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

@inproceedings{Georgia2015IdentifyingAP,
  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},
  year={2015}
}
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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Showing 1-5 of 5 references

Big data in the big apple

E. Copeland
Capital City Foundation., • 2015
View 2 Excerpts

A computer-system that classifies the prefectures of greece in forest fire risk zones using fuzzy sets

L. S. Iliadis, A. K. Papastavrou, P. D. Lefakis
Forest Policy and Economics, • 2002
View 1 Excerpt

Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks

M. P. de Vasconcelos, S. Silva, M. Tome, M. Alvim, J. C. Pereira
Photogrammetric engineering and remote sensing, • 2001
View 2 Excerpts

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