Where are the poor? A disaggregation approach of mapping urban poverty

In this paper, we highlight an approach for mapping indicators of poverty. The main drive for developing such an approach is to address the neglected aspect of the spatial dimension of poverty since in most cases the indicators chosen for poverty rely on household data. Such data if spatially analyzed are usually aggregated to administrative units such as neighbourhoods or wards. Therefore the spatial heterogeneity within the administrative unit is hidden. For target interventions, detailed spatial information is needed on poverty as a multidimensional phenomenon at a disaggregated level. The study shows an approach to address the neglected aspect of the spatial dimension of poverty, based on a case study of the city of Cebu in the Philippines. It is integrating locally available datasets on poverty relevant issues from several municipal departments with information extracted from a recent very-high resolution satellite image. The indicators identified and mapped are dwelling size, building density, lack of proper road network, poor structural quality of buildings and access to water and sanitation. Analysis is done at both disaggregated and aggregated levels (using data from the census) to demonstrate how data aggregation can hide spatial variation of poverty but also to examine the robustness of the selected spatial indicators. The results at the disaggregated level clearly locate poverty areas and show that the nature of poverty varies among those areas. For some areas, the main issue is e.g. structural quality of buildings, while for others it is either access to proper sanitation and/or access to water supply. This approach has potential by using locally available datasets in combination with satellite image data to provide spatially detailed information usable for both planning and monitoring poverty intervention.