{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "56ac906e", "metadata": {}, "outputs": [], "source": [ "import duckdb\n", "import geopandas as gpd\n", "import jenkspy\n", "from lonboard import BitmapTileLayer, Map, PolygonLayer\n", "from lonboard.colormap import apply_categorical_cmap\n", "import numpy as np\n", "import pyarrow as pa\n", "\n", "con = duckdb.connect()\n", "con.install_extension(\"spatial\")\n", "con.load_extension(\"spatial\")" ] }, { "cell_type": "markdown", "id": "5d97e882", "metadata": {}, "source": [ "# 1. Total private dwellings and private dwellings per square kilometer at Dissemination Area geographic level\n", "These values are from the 2021 Census of Population" ] }, { "cell_type": "code", "execution_count": 2, "id": "580c82ad-f64d-439f-9055-2307fdf7cccd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "Vancouver CMA is geo.cma_dguid = '2021S0503933'\n", "\"\"\"\n", "\n", "con.execute(\"\"\"\n", "DROP TABLE IF EXISTS geo_data;\n", "CREATE TABLE geo_data AS\n", "SELECT\n", " geo.da_dguid,\n", " cop.count_total_1,\n", " cop.count_total_4,\n", " cop.count_total_6,\n", " cop.count_total_7,\n", " CASE\n", " WHEN cop.count_total_7 = 0.0 THEN 0\n", " WHEN cop.count_total_4 = 0 THEN 0\n", " WHEN cop.count_total_4 IS NULL THEN 0\n", " ELSE \n", " ROUND(\n", " (cop.count_total_4 / cop.count_total_7), 2\n", " ) \n", " END AS count_total_4_per_square_km,\n", " geo.geom\n", "FROM\n", " 'https://data.dataforcanada.org/processed/statistics_canada/census_of_population/2021/tabular/da_2021.parquet' AS cop,\n", " 'https://data.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/da_2021.parquet' AS geo\n", "WHERE geo.csd_name = 'Vancouver' AND cop.da_dguid = geo.da_dguid;\n", "\n", "\"\"\")\n", "\n", "con.execute(\"\"\"\n", "COPY geo_data TO './da_2021_characteristic.parquet' (FORMAT PARQUET);\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "e4794c4d-6013-40b5-8e59-046fc2495d34", "metadata": {}, "outputs": [], "source": [ "characteristic_values = con.execute(\"SELECT DISTINCT count_total_4_per_square_km FROM geo_data\").fetchall()\n", "\n", "values = np.array([v[0] for v in characteristic_values])\n", "\n", "# Compute Jenks breaks\n", "num_classes = 5\n", "breaks = jenkspy.jenks_breaks(values, n_classes=num_classes)\n", "\n", "# Create a bin range mapping: (lower, upper) for each bin\n", "bin_ranges = [(breaks[i], breaks[i+1]) for i in range(len(breaks)-1)]\n", "\n", "# Create a function to get the range string for a value\n", "def jenks_range(value) -> str:\n", " for i, (low, high) in enumerate(bin_ranges):\n", " if low <= value <= high:\n", " return f\"{int(low)}-{int(high)}\"\n", " return \"unknown\"\n", "\n", "\n", "characteristic_df = gpd.read_parquet('./da_2021_characteristic.parquet')\n", "characteristic_df['category'] = characteristic_df[\"count_total_4_per_square_km\"].apply(lambda v: jenks_range(v))\n", "characteristic_df['category'] = characteristic_df['category'].astype('category')\n", "\n", "# Categories to colors\n", "cmap = {}\n", "colors = [\n", " [255, 255, 255],\n", " [255, 191.25, 191.25],\n", " [255, 127.50, 127.50],\n", " [255, 63.75, 63.75],\n", " [255, 0, 0]\n", "]\n", "for index, value in enumerate(sorted(characteristic_df['category'].unique(), key=lambda x: int(x.split('-')[0]))):\n", " cmap[value] = colors[index]" ] }, { "cell_type": "code", "execution_count": 17, "id": "a6a2ae6c-61b7-4c0e-bbe7-a580a511ee5a", "metadata": {}, "outputs": [], "source": [ "# OpenStreetMap\n", "\n", "# We set `max_requests < 0` because `tile.openstreetmap.org` supports HTTP/2.\n", "basemap = BitmapTileLayer(\n", " data=\"https://tile.openstreetmap.org/{z}/{x}/{y}.png\",\n", " tile_size=256,\n", " max_requests=-1,\n", " min_zoom=0,\n", " max_zoom=19,\n", ")" ] }, { "cell_type": "code", "execution_count": 22, "id": "56e96627-0e82-436a-bd8e-e51546c7526b", "metadata": {}, "outputs": [], "source": [ "# Google Satellite\n", "basemap = BitmapTileLayer(\n", " data=\"http://mt0.google.com/vt/lyrs=s&hl=en&x={x}&y={y}&z={z}\",\n", " tile_size=256,\n", " max_requests=-1,\n", " min_zoom=0,\n", " max_zoom=19,\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "id": "6935a061-41fc-4223-b155-4caf4c6df103", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0bf8da87ca7045eb9394ed83a01c2857", "version_major": 2, "version_minor": 1 }, "text/plain": [ "Map(custom_attribution='', layers=(BitmapTileLayer(data='http://mt0.google.com/vt/lyrs=s&hl=en&x={x}&y={y}&z={…" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_color = apply_categorical_cmap(pa.array(characteristic_df['category']), cmap)\n", "\n", "cop_layer = PolygonLayer.from_geopandas(gdf=characteristic_df,\n", " stroked=True,\n", " get_fill_color=get_color,\n", " get_line_color=[255, 255, 255],\n", " get_line_width=5,\n", " line_width_min_pixels=0.2,\n", " line_width_units=\"meters\",\n", " opacity=0.4,\n", " auto_highlight = True\n", " )\n", "\n", "m = Map([basemap, cop_layer])\n", "\n", "m" ] }, { "cell_type": "markdown", "id": "2e87d9fa-50d0-4278-99b3-7399b88aa010", "metadata": {}, "source": [ "# 2. Percentage of people with income $100,000 and over\n", "These values are from the 2021 Census of Population" ] }, { "cell_type": "code", "execution_count": 24, "id": "3fcf04bc-2c8b-4e76-9c6d-7d1eb3892dbc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.execute(\"\"\"\n", "DROP TABLE IF EXISTS geo_data_2;\n", "CREATE TABLE geo_data_2 AS\n", "SELECT\n", " geo.da_dguid,\n", " cop.count_total_1,\n", " cop.count_total_155,\n", " cop.count_total_168,\n", " CASE\n", " WHEN cop.count_total_168 = 0.0 THEN 0\n", " WHEN cop.count_total_155 = 0 THEN 0\n", " WHEN cop.count_total_168 IS NULL THEN 0\n", " WHEN cop.count_total_155 IS NULL THEN 0\n", " ELSE \n", " ((cop.count_total_168/cop.count_total_155) * 100) \n", " END AS percentage_over_100k,\n", " geo.geom\n", "FROM\n", " 'https://data.dataforcanada.org/processed/statistics_canada/census_of_population/2021/tabular/da_2021.parquet' AS cop,\n", " 'https://data.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/da_2021.parquet' AS geo\n", "WHERE geo.cma_dguid = '2021S0503933' AND cop.da_dguid = geo.da_dguid;\n", "\"\"\")\n", "\n", "con.execute(\"\"\"\n", "COPY geo_data_2 TO './da_2021_characteristic_2.parquet' (FORMAT PARQUET);\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 25, "id": "b730c891-d3b6-4fb4-a9ea-dd898e9e8490", "metadata": {}, "outputs": [], "source": [ "characteristic_values = con.execute(\"SELECT DISTINCT percentage_over_100k FROM geo_data_2\").fetchall()\n", "\n", "values = np.array([v[0] for v in characteristic_values])\n", "\n", "# Compute Jenks breaks\n", "num_classes = 5\n", "breaks = jenkspy.jenks_breaks(values, n_classes=num_classes)\n", "\n", "# Create a bin range mapping: (lower, upper) for each bin\n", "bin_ranges = [(breaks[i], breaks[i+1]) for i in range(len(breaks)-1)]\n", "\n", "# Create a function to get the range string for a value\n", "def jenks_range(value) -> str:\n", " for i, (low, high) in enumerate(bin_ranges):\n", " if low <= value <= high:\n", " return f\"{int(low)}-{int(high)}\"\n", " return \"unknown\"\n", "\n", "\n", "characteristic_df = gpd.read_parquet('./da_2021_characteristic_2.parquet')\n", "characteristic_df['category'] = characteristic_df[\"percentage_over_100k\"].apply(lambda v: jenks_range(v))\n", "characteristic_df['category'] = characteristic_df['category'].astype('category')\n", "\n", "# Categories to colors\n", "cmap = {}\n", "colors = [\n", " [255, 255, 255],\n", " [255, 191.25, 191.25],\n", " [255, 127.50, 127.50],\n", " [255, 63.75, 63.75],\n", " [255, 0, 0]\n", "]\n", "for index, value in enumerate(sorted(characteristic_df['category'].unique(), key=lambda x: int(x.split('-')[0]))):\n", " cmap[value] = colors[index]" ] }, { "cell_type": "code", "execution_count": 26, "id": "8f766c5a-5d6d-490e-b082-6b6efe399409", "metadata": {}, "outputs": [], "source": [ "get_color = apply_categorical_cmap(pa.array(characteristic_df['category']), cmap)\n", "\n", "cop_layer = PolygonLayer.from_geopandas(gdf=characteristic_df,\n", " stroked=True,\n", " get_fill_color=get_color,\n", " get_line_color=[255, 255, 255],\n", " get_line_width=5,\n", " line_width_min_pixels=0.2,\n", " line_width_units=\"meters\",\n", " opacity=0.4,\n", " auto_highlight = True\n", " )" ] }, { "cell_type": "code", "execution_count": 27, "id": "85c8c731-538e-440a-b784-125968222b7c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "91c9a47b91814bd28c7b5c0a10557973", "version_major": 2, "version_minor": 1 }, "text/plain": [ "Map(custom_attribution='', layers=(BitmapTileLayer(data='http://mt0.google.com/vt/lyrs=s&hl=en&x={x}&y={y}&z={…" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = Map([basemap, cop_layer])\n", "\n", "m" ] }, { "cell_type": "code", "execution_count": null, "id": "e09e4973-9018-4065-b9f9-d4259019bcf5", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }