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   "source": [
    "# Spatial Clustering of GeoDataFrame into Polygons\n",
    "This example demonstrates how to cluster spatial point data (e.g., buildings or services) into polygons using density-based algorithms:\n",
    "- DBSCAN or HDBSCAN methods\n",
    "- Parameters for minimum distance and minimum points per cluster"
   ],
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  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Import necessary libraries\n",
    "from objectnat import get_clusters_polygon\n",
    "import geopandas as gpd"
   ],
   "id": "1a1dbf25992ecf52",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 1. Load Point Dataset\n",
    "Load a set of points (e.g., buildings) for spatial clustering.\n"
   ],
   "id": "fb6c09592d642382"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Load building data\n",
    "buildings = gpd.read_parquet('examples_data/buildings.parquet')"
   ],
   "id": "a4e1407403d83325",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
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   "source": [
    " ## 2. Perform Clustering and Create Cluster Polygons\n",
    " Use the `get_clusters_polygon()` function to cluster points into groups based on spatial proximity.\n",
    " \n",
    " Parameters:\n",
    " - `min_dist`: maximum distance between neighboring points (e.g., 20 meters)\n",
    " - `min_point`: minimum number of points to form a cluster (e.g., 10)\n",
    " - `method`: clustering algorithm ('DBSCAN' or 'HDBSCAN')"
   ],
   "id": "9149bdc20488bbe5"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Apply clustering with DBSCAN\n",
    "clusters, buildings_clustered = get_clusters_polygon(\n",
    "    points=buildings,\n",
    "    min_dist=70,\n",
    "    min_point=2,\n",
    "    method='DBSCAN'\n",
    ")\n",
    "# Show cluster polygons\n",
    "m = clusters.explore()\n",
    "\n",
    "# Optional: show clustered buildings colored by cluster ID\n",
    "# buildings_clustered.explore(m=m, column='cluster', categorical=True)\n",
    "m"
   ],
   "id": "f990273f2f2e26c",
   "outputs": [],
   "execution_count": null
  }
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