Update DuckDB lonboard example

experiments/duckdb_census_of_population_lonboard.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "id": "56ac906e",
    "metadata": {},
    "outputs": [],
@@ -25,28 +25,32 @@
    "id": "5d97e882",
    "metadata": {},
    "source": [
-    "# 1.0 Total private dwellings and private dwellings per square kilometer for Ottawa\n",
+    "# 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": 26,
+   "execution_count": 2,
    "id": "580c82ad-f64d-439f-9055-2307fdf7cccd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<duckdb.duckdb.DuckDBPyConnection at 0x7fd635715cb0>"
+       "<duckdb.duckdb.DuckDBPyConnection at 0x7f05770247b0>"
       ]
      },
-     "execution_count": 26,
+     "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",
@@ -56,45 +60,43 @@
     "    cop.count_total_4,\n",
     "    cop.count_total_6,\n",
     "    cop.count_total_7,\n",
-    "    ROUND(\n",
+    "    CASE\n",
-    "        (cop.count_total_4 / cop.count_total_7), 2\n",
+    "        WHEN cop.count_total_7 = 0.0 THEN 0\n",
-    "    ) AS count_total_4_per_square_km,\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 IN ('Vancouver') AND cop.da_dguid = geo.da_dguid;\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_private_dwellings.parquet' (FORMAT PARQUET);\n",
+    "COPY geo_data TO './da_2021_characteristic.parquet' (FORMAT PARQUET);\n",
     "\"\"\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 3,
    "id": "e4794c4d-6013-40b5-8e59-046fc2495d34",
    "metadata": {},
    "outputs": [],
    "source": [
-    "private_dwellings_per_square_km = con.execute(\"SELECT DISTINCT count_total_4_per_square_km FROM geo_data\").fetchall()\n",
+    "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 private_dwellings_per_square_km])\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)"
+    "breaks = jenkspy.jenks_breaks(values, n_classes=num_classes)\n",
-   ]
+    "\n",
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "8672f3f8-82bf-439e-8558-cb3566f2062f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "# 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",
@@ -102,22 +104,14 @@
     "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 f\"{int(low)}-{int(high)}\"\n",
     "    return \"unknown\"\n",
     "\n",
     "\n",
-    "dwellings_df = gpd.read_parquet('./da_2021_private_dwellings.parquet')\n",
+    "characteristic_df = gpd.read_parquet('./da_2021_characteristic.parquet')\n",
-    "dwellings_df['category'] = dwellings_df[\"count_total_4_per_square_km\"].apply(lambda v: jenks_range(v))\n",
+    "characteristic_df['category'] = characteristic_df[\"count_total_4_per_square_km\"].apply(lambda v: jenks_range(v))\n",
-    "dwellings_df['category'] = dwellings_df['category'].astype('category')"
+    "characteristic_df['category'] = characteristic_df['category'].astype('category')\n",
-   ]
+    "\n",
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "f265300a-9cf7-4ab7-8bdb-d66feae3a2f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "# Categories to colors\n",
     "cmap = {}\n",
     "colors = [\n",
@@ -127,13 +121,13 @@
     "    [255, 63.75, 63.75],\n",
     "    [255, 0, 0]\n",
     "]\n",
-    "for index, value in enumerate(dwellings_df['category'].unique()):\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": 33,
+   "execution_count": 17,
    "id": "a6a2ae6c-61b7-4c0e-bbe7-a580a511ee5a",
    "metadata": {},
    "outputs": [],
@@ -152,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 22,
    "id": "56e96627-0e82-436a-bd8e-e51546c7526b",
    "metadata": {},
    "outputs": [],
@@ -169,53 +163,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 6,
-   "id": "fef53303-147d-44af-b8f4-b824f64b486f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "get_color = apply_categorical_cmap(pa.array(dwellings_df['category']), cmap)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
    "id": "6935a061-41fc-4223-b155-4caf4c6df103",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "cop_layer = PolygonLayer.from_geopandas(gdf=dwellings_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_units=\"meters\",\n",
-    "                                        opacity=0.4,\n",
-    "                                        auto_highlight = True,\n",
-    "                                        highlight_color=[0,0,0,0]\n",
-    "                                       )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "1ab3dded-cff9-40cb-ac32-306581018083",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = Map([basemap, cop_layer])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "afb530c1-f652-4aaa-a3fc-1214a71ffdce",
-   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5d5e30381eff4f839a04ae81eff1b1e2",
+       "model_id": "0bf8da87ca7045eb9394ed83a01c2857",
        "version_major": 2,
        "version_minor": 1
       },
@@ -223,14 +178,184 @@
        "Map(custom_attribution='', layers=(BitmapTileLayer(data='http://mt0.google.com/vt/lyrs=s&hl=en&x={x}&y={y}&z={…"
       ]
      },
-     "execution_count": 38,
+     "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": [
+       "<duckdb.duckdb.DuckDBPyConnection at 0x7f05770247b0>"
+      ]
+     },
+     "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": {