geohoods_to_health.geojson</td>\n",
       "      <td>2022-02-17 21:37:49.332130</td>\n",
       "      <td>2022-02-17 21:37:49.560731</td>\n",
       "      <td>21</td>\n",
       "      <td>140</td>\n",
       "      <td>['Polygon']</td>\n",
       "      <td>23.1 KiB</td>\n",
       "      <td>2.3 MiB</td>\n",
       "      <td>City of Toronto Open Data Portal</td>\n",
       "      <td><a href=\"https://open.toronto.ca/dataset/wellbeing-toronto-health/\" target=\"_blank\">https://open.toronto.ca/dataset/wellbeing-toronto-health/</a></td>\n",
       "      <td><a href=\"https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/bcab8834-3743-49f5-a956-9a98b7130e27\" target=\"_blank\">https://ckan0.cf.opendata.inter.prod-toronto.ca/download_resource/bcab8834-3743-49f5-a956-9a98b7130e27</a></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get files and dataset names\n",
    "sources = read_data(data_sources_file)\n",
    "datasets = [Path(row.file).with_suffix('').stem for row in sources.itertuples() if Path(row.file).stem != study_area_dataset]\n",
    "files = [f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson' for name in datasets]\n",
    "\n",
    "# Read datasets\n",
    "datasets = {name: read_data(f'{dist_folder}/by-dataset/geohoods_to_{name}.geojson') for name in datasets}\n",
    "\n",
    "# Create summary info\n",
    "info = pd.DataFrame([df_info(df, name) for name, df in datasets.items()])\n",
    "info_files = pd.DataFrame([file_info(name) for name in files])\n",
    "summary = pd.concat([info, info_files], axis=1)\n",
    "\n",
    "# Add supplymentary info\n",
    "sources = sources.rename(columns={'file': 'dataset'})\n",
    "sources = sources[sources['dataset'] != f'{study_area_dataset}.geojson']\n",
    "sources['dataset'] = [k for k in datasets]\n",
    "summary = pd.merge(\n",
    "    summary,\n",
    "    sources,\n",
    "    on='dataset',\n",
    "    how='left'\n",
    ")\n",
    "                    \n",
    "# Rearrange summary columns and display\n",
    "summary['file'] = files\n",
    "summary = summary.rename(columns={'url': 'raw_url'})\n",
    "summary = summary[['dataset', 'file', 'created', 'modified', 'columns', 'rows', 'geometry', 'memory_usage', 'size', 'source', 'source_url', 'raw_url']]\n",
    "display(HTML(summary.to_html(render_links=True)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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