Philly Landlord Spotter
A tenant-centered open-data map that helped Philadelphia renters and organizers research property ownership during the eviction crisis.
Problem
Public property records existed, but they were not packaged around the tenant question: who owns my building, and what else do they own nearby?
Approach
I combined Philadelphia property assessment data, Python processing, a Node/Express backend, and a React map UI to group records by owner and make ownership patterns searchable.
Outcome
The shipped public project earned national coverage in Mic and became the subject of a Towards Data Science article about the data work behind it.
Highlights
- Grouped Philadelphia Open Data property records by owner for tenant research.
- Used map clustering and visual summaries to make large public datasets usable.
- Connected software delivery, open data, public writing, and tenant advocacy.
Tenant-Centered Framing
The project was built around renters and organizers, not around a generic real-estate data dashboard. The important workflow was discovering ownership patterns that could help people coordinate and research their housing situation.
Data and Interface
Python processing shaped city property data into an application-ready form. A Node/Express service and React map frontend made the records searchable, clusterable, and easier to inspect.
The UI emphasized practical exploration: owner grouping, mapped properties, and supporting charts over raw record dumps.
Public Proof
This is the strongest external proof point in the project set. It has a public repo, press coverage, and a published technical article that explains the data work behind it.