Estimation of population density of census sectors using remote sensing data and spatial regression

Abstract

Assuming that urban planning aims the optimization of urban functioning and the well-being of citizens, questions like "how many people are living in the city?" and "where do they live?" become key issues. In this work we utilized landscape metrics generated by the FragStats software for the estimation of population density out of census sectors in the mega city of São Paulo, Brazil. The metrics were calculated over an image from the QuickBird II sensor classified by the Maximum Likelihood algorithm. The accuracy of the classified image was analyzed qualitatively. Ordinary linear regression models were generated and formal statistical tests applied. The residuals from each model had its spatial dependency analyzed by visualizing its LISA Maps and by the Global Moran index. Afterwards, spatial regression models were tried and a significant improvement was obtained in terms of spatial dependency reduction and increase of the prediction power of the models. For the sake of comparison, the use of dummy variables was also tried and it became a suitable option for eliminating spatial dependency of the residuals as well. The results proved that some landscape metrics obtained over high resolution images, classified by simple supervised methods, can predict well the population density at the area under study when using it as independent variable in spatial regression models. © 2011 Springer-Verlag Berlin Heidelberg.