10.5281/zenodo.556510
https://zenodo.org/records/556510
oai:zenodo.org:556510
Blancas Reyes, Eduardo
Eduardo
Blancas Reyes
University of Chicago
Helsby, Jennifer
Jennifer
Helsby
University of Chicago
Rasch, Katharina
Katharina
Rasch
University of Chicago
van der Boor, Paul
Paul
van der Boor
University of Chicago
Ghani, Rayid
Rayid
Ghani
University of Chicago
Haynes, Lauren
Lauren
Haynes
University of Chicago
Cunningham, Edward P.
Edward P.
Cunningham
City of Cincinnati, Dept. of Buildings and Inspections
Early detection of properties at risk of blight using spatiotemporal data
Zenodo
2017
Social Good
Urban Blight
Supervised Learning
2017-04-21
10.5281/zenodo.603933
https://zenodo.org/communities/dfp17
Creative Commons Attribution 4.0 International
Urban blight is a domino effect phenomenon: properties first fall into disrepair, then land values decline, and finally home abandonment and vacancy follows. This effect spreads from one home to another in the neighborhood, depressing values of nearby properties [8]. In partnership with the City of Cincinnati Office of Performance and Data Analytics and their Department of Buildings & Inspections, we used geographical data from the city and historical data on home inspections to train a Machine Learning model to provide proactive suggestions for property inspections targeted at catching blight early. Our best model reaches a precision of 70% for the top 6,000 predictions. This is a significant improvement over the discovery rate of the current approach, where 60% (in 2015) of citizen complaints result in the discovery of code violations. While our model can have a huge impact in tackling the blight problem, without field validation, the model can potentially have unintended consequences and ethical issues, such risks are being taken into account for the development of the project.