> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt
> Use this file to discover all available pages before exploring further.

# GeoStats & Map Matching

> Spatial statistics, clustering, and routing

Wherobots provides spatial statistics, map matching, and raster ML capabilities for geospatial analysis at scale.

## Spatial Statistics

Analyze patterns, clusters, and relationships in your spatial data.

<Columns cols={2}>
  <Card title="DBSCAN Clustering" icon="circle-nodes" href="/tutorials/example-notebooks/clustering-dbscan">
    Identify clusters of spatial points using density-based clustering. DBSCAN finds core points with enough neighbors within a distance threshold and groups connected points into clusters.
  </Card>

  <Card title="Hotspot Detection" icon="fire" href="/tutorials/example-notebooks/getis-ord-gi*">
    Find statistically significant hotspots and coldspots with the Getis-Ord Gi\* statistic. Identify areas with unusually high or low concentrations of values.
  </Card>

  <Card title="Outlier Detection" icon="triangle-exclamation" href="/tutorials/example-notebooks/local-outlier-factor">
    Detect spatial outliers using Local Outlier Factor (LOF). Identify points that are anomalous compared to their spatial neighbors.
  </Card>

  <Card title="K-Nearest Neighbors" icon="magnifying-glass-location" href="/tutorials/example-notebooks/k-nearest-neighbor-join">
    Find the K nearest neighbors for spatial joins. Efficiently match points to their closest neighbors in another dataset.
  </Card>
</Columns>

## Map Matching

<Card title="GPS Map Matching" icon="route" href="/tutorials/example-notebooks/gps-map-matching">
  Match GPS traces to road networks for accurate routing analysis. Clean noisy GPS data by snapping points to the most likely road segments.
</Card>

## API Reference

For detailed API documentation, see:

* [GeoStats & Map Matching Reference](/reference/geostats-map-matching/clustering/dbscan-python)