Executive Summary
An Indianapolis-based public-health research team running a healthy food access study needed accurate store location data scraping to map where residents could — and could not — reach a full-service grocer. We delivered a structured store-location dataset for Kroger and competing chains, with addresses, coordinates, and store attributes, giving researchers the geospatial base layer to identify food deserts across their study area.
The Business Challenge
Food-desert analysis depends on knowing exactly where grocery stores are, what type they are, and how they're distributed relative to population. Public store locators are inconsistent and not export-friendly, and combining several chains into one clean, mappable dataset by hand is slow and error-prone. The team needed research-grade scraped data: complete, accurately geocoded, and structured for GIS tools.
The Developer Asset
We produced a grocery store location dataset via web scraping with store name, banner, full address, latitude/longitude, and available attributes such as store type and hours. Records were normalized across chains into one schema and validated for geocoding accuracy, so the data dropped cleanly into the team's mapping and analysis workflow.
The Solution
We aggregated store location data scraping for Kroger plus the relevant competitor chains across the study geography, deduplicated overlapping records, and delivered in CSV/GeoJSON-friendly structure. Coordinates were validated to support distance and accessibility analysis (e.g., share of population beyond a set distance from a full-service grocer). Because this was academic research, scope was kept to publicly available business-location data.
The Results & Business Value
- A clean, geocoded multi-chain store dataset ready for GIS analysis.
- Hours of manual locator-scraping and cleanup eliminated.
- Reliable base layer for food-desert and access mapping.
- Structured for reproducible academic analysis and publication.
Frequently asked questions
Store name, banner, address, latitude/longitude, and attributes like type and hours where available.
Yes — records are normalized into a single schema and deduplicated.
Yes, coordinates are validated and delivered in mapping-friendly formats.
Yes; this engagement used publicly available business-location data for research.