Modelling the impact of the urban spatial structure on the choice of residential locations using 'big earth data' and machine learning

Abstract

People settle in areas of the city which fit to their individual social and economic situation. In consequence, similar social groups can often be found in similar areas of cities - a process commonly known as segregation. These processes are well-studied from a socioeconomic perspective. In this study, in contrast, we address this topic with an explicitly spatial analysis of these living environments. We present an exploratory data analysis approach to study physical characteristics in different living environments based on a large number of variables derived from spatial data such as satellites, OpenStreetMap and statistical data. Several sensitivity analyses are performed to quantitatively analyze the descriptive performance of these spatial variables on three socioeconomic groups: high and low status households as well as the proportion of foreign population. Non-parametric regression models based on random forests yield highest R 2 of almost 0.52 for the proportion of foreign population.