Michael Madaio
Oliver L. Haimson
Duen Horng Chau
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. 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.
Supplemental Material
- National Fire Protection Association. Fires in the U.S. http://www.nfpa.org/research/reports-and-statistics/fires-in-the-us. Accessed: 2016-02-09.Google Scholar
- City of Atlanta. Code of ordinances: Fire prevention and protection. https://www.municode.com/library/ga/atlanta/codes/code_of_ordinances, 2015.Google Scholar
- L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001. Google ScholarDigital Library
- J. B. Carr and K. LeRoux. Which local governments cooperate on public safety?: lessons from michigan. Working Group on Interlocal Services Cooperation, page 4, 2005.Google Scholar
- J. Clare, L. Garis, D. Plecas, and C. Jennings. Reduced frequency and severity of residential fires following delivery of fire prevention education by on-duty fire fighters: Cluster randomized controlled study. Journal of safety research, 43(2):123--128, 2012.Google Scholar
- E. Copeland. Big data in the big apple. Capital City Foundation., 2015.Google Scholar
- C. Cortes and V. Vapnik. Support-vector networks. Machine learning, 20(3):273--297, 1995. Google ScholarDigital Library
- M. DaCosta, J. Krinsley, and B. Abelson. Optimizing local smoke alarm inspections with federal data. Bloomberg Data for Good Exchange, 2015.Google Scholar
- M. P. de Vasconcelos, S. Silva, M. Tome, M. Alvim, and J. C. Pereira. Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric engineering and remote sensing, 67(1):73--81, 2001.Google Scholar
- L. Dobric\ua, T. Ionescu, L. Dobric\ua, and S. Elena. Solutions based on gis technology in components of urban management applications. U.P.B Scientific Bulletin.Google Scholar
- J. H. Friedman. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4):367--378, 2002. Google ScholarDigital Library
- L. Garis and J. Clare. A dynamic risk-based framework for redesigning the scheduling of fire safety inspections. 2014.Google Scholar
- J. Gonzalez. Fuzzywuzzy. https://github.com/seatgeek/fuzzywuzzy, 2015.Google Scholar
- L. S. Iliadis. A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation. Environmental Modelling & Software, 20(5):613--621, 2005.Google ScholarCross Ref
- D. Kansagara, H. Englander, A. Salanitro, D. Kagen, C. Theobald, M. Freeman, and S. Kripalani. Risk prediction models for hospital readmission: a systematic review. Jama, 306(15):1688--1698, 2011.Google ScholarCross Ref
- H. Lakkaraju, E. Aguiar, C. Shan, D. Miller, N. Bhanpuri, R. Ghani, and K. L. Addison. A machine learning framework to identify students at risk of adverse academic outcomes. In KDD, pages 1909--1918. ACM, 2015. Google ScholarDigital Library
- A. Lapucci, S. Lombardo, M. Petri, and A. Santucci. A KDD based multicriteria decision making model for fire risk evaluation. In Conference on GIS-cience (AGILE), 2005.Google Scholar
- M. McGlohon, S. Bay, M. G. Anderle, D. M. Steier, and C. Faloutsos. Snare: a link analytic system for graph labeling and risk detection. In KDD, pages 1265--1274. ACM, 2009. Google ScholarDigital Library
- J. A. Nelder and R. J. Baker. Generalized linear models. Encyclopedia of Statistical Sciences.Google Scholar
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. JMLR, 12:2825--2830, 2011. Google ScholarDigital Library
Index Terms
- Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta
Recommendations
A Dynamic Pipeline for Spatio-Temporal Fire Risk Prediction
KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningRecent high-profile fire incidents in cities around the world have highlighted gaps in fire risk reduction efforts, as cities grapple with fewer resources and more properties to safeguard. To address this resource gap, prior work has developed machine ...
CityGuard: Citywide Fire Risk Forecasting Using A Machine Learning Approach
Forecasting the fire risk is of great importance to fire prevention deployments in a city, which can reduce loss even deaths caused by fires. However, it is very challenging because fires are influenced by many complex factors, including spatial ...
A Fuzzy Approach to Building Fire Risk Assessment and Analysis
IITA '09: Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application - Volume 01Building fire risk evaluation is the base of building fire risk management. Carrying out building fire risk evaluation can effectively prevent and control the building fire accident occurring. In this study, a fuzzy approach to building fire risk ...
Comments