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. 1. Introduction In a GIS, the impact of phenomena in a specific area due to the proximity of the event (e.g., the study of the impact area of an earthquake, or the area constraint around a river basin) is performed using buffer area geoprocessing functions. Given a geospatial event topologically represented as a georeferenced punctual, linear, or areal element, an atomic buffer area is constituted by circular areas centered on the element. For example, if the event is the epicenter of an earthquake, georeferenced by a point, a set of buffer areas is formed by concentric circular areas around that point; the radius of each circular buffer area is defined a priori. When it is not possible to define statically an area of impact and we need to determine what is the area affected by the presence of a consistent set of events, we are faced with the problem of detecting this area as a cluster on which the georeferenced events are thickened as well. These clusters are georeferenced, represented as polygons on the map, and called hotspot areas. The study of hotspot areas is vital in many disciplines such as crime analysis [1–3], which studies the spread on the territory of criminal events, fire analysis [4], which analyzes the phenomenon of spread of fires on forested areas, and disease analysis [5–7], which studies the localization of focuses of diseases and their temporal evolution. The clustering methods mainly used for detecting hotspot areas are the algorithms based on density (see [8, 9]); they can detect the exact geometry of the hotspots, but are highly expensive in terms of computational complexity, and in the great majority of cases, it is not necessary to determine exactly the shape of the clusters. The clustering algorithm more used for its linear computational complexity is the Fuzzy C-Means algorithm (FCM) [10], a partitive fuzzy clustering method that uses the Euclidean distance to determine prototypes cluster as points. Let be a dataset composed of pattern , where is the th component (feature) of the pattern . The FCM
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