Spatiotemporal Hotspots Analysis for Exploring the Evolution of Diseases: An Application to Oto-Laryngopharyngeal Diseases

Abstract

This paper presents a spatiotemporal analysis of hotspot areas based on the Extended Fuzzy C-Means method implemented in a geographic information system. This method has been adapted for detecting spatial areas with high concentrations of events and tested to study their temporal evolution. The data consist of georeferenced patterns corresponding to the residence of patients in the district of Naples (Italy) to whom a surgical intervention to the oto-laryngopharyngeal apparatus was carried out between the years 2008 and 2012.

WebGIS Platform for Detecting Spatio-Temporal Hotspots of Oto-Laryngo-Pharyngeal Diseases

“…We present the results obtained for carcinoma disease since the other above diseases were analyzed in [6,7]. We obtain two hotspots covering the city of Naples and many Vesuvian towns: the cluster showing the greatest expansion in 2012 is the one that covers the city of Naples (cfr.…”

“…The study of hotspots is vital in many disciplines in which it is necessary to detect the geographic areas on which thicken events, as crime analysis [2,8,14], which studies the spread on the territory of criminal events, fire analysis [5] which analyzes the phenomenon of fires on forested areas, and disease analysis, which studies the localization of focuses of diseases and their spatial evolution during the time [15,16,17],. The clustering methods mainly use are the algorithms based on density [6,11] but they are highly expensive in terms of computational complexity and it is not necessary to determine the exact geometry of the clusters in the great majority of cases. The clustering algorithm Fuzzy C-Means algorithm (FCM) [1] has a linear computational complexity and uses the Euclidean distance to determine prototypes cluster as points.…”