background: gastrointestinal tract cancer (gi.c) is one of the common cancers in world‑wide. the incidence rate of it is different in various geographical regions. this study was performed to assess spatial clusters of the occurrence of gi.c in fars province. methods: in this cross‑sectional study, the new cases were 4569 cases from 2001 to 2009. the crude incidence rates were standardized base...

background: using spatial analysis tools to determine the spatial patterns of china province-level perinatal mortality and using spatial econometric model to examine the impacts of health care resources and different socio-economic factors on perinatal mortality. methods: the global moran’s i index is used to examine whether the spatial autocorrelation exists in selected regions and moran’s i s...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

in this research, the framework is presented for unsupervised change detection using multitemporal sar images based on integration clustering and level set methods. spatial correlation between pixels were considered by using contextual information. also as proposed method was used integration of gustafson-kessel clustering techniques (gkc) and level set methods for change detection. using clust...

the main purpose of this article is to present a clear picture of social bordering of urban space in mashhad and to identify the socio-spatial hierarchy in this city. 17510 resident householders were selected through stratified sampling and they were interviewed. the hierarchical clustering analysis was used to analyze the data, based on three social basic characteristics including occupational...

In this chapter, we present two approaches for clustering spatial functional data. The first one is the model-based that uses concept of density random variables. second hierarchical based on univariate statistics data such as mode or mean. These take into account features data: observations are spatially close and share a common distribution associated methodologies illustrated by an applicati...