Spatial Analysis of Poverty Areas (Case Study: GhaemshahrCity)

geographers and urban planners are using tools in order to identify the poverty areas in the cities to mitigate the social and spatial consequences of that. The purpose of this applied research is to identify and analyze the poverty areas in Ghaemshahr city using descriptive-analysis method. The dominant approach for identifying poverty areas is spatial analysis. In this research, 25 indicators were extracted from the statistical block data of 1390 census. Grey Analysis Method was used to weight research Indicators and for overlapping layers researchers used the Fuzzy method in ArcGIS software. To analyze the spatial distribution of poverty areas spatial statistics were applied. The results illustrated that the central areas of the city around the main squares are in a good condition regarding research indicators and the highest levels of urban poverty is recognized in neighborhoods 15, 16, 17 and 20 which are located in the south and north-east. According to the classification of poverty in the city, the poverty level of the most deprived class was 6.66% and poverty in the deprived, middle, affluent and very affluent class were calculated 19.08, 32.90, 23.32 and 18.02 respectively. Urban poverty clustering has been confirmed using the Moran model; spatial patterns of clustering have been identified using the Hot Spot Analysis.