Spatial Statistics
Analyze patterns, clusters, and relationships in your spatial data.DBSCAN Clustering
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.
Hotspot Detection
Find statistically significant hotspots and coldspots with the Getis-Ord Gi* statistic. Identify areas with unusually high or low concentrations of values.
Outlier Detection
Detect spatial outliers using Local Outlier Factor (LOF). Identify points that are anomalous compared to their spatial neighbors.
K-Nearest Neighbors
Find the K nearest neighbors for spatial joins. Efficiently match points to their closest neighbors in another dataset.
Map Matching
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.
RasterFlow
RasterFlow is currently in Private Preview. Wherobots is rolling out RasterFlow to a select group of Organizations. Register your interest for early access.
Get Started with RasterFlow
Scale raster processing and geospatial ML workflows with RasterFlow’s simple, high-level API.
RasterFlow Models
Use built-in models for land cover, canopy height, and more — or bring your own PyTorch models.
RasterFlow Datasets
Work with built-in datasets like Sentinel-2 and NAIP, or bring your own imagery.
Run as a Job
Submit RasterFlow workflows as automated Job Runs for production pipelines.