  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.
