Thursday, September 17, 2015

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 Geographer66(1), 124-137.


  1. Was there any data presented on what the response time was after the GIS analysis had been implemented in order to approach the stroke-events?

  2. Are they going to bring this procedure to the US? I feel like it would be a great thing to test in big US cities where the large area of the city creates traffic issues, etc. that increase the amount of time it takes to get emergency medical services to a person that needs assistance.
    Also what exactly are neural networks? I wasn't clear as to what they entailed.
    Overall this seems like a very interesting study and a great system for minimizing health risks in large populations where it might be hard to dispatch ambulances in adequate time to save a person across a large city area.

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  4. I am curious, how exactly did they combined GIS and neural networks to perform health emergency assessments to generate hazard maps that show areas that are potentially at high risk for emergencies?