Linking socioeconomic classes and land cover data in Lima, Peru: Assessment through the application of remote sensing and GIS

The spatial differentiation of socioeconomic classes in a city can deliver insight into the nexus of urban development and the environment. The purpose of this paper is to identify poor and rich regions in large cities according to the predominant physical characteristics of the regions. Meaningful spatial information from urban systems can be derived using remote sensing and GIS tools, especially in large difficult-to-manage cities where the dynamics of development results in rapid changes to urban patterns. We use here very high resolution imagery data for the identification of homogeneous socioeconomic zones in a city. We formulate the categorization task as a GIS analysis of an image classified with conventional techniques. Experiments are conducted using a QuickBird image of a study area in Lima, Peru. We provide accuracy assessment of results compared to ground truth data. Results show an approximated allocation of socioeconomic zones within Lima. The methodology described could also be applied to other urban centers, particularly large cities of Latin America, which have characteristics similar to those of the study area.