  In this article, we propose a systematic approach for fire station location
planning. We develop machine learning models, based on Random Forest and
Extreme Gradient Boosting, for demand prediction and utilize the models further
to define a generalized index to measure quality of fire service in urban
settings. Our model is built upon spatial data collected from multiple
different sources. Efficacy of proper facility planning depends on choice of
candidates where fire stations can be located along with existing stations, if
any. Also, the travel time from these candidates to demand locations need to be
taken care of to maintain fire safety standard. Here, we propose a travel time
based clustering technique to identify suitable candidates. Finally, we develop
an optimization problem to select best locations to install new fire stations.
Our optimization problem is built upon maximum coverage problem, based on
integer programming. We further develop a two-stage stochastic optimization
model to characterize the confidence in our decision outcome. We present a
detailed experimental study of our proposed approach in collaboration with city
of Victoria Fire Department, MN, USA. Our demand prediction model achieves true
positive rate of 80% and false positive rate of 20% approximately. We aid
Victoria Fire Department to select a location for a new fire station using our
approach. We present detailed results on improvement statistics by locating a
new facility, as suggested by our methodology, in the city of Victoria.
