Poverty Mapping in Uganda: An Analysis Using Remotely Sensed and Other Environmental Data
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This is the 36th of a series of Working Papers prepared for the Pro-Poor Livestock Policy Initiative (PPLPI). The purpose of these papers is to explore issues related to livestock development in the context of poverty alleviation. In order to reduce poverty we must first describe, explain and predict its spatial distribution over large areas with as high a level of local accuracy as possible. Poverty maps are traditionally produced by exploiting links between census (wide area) and survey (small er area coverage) data. The detailed relationships found within the survey data are extended to the census data that must share some predictor variables in common with the survey data. Both census and survey data tend to be socioeconomic in nature; the mapping thus exploits the internal correlations within potentially strongly correlated data sets one measure of poverty is often correlated with another.