Firebird: Predicting fire risk and prioritizing fire inspections in Atlanta

Michael Madaio, Shang Tse Chen, Oliver L. Haimson, Wenwen Zhang, Xiang Cheng, Matthew Hinds-Aldrich, Duen Horng Chau, Bistra Dilkina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Scopus citations

Abstract

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. Further-more, 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.

Original languageEnglish (US)
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages185-194
Number of pages10
ISBN (Electronic)9781450342322
DOIs
StatePublished - Aug 13 2016
Externally publishedYes
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: Aug 13 2016Aug 17 2016

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume13-17-August-2016

Conference

Conference22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
Country/TerritoryUnited States
CitySan Francisco
Period8/13/168/17/16

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • Data science
  • Fire risk
  • Government innovation
  • Interactive visualization
  • Predictive modeling

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