Identifying and Prioritizing Fire Inspections : A Case Study of Predicting Fire Risk in Atlanta

  title={Identifying and Prioritizing Fire Inspections : A Case Study of Predicting Fire Risk in Atlanta},
  author={Michael Madaio Georgia and Oliver L. Haimson and Xiang Cheng},
The Atlanta Fire Rescue Department (AFRD) attempts to reduce fire risk by inspecting buildings for potential hazards and fire code violations. This paper provides a case study exemplifying how data science can be used to help cities identify and prioritize potential property inspections, using machine learning, geocoding, and information visualization. As a result of this work, we generated a risk score for 5,000 buildings in the city, with an average of 73% accuracy for predicting future fires… CONTINUE READING

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