Autologistic regression model for poverty mapping and analysis

Poverty mapping in developing countries has become an increasingly important tool in the search for ways to improve living standards in an economically and environmentally sustainable manner. Although the classical econometric methods provide information on the geographic distribution of poverty, they do not take into account the spatial dependence of the data and generally they do not consider any environmental information. Methods which use spatial analysis tools are required to explore such spatial dimensions of poverty and its linkages with the environmental conditions. This study applies a spatial analysis to determine those variables that affect household poverty and to estimate the number of poor people in the target areas.