  Emergency response management (ERM) is a challenge faced by communities
across the globe. First responders must respond to various incidents, such as
fires, traffic accidents, and medical emergencies. They must respond quickly to
incidents to minimize the risk to human life. Consequently, considerable
attention has been devoted to studying emergency incidents and response in the
last several decades. In particular, data-driven models help reduce human and
financial loss and improve design codes, traffic regulations, and safety
measures. This tutorial paper explores four sub-problems within emergency
response: incident prediction, incident detection, resource allocation, and
resource dispatch. We aim to present mathematical formulations for these
problems and broad frameworks for each problem. We also share open-source
(synthetic) data from a large metropolitan area in the USA for future work on
data-driven emergency response.

Computers and Society;Artificial Intelligence