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Insurance and risk analytics require evaluating environmental exposure across millions of policies and properties. Wherobots enables carriers and reinsurers to run large-scale spatial risk models, enrich policy data with hazard layers, and automate underwriting workflows that depend on location intelligence.

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How to use these solutions

Treat these notebooks as data engineering patterns as you evaluate Wherobots for your business or organization:

Catastrophe Exposure Analysis

Overlay insured property locations with flood zones, wildfire perimeters, hurricane tracks, and seismic hazard maps to quantify portfolio exposure.

Automated Underwriting Enrichment

Enrich policy submissions with proximity-to-hazard scores, building footprint characteristics, and land cover classifications using spatial joins.

Weather & Climate Analytics

Integrate NOAA severe weather data, historical storm tracks, and climate projections to model loss frequency and severity at granular spatial resolution.

Portfolio Concentration Monitoring

Detect spatial clustering of insured risks using hotspot analysis to identify dangerous accumulations before they become catastrophic losses.

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Key Capabilities

Join parcel and property records against FEMA flood hazard zones to count exposed parcels, total assessed value at risk, and land use mix per city or portfolio segment.

California coastal flood risk

Quantify parcels and assessed value inside FEMA flood zones.
Join millions of policy locations against hazard polygons, peril footprints, and administrative boundaries in a single query with WherobotsDB’s optimized spatial join engine.

Spatial joins in Wherobots

Join policy locations against hazard polygons at scale.
Access and analyze NOAA Severe Weather Data Inventory records to correlate historical severe weather events with insured property locations across the US.

Exploring storm data

Correlate NOAA severe weather records with your locations.
Apply Getis-Ord Gi* spatial statistics to identify statistically significant clusters of claims, losses, or insured values. Surface portfolio concentrations before the next catastrophe.

Hotspot detection

Separate real concentrations from noise with Getis-Ord Gi*.
Join property records with building footprints from Overture Maps, roof condition assessments from satellite imagery, and elevation data to build comprehensive risk profiles.

Using Overture Maps

Pull building footprints and places from one open catalog.
Classify land cover around insured properties using ESA WorldCover or RasterFlow models to assess wildland-urban interface exposure, flood susceptibility, and vegetation fire risk.

Exploring ESA WorldCover

Classify land cover at 10 m resolution anywhere on earth.

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Wherobots Advantage

Get started with Wherobots

Try Wherobots with your own geospatial data in minutes.