In this research conducted by Grekousis and Photis, they attack the growing need for predictability of high risk emergency medical calls. The research conducted is in support of giving on staff medical personal the advantage of time on emergency medical calls around the surrounding area. They do so by combining GIS and neural networks to performing health emergency assessments to generate hazard maps that show areas that are potentially at high risk for emergencies. Through the use of those neural networks they can predict the location of future emergency events.
As a result, emergency services will have a detailed idea in advance of where there is a high possibility of an emergency occurring and can formulate a response, thus improving incident management and health planning. The example in the research is Athens Greece, where they tested this approach on stroke-events. Finding, with the help of GIS analysis, that health services can locate ambulances in places near the expected emergency cases, minimizing response time.
Grekousis, G., & Photis, Y. N. (2014). Analyzing high-risk emergency areas with GIS and neural networks: The case of Athens, Greece. The Professional Geographer, 66(1), 124-137.