Add contextily road basemaps to NB02 and NB05 geographic plots

NB02: Block group boundary map now shows street tiles behind the
census polygons and isochrone overlay (Austin, TX).

NB05: POI scatter plot now shows street tiles behind the category-
colored markers (Seattle, WA).

Author: mihiarc

docs/notebooks/02-census-block-groups.ipynb

@@ -68,15 +68,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Uncomment to install on Google Colab:\n",
-    "# !pip install 'socialmapper @ git+https://github.com/mihiarc/socialmapper.git'\n",
-    "\n",
-    "from socialmapper import create_isochrone, get_census_blocks\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd"
-   ]
+   "source": "# Uncomment to install on Google Colab:\n# !pip install 'socialmapper @ git+https://github.com/mihiarc/socialmapper.git'\n\nfrom socialmapper import create_isochrone, get_census_blocks\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport geopandas as gpd\nimport contextily as cx"
   },
   {
    "cell_type": "markdown",
@@ -248,47 +240,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from shapely.geometry import shape\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))\n",
-    "\n",
-    "# Collect areas for the colormap\n",
-    "areas = [b[\"area_sq_km\"] for b in blocks]\n",
-    "norm = plt.Normalize(vmin=min(areas), vmax=max(areas))\n",
-    "cmap = plt.cm.YlOrRd\n",
-    "\n",
-    "# Plot each block group polygon\n",
-    "for b in blocks:\n",
-    "    geom = shape(b[\"geometry\"])\n",
-    "    color = cmap(norm(b[\"area_sq_km\"]))\n",
-    "    if geom.geom_type == \"Polygon\":\n",
-    "        xs, ys = geom.exterior.xy\n",
-    "        ax.fill(xs, ys, alpha=0.6, fc=color, ec=\"#555555\", linewidth=0.5)\n",
-    "    elif geom.geom_type == \"MultiPolygon\":\n",
-    "        for poly in geom.geoms:\n",
-    "            xs, ys = poly.exterior.xy\n",
-    "            ax.fill(xs, ys, alpha=0.6, fc=color, ec=\"#555555\", linewidth=0.5)\n",
-    "\n",
-    "# Overlay the isochrone boundary\n",
-    "iso_geom = shape(iso[\"geometry\"])\n",
-    "xs, ys = iso_geom.exterior.xy\n",
-    "ax.plot(xs, ys, color=\"#d63384\", linewidth=2.5, linestyle=\"--\", label=\"15-min drive isochrone\")\n",
-    "\n",
-    "# Colorbar\n",
-    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
-    "sm.set_array([])\n",
-    "cbar = plt.colorbar(sm, ax=ax, shrink=0.7, pad=0.02)\n",
-    "cbar.set_label(\"Area (sq km)\", fontsize=11)\n",
-    "\n",
-    "ax.set_title(\"Census Block Groups within 15-min Drive of Austin, TX\", fontsize=14, fontweight=\"bold\", color=\"#1a1a2e\")\n",
-    "ax.set_xlabel(\"Longitude\")\n",
-    "ax.set_ylabel(\"Latitude\")\n",
-    "ax.legend(loc=\"upper right\", fontsize=10)\n",
-    "ax.set_aspect(\"equal\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
+   "source": "from shapely.geometry import shape\n\nfig, ax = plt.subplots(figsize=(10, 10))\n\n# Build a GeoDataFrame of block group polygons with area data\ngeometries = [shape(b[\"geometry\"]) for b in blocks]\nareas = [b[\"area_sq_km\"] for b in blocks]\ngdf_blocks = gpd.GeoDataFrame({\"area_sq_km\": areas}, geometry=geometries, crs=\"EPSG:4326\")\n\n# Plot block groups colored by area\ngdf_blocks.plot(\n    ax=ax, column=\"area_sq_km\", cmap=\"YlOrRd\", alpha=0.6,\n    edgecolor=\"#555555\", linewidth=0.5, legend=True,\n    legend_kwds={\"label\": \"Area (sq km)\", \"shrink\": 0.7, \"pad\": 0.02},\n)\n\n# Overlay the isochrone boundary\niso_geom = shape(iso[\"geometry\"])\ngdf_iso = gpd.GeoDataFrame(geometry=[iso_geom], crs=\"EPSG:4326\")\ngdf_iso.boundary.plot(ax=ax, color=\"#d63384\", linewidth=2.5, linestyle=\"--\", label=\"15-min drive isochrone\")\n\n# Add road basemap\ncx.add_basemap(ax, crs=\"EPSG:4326\", source=cx.providers.CartoDB.Positron)\n\nax.set_title(\"Census Block Groups within 15-min Drive of Austin, TX\", fontsize=14, fontweight=\"bold\", color=\"#1a1a2e\")\nax.set_xlabel(\"Longitude\")\nax.set_ylabel(\"Latitude\")\nax.legend(loc=\"upper right\", fontsize=10)\nplt.tight_layout()\nplt.show()"
   },
   {
    "cell_type": "markdown",
@@ -559,4 +511,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/notebooks/05-points-of-interest.ipynb

@@ -42,24 +42,7 @@
    "id": "cell-2",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Uncomment to install on Google Colab:\n",
-    "# !pip install 'socialmapper @ git+https://github.com/mihiarc/socialmapper.git'\n",
-    "\n",
-    "from socialmapper import (\n",
-    "    get_poi,\n",
-    "    import_poi_csv,\n",
-    "    create_isochrone,\n",
-    "    get_census_blocks,\n",
-    "    get_census_data,\n",
-    "    create_map,\n",
-    ")\n",
-    "from socialmapper.poi_categorization import POI_CATEGORY_MAPPING\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "from IPython.display import Image, display"
-   ]
+   "source": "# Uncomment to install on Google Colab:\n# !pip install 'socialmapper @ git+https://github.com/mihiarc/socialmapper.git'\n\nfrom socialmapper import (\n    get_poi,\n    import_poi_csv,\n    create_isochrone,\n    get_census_blocks,\n    get_census_data,\n    create_map,\n)\nfrom socialmapper.poi_categorization import POI_CATEGORY_MAPPING\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport geopandas as gpd\nimport contextily as cx\nfrom IPython.display import Image, display"
   },
   {
    "cell_type": "markdown",
@@ -432,46 +415,7 @@
    "id": "cell-22",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(9, 9))\n",
-    "\n",
-    "# Assign a color to each category\n",
-    "unique_categories = df_all[\"category\"].unique()\n",
-    "color_map = {cat: plt.cm.Set2(i / max(len(unique_categories) - 1, 1)) for i, cat in enumerate(unique_categories)}\n",
-    "\n",
-    "for category in unique_categories:\n",
-    "    subset = df_all[df_all[\"category\"] == category]\n",
-    "    ax.scatter(\n",
-    "        subset[\"lon\"],\n",
-    "        subset[\"lat\"],\n",
-    "        c=[color_map[category]],\n",
-    "        label=f\"{category} ({len(subset)})\",\n",
-    "        s=40,\n",
-    "        alpha=0.75,\n",
-    "        edgecolors=\"white\",\n",
-    "        linewidths=0.5,\n",
-    "    )\n",
-    "\n",
-    "# Mark the origin\n",
-    "ax.scatter(\n",
-    "    -122.3321, 47.6062,\n",
-    "    c=\"red\", marker=\"*\", s=200, zorder=5,\n",
-    "    label=\"Origin (Seattle center)\",\n",
-    "    edgecolors=\"darkred\", linewidths=0.8,\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"Longitude\", fontsize=11)\n",
-    "ax.set_ylabel(\"Latitude\", fontsize=11)\n",
-    "ax.set_title(\n",
-    "    \"POI Locations by Category near Seattle, WA\",\n",
-    "    fontsize=13,\n",
-    "    fontweight=\"bold\",\n",
-    ")\n",
-    "ax.legend(loc=\"upper left\", fontsize=8, framealpha=0.9)\n",
-    "ax.set_aspect(\"equal\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
+   "source": "fig, ax = plt.subplots(figsize=(9, 9))\n\n# Assign a color to each category\nunique_categories = df_all[\"category\"].unique()\ncolor_map = {cat: plt.cm.Set2(i / max(len(unique_categories) - 1, 1)) for i, cat in enumerate(unique_categories)}\n\nfor category in unique_categories:\n    subset = df_all[df_all[\"category\"] == category]\n    ax.scatter(\n        subset[\"lon\"],\n        subset[\"lat\"],\n        c=[color_map[category]],\n        label=f\"{category} ({len(subset)})\",\n        s=40,\n        alpha=0.75,\n        edgecolors=\"white\",\n        linewidths=0.5,\n        zorder=5,\n    )\n\n# Mark the origin\nax.scatter(\n    -122.3321, 47.6062,\n    c=\"red\", marker=\"*\", s=200, zorder=6,\n    label=\"Origin (Seattle center)\",\n    edgecolors=\"darkred\", linewidths=0.8,\n)\n\n# Add road basemap for geographic context\ncx.add_basemap(ax, crs=\"EPSG:4326\", source=cx.providers.CartoDB.Positron)\n\nax.set_xlabel(\"Longitude\", fontsize=11)\nax.set_ylabel(\"Latitude\", fontsize=11)\nax.set_title(\n    \"POI Locations by Category near Seattle, WA\",\n    fontsize=13,\n    fontweight=\"bold\",\n)\nax.legend(loc=\"upper left\", fontsize=8, framealpha=0.9)\nplt.tight_layout()\nplt.show()"
   },
   {
    "cell_type": "markdown",