Optimization of emergency response using higher order learning and clustering of 911 text messages

In real-time emergency response an accurate picture of the situation is needed quickly. Often during large-scale disasters, cell towers become overloaded, and the only way of communication is through text messages. It becomes important to gather information from text messages sent to emergency numbers in order to respond quickly and efficiently with life-saving efforts. In addition, responders are unable to manually handle the large volume of incoming texts. To add to this difficult problem, these data sources tend to be microtext. This research developed a methodology to summarize text messages sent during an emergency, including analysis of locations. The real-time disaster needs were then input into a mixed integer programming resource allocation model for distribution of resources for disaster aid. Prior research included resource allocation and text modeling, but the combination of the two is a novel application not only in this arena, but more broadly across domains.