Fuzzy Approach and the Multidimensional Poverty Mapping in Urban Space Case Study: Kamyaran City

Most of the methods designed for the analysis of poverty have two limitations: i) they are one-dimensional, i.e. they refer to only one proxy of poverty, namely low income or consumption expenditure; ii) they need to dichotomize the population into the poor and the non-poor by means of the poverty line. Nowadays, many authors recognize that poverty is a complex phenomenon that cannot solely be reduced to monetary dimension. Thus, the need for a multidimensional approach, which consists in extending the analysis of variety nonmonetary indicators of living conditions, is tangible. Due to second limitation, fuzzy sets can be used to identify those households that are absolutely deprived and poor, those households that are slightly less deprived, and households lying on the threshold of poverty. As poverty levels that have space and time components, a cartographic approach within a GIS environment allows the display and representation of poverty which aids the understanding of the causes of poverty. This study uses fuzzy approach and geostatistics techniques to identify reliable methods to measure multidimensional poverty and uses a poverty mapping in urban areas. The data of this descriptive-analytical study is collected by a survey technique via questionnaire. This survey uses a stratified random spatial sampling to determine and select samples. This study is based on the fuzzy multidimensional poverty measurement that proposed by Costa (2002) and geostatistics techniques (Kriging and Cokriging). Some factors such as economic and income; housing; property and finance; health; and knowledge and skills as criteria are taken in to consideration in the process of data analysis. According to results, spherical and exponential models suite with fuzzy membership of poverty more than the others. Poverty mapping clearly shows the spatial heterogeneity of poverty in this study area.