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Site selection decisions involve evaluating hundreds of spatial variables across thousands of candidate locations. Wherobots enables organizations to move beyond manual GIS workflows and run sophisticated, data-driven location analysis at the scale of entire countries or markets.

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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:

Trade Area Analysis

Generate isochrones and trade areas around candidate sites to understand drive-time catchments, population coverage, and competitive overlap using Spatial SQL.

Demographic & Market Scoring

Join candidate locations with census data, consumer spending indices, and foot traffic datasets to score sites by market potential.

Competitor Proximity & Saturation

Measure how saturated each candidate market is by finding the k nearest competing locations for every site in a single distributed join.

Accessibility & Infrastructure

Evaluate transit access, road connectivity, and walkability scores at each candidate location by analyzing road networks and urban infrastructure data.

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Key Capabilities

Generate drive-time and walk-time isochrones for thousands of candidate locations in a single batch job. Understand exactly how far customers can travel to reach each site.

Making drive time isochrones

Build travel-time polygons for thousands of locations at once.
Use DBSCAN clustering to identify natural groupings of demand signals, competitor locations, or customer addresses to inform where gaps and opportunities exist.

DBSCAN clustering

Find natural groupings in demand and competitor locations.
Apply Getis-Ord Gi* statistics to identify statistically significant spatial concentrations of demand, revenue, or other performance metrics across geographies.

Hotspot detection

Separate real concentrations from noise with Getis-Ord Gi*.
Find the k nearest competitors, transit stops, or distribution centers for every candidate site in one distributed join, and use the resulting distances as scoring inputs.

k-Nearest Neighbor joins

Score every candidate by distance to its nearest competitors.
Calculate aggregate statistics within trade areas, zip codes, or custom polygons. Summarize population density, income levels, land cover mix, and more for each zone.

Zonal statistics

Summarize raster values inside your own trade area boundaries.

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Wherobots Advantage

Get started with Wherobots

Try Wherobots with your own geospatial data in minutes.