All case studies
Disaster Relief2015–2020Washington, D.C.
American Red Cross + DataKind
Predicting Home Fire Risk to Save Lives at Scale
2.5M+
Homes Protected
The challenge
The American Red Cross responds to a home fire every 24 minutes in the US — but had no way to proactively identify and reach the highest-risk households before tragedy struck.
The solution
DataKind DC volunteers built a national fire risk prediction model using open data (census, fire incident records, housing age, poverty indices) to score every US census tract. The Red Cross used the model to direct their Home Fire Campaign canvassers to highest-risk neighbourhoods.
Outcomes
- Model identifies highest-risk communities across all 50 states
- Volunteers installed smoke alarms in 2.5M+ high-risk homes
- Over 1,000 lives saved through targeted prevention campaigns
- Model iterated and improved multiple times with open-source code
The pattern, applied to your business
See this pattern in your own business
Most of these patterns are reproducible at SMB scale on the Microsoft AI stack you probably already own. The 30-minute readiness assessment is the fastest way to find out which one fits.