Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Feb 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2021Urban Fire Station Location Planning using Predicted Demand and Service Quality Indexcited by 0
- 2024Statistical and Machine Learning Models for Predicting Fire and Other Emergency Eventscited by 0
- 2025GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interfacecited by 0
- 2025GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface23 Dec 2025
- 2024Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events14 Feb 2024
- 2021Urban Fire Station Location Planning using Predicted Demand and Service Quality Index5 Sep 2021