Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta
Michael Madaio, Carnegie Mellon University; Shang-Tse Chen*, Georgia Institute of Technology; Oliver Haimson, University of California, Irvine; Wenwen Zhang, Georgia Institute of Technology; Xiang Cheng, Emory University; Matthew Hinds-Aldrich, Atlanta Fire Rescue Department; Duen Horng Chau, Georgia Tech; Bistra Dilkina, Georgia Tech
The Atlanta Fire Rescue Department (AFRD), like many municipal fire departments, actively works to reduce fire risk by inspecting commercial properties for potential hazards and fire code violations. However, AFRD’s fire inspection practices relied on tradition and intuition, with no existing data-driven process for prioritizing fire inspections or identifying new properties requiring inspection. In collaboration with AFRD, we developed the Firebird framework to help municipal fire departments identify and prioritize commercial property fire inspections, using machine learning, geocoding, and information visualization. Firebird computes fire risk scores for over 5,000 buildings in the city, with true positive rates of up to 71% in predicting fires. It has identified 6,096 new potential commercial properties to inspect, based on AFRD’s criteria for inspection. Furthermore, through an interactive map, Firebird integrates and visualizes fire incidents, property information and risk scores to help AFRD make informed decisions about fire inspections. Firebird has already begun to make positive impact at both local and national levels. It is improving AFRD’s inspection processes and Atlanta residents’ safety, and was highlighted by National Fire Protection Association (NFPA) as a best practice for using data to inform fire inspections.
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