Tuesday, May 17, 2016

High-risk areas for healthcare

Grekousis' article discusses the different ways in which public health services might use GIS. 
GIS and the survey of communities can help officials understand the future demand for health services and potential locations of hospitals or emergency vehicles, survey the population's health status in order to predict specific needs and potential problems, research and diagnose problems like epidemics, and evaluate the effectiveness of the policies and care. This specific study focuses on Athens, Greece, and the city’s needs based on location and population. The methods involved were GIS, neural networks, and kernel density estimation (KDE) to generate raster maps that portrayed high risk areas. KDE is a technique that creates a map of density values, “in which the density at each location reflects the concentration of points in the surrounding area” (Grekousis 2014). This research can be potentially lifesaving, in regards to the population’s needs and emergency instances that require immediate, quality healthcare. In these maps, Grekousis portrays predicted and real events across Athens along with the KDE. In the last map, both sets of data are combined.

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. Are these "high risk" areas correlated with socioeconomic status or poverty?

    1. Historically, these areas are the ones more likely to go automatically default into a high-risk area. Is there a way we can find out if there is also a correlation with the barely appearing high-risk area in the very top of the figure between access to the proper healthcare and need?