Using GIS to identify and locate “hotspots,” or areas with
statistically significant amounts of various phenomena, is both intuitive and
useful, given its powerful ability to both analyze and visually display this
kind of information. Martino, et al.
(2014) use GIS to this end to explore locational patterns and statistical
algorithms employed in the analysis of brain cancer in “Spatio-temporalhotspots and application on a disease analysis case via GIS.” With this research the authors sought to
identify locations with statistically significant proportions of brain cancer
incidence in New Mexico, with further consideration of temporal factors, for
the years 1973 to 1991. The data used
for the research was a collection of 5,000 point locations of cancer incidence
for the indicated time period, with the enumeration units as the counties that
compose the state of New Mexico. The
authors’ examination focused on a comparison of two methods of cluster
analysis- the Extended Fuzzy C Means (EFCM) and the Extended Gustafson-Kessel
(EGK) algorithms- employed within the GIS environment. The EFCM algorithm they used employs a
circular shaped hotspot area, determined by the patterns of point event
clusters across space, and makes a temporal comparison for a specified period
of time. The EGK method is an analogous
procedure which produced very similar results, but employs an ellipsoid shaped
hotspot area, and uses a different variation of membership calculation within
one if its formulas. Analysis of the
results of this research lead the authors to conclude that both algorithms,
used within a GIS, are equally effective in predicting the spatial area of the
diffusion of this disease, and may be of use in larger-scale studies seeking to
identify temporal and/or spatial determinants within a specified study
area.
Martino, F., Sessa, S., Barillari, U. S., & Barillari,
M. R. (2014). Spatio-temporal hotspots and application on a disease analysis
case via GIS. Soft Computing - A Fusion Of Foundations, Methodologies
And Applications, (12), 2377.
http://dl.acm.org/citation.cfm?id=2689536
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