Who settles where? Simulating urban growth and socioeconomic level using cellular automata and random forest regression

Cities in developing countries share a pattern of accelerated and largely unplanned urbanization that results in internal socioeconomic inequalities. The modeling of urban growth with spatial distribution of socioeconomic groups has been studied only to a limited extent. This paper proposes a method to simulate both urban growth and the socioeconomic group that is likely to settle in a particular location as a function of local environmental characteristics. Using a cellular automata model, newly urbanized cells are identified and then, as a post-processing step, distributed among five socioeconomic groups in a preferential settlement selection process. A land value map learned with a random forest regressor is used for this purpose. Our case study is Greater Mexico City during the period 1997–2010. We identified that the main features influencing the location of socioeconomic groups are the closest socioeconomic group, the distance to water bodies, and the distance to the urban center. This suggests that the newly urbanized cells are likely to settle in neighborhoods of similar socioeconomic levels. Moreover, the increasing distance from the urban center results in a generally decreasing land value. However, regions with a high land value were also found in remote areas where environmental features that improve the ecosystem services are present.