The Challenge
Animal rescue data in Colorado was historically siloed. Each shelter maintained its own records, making it nearly impossible for state-level advocates to see where resources were most needed.
"We had plenty of data, but zero visibility into the geographical flow of rescue animals."
The goal was to build a central intelligence layer that could answer one critical question: Where are animals being surrendered faster than shelters can process them?
The Intelligence Layer
Fig 1.1 — Geospatial Heatmap of Shelter Capacity
Data Wrangling
I used Python to clean and merge CSV exports from diverse shelter management systems, ensuring breed, age, and intake types were standardized for accurate comparison.
Geographic Insights
By mapping intake locations against shelter outcomes, we identified "Rescue Deserts" where travel distances were preventing successful adoptions.
Impact Summary
- Consolidated data from 12+ regional partners.
- Visualized seasonal spikes in intake to predict staffing needs.
Stack & Tools
Python (Pandas)
Tableau
Data Normalization