Density cluster methods are used in spatial analysis for the determination of buffer impact areas called hotspots. We use the Extended Fuzzy C-Means (EFCM) algorithm because it has essentially three advantages: robustness to noise and outliers, linear computational complexity and automatic determination of the final number of clusters. The EFCM algorithm inside a Geographic Information Systems (GIS) is useful to determine hotspots as circles in the case of bidimensional pattern data. As example, we show these areas around to fire point-events data of the Santa Fè district (NM), downloaded from http://www.fs.fed.us/r3/gis/sfe_gis.shtml, and their spatial-temporal evolution in the period 2000 2006.
The EFCM algorithm as tool for the creation of hotspots in spatio -temporal GIS / Cardone, B.; Di Martino, F.; Sessa, Salvatore. - (2008). (Intervento presentato al convegno GIS DAY 2008 tenutosi a Napoli nel 19 Novembre 2008).
The EFCM algorithm as tool for the creation of hotspots in spatio -temporal GIS
B. Cardone;F. Di Martino;SESSA, SALVATORE
2008
Abstract
Density cluster methods are used in spatial analysis for the determination of buffer impact areas called hotspots. We use the Extended Fuzzy C-Means (EFCM) algorithm because it has essentially three advantages: robustness to noise and outliers, linear computational complexity and automatic determination of the final number of clusters. The EFCM algorithm inside a Geographic Information Systems (GIS) is useful to determine hotspots as circles in the case of bidimensional pattern data. As example, we show these areas around to fire point-events data of the Santa Fè district (NM), downloaded from http://www.fs.fed.us/r3/gis/sfe_gis.shtml, and their spatial-temporal evolution in the period 2000 2006.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
