update

Author: spatialthoughts

_toc.yml

@@ -10,6 +10,7 @@ parts:
           - file: notebooks/geopandas_spatial_query.ipynb
           - file: notebooks/geopandas_extract_from_excel.ipynb
           - file: notebooks/geopandas_fuzzy_table_join.ipynb
+          - file: notebooks/geopandas_flood_frequncy.ipynb
     - caption: Xarray for Raster Data Processing
       chapters:
           - file: notebooks/xarray_raster_styling_analysis.ipynb

notebooks/geopandas_flood_frequncy.ipynb

@@ -5,27 +5,26 @@
    "id": "cell-0",
    "metadata": {},
    "source": [
-    "# Flood Frequncy Mapping with GeoPandas DuckDB\n",
+    "# Creating a Flood Frequncy Map\n",
     "\n",
     "## Overview\n",
     "\n",
-    "[Google Groundsource](https://zenodo.org/records/18647054) dataset of high-resolution flood observation derived from news articles. It contains data of flood events from 2000-2025 with precise dates and geographic polygons derived from Google Maps. The full dataset contains 2.6 million records and is available as a Parquet file. This notebook demonstrates how to use GeoPandas to efficiently load, filter, and analyze this large Parquet dataset to aggregate this records over a regular grid and create a flood frequency map for your chosen country.\n",
+    "[Google Groundsource](https://zenodo.org/records/18647054) is a dataset of high-resolution flood observation derived from news articles. It contains data of flood events from 2000-2025 with precise dates and geographic polygons derived from Google Maps. The full dataset contains 2.6 million records and is available as a Parquet file. This notebook demonstrates how to use GeoPandas to efficiently load, filter, and analyze this large Parquet dataset to aggregate this records over a regular grid and create a flood frequency map for your chosen country.\n",
     "\n",
     "**Input Layers**:\n",
     "* `groundsource_2026.parquet`: Google Groundsource flood observations dataset (Parquet format)\n",
     "* `ne_10m_admin_0_countries_ind.zip`: Natural Earth Admin0 country boundaries shapefile\n",
     "\n",
     "**Output**:\n",
-    "* `flood_frequency_grid.gpkg`: A GeoPackage of a 10km x 10km grid with total flood event counts.\n",
+    "* `flood_frequency_grid.gpkg`: A GeoPackage with a layer of 10km x 10km grid cells with flood event counts.\n",
     "\n",
     "**Data Credit**\n",
     "\n",
     "* Google Groundsource dataset: [https://zenodo.org/records/18647054](https://zenodo.org/records/18647054)\n",
     "* Natural Earth: [https://www.naturalearthdata.com/](https://www.naturalearthdata.com/)\n",
     "\n",
     "**Running the Notebook**:  \n",
-    "The preferred way to run this notebook is on Google Colab.  \n",
-    "<a href='https://colab.research.google.com/github/spatialthoughts/geopython-tutorials/blob/main/notebooks/geopandas_flood_frequency.ipynb' target='_parent'><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "The preferred way to run this notebook is on Google Colab.  <a href='https://colab.research.google.com/github/spatialthoughts/geopython-tutorials/blob/main/notebooks/geopandas_flood_frequency.ipynb' target='_parent'><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {
@@ -461,14 +460,24 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "101c8fe3",
+   "metadata": {},
+   "source": [
+    "Save the results to a GeoPackage."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "2f53ca54",
    "metadata": {},
    "outputs": [],
    "source": [
-    "grid_india_a.to_file(os.path.join(output_folder, 'grid_india_flood_risk.gpkg'))"
+    "output_filename = 'flood_frequency_grid.gpkg'\n",
+    "output_filepath = os.path.join(output_folder, output_filename)\n",
+    "grid_country.to_file(output_filepath)"
    ]
   }
  ],