diff --git a/experiments/presentation/example_census_of_pop_2021.ipynb b/experiments/presentation/example_census_of_pop_2021.ipynb new file mode 100644 index 0000000..954a09a --- /dev/null +++ b/experiments/presentation/example_census_of_pop_2021.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "3f027141", + "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": "code", + "execution_count": 8, + "id": "0e528d32", + "metadata": {}, + "outputs": [], + "source": [ + "# OpenStreetMap\n", + "\n", + "# 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": 9, + "id": "a05a1a2c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "16de2872de2b484d9e0b3c97803d98ba", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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_155,\n", + " cop.count_total_168,\n", + " CASE\n", + " WHEN cop.count_total_168 = 0.0 THEN 0\n", + " WHEN cop.count_total_155 = 0.0 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-01.dataforcanada.org/processed/statistics_canada/census_of_population/2021/tabular/da_2021.parquet' AS cop,\n", + " 'https://data-01.dataforcanada.org/processed/statistics_canada/boundaries/2021/digital_boundary_files/da_2021.parquet' AS geo\n", + "WHERE geo.csd_dguid in (\n", + " '2021A00056001009', -- Whitehorse, YT\n", + " '2021A00056106023', -- Yellowknife, NT\n", + " '2021A00056204003', -- Iqaluit, NU\n", + " '2021A00055915022', -- Vancouver, BC\n", + " '2021A00054806016', -- Calgary, AB\n", + " '2021A00054706027', -- Regina, SK\n", + " '2021A00054611040', -- Winnipeg, MB\n", + " '2021A00053506008', -- Ottawa, ON\n", + " '2021A00052466023', -- Montréal, QC\n", + " '2021A00051301006', -- Saint John, NB\n", + " '2021A00051102075', -- Charlottetown, PE\n", + " '2021A00051209034', -- Halifax, NS\n", + " '2021A00051001519' -- St. John's, NL\n", + " ) \n", + "AND cop.da_dguid = geo.da_dguid;\n", + "\"\"\")\n", + "\n", + "con.execute(\"\"\"\n", + "COPY geo_data TO './da_2021_characteristic.parquet' (FORMAT PARQUET);\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "453d6956-9869-4a89-969f-b56a6e791c78", + "metadata": {}, + "outputs": [], + "source": [ + "characteristic_values = con.execute(\"SELECT DISTINCT percentage_over_100k 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[\"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": 11, + "id": "68deef68-9ed5-4187-86f0-c23c6c164c8d", + "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": 12, + "id": "f5dd0922-4c72-4911-8e06-da3f8bdb09bd", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2ce00adb4a304296996a446110f56863", + "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": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = Map([basemap, cop_layer])\n", + "\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d359f0cf-4c05-4fca-9176-709cd5ff977e", + "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 +}