Dynamic modeling to support an integrated analysis among land use change, accessibility and gentrification

The gentrification phenomenon is characterized by the replacement of the prevailing social class living in a residential area by another one with a higher income due to improvements in technical and social infrastructures, such as upgrades in the accessibility conditions. Gentrification is a matter of great concern, especially in big cities of developing countries, where infrastructure provision should not reinforce patterns of sociospatial segregation. The main motivation of this work is to verify if land use transitions could lead to possible gentrification, under the influence of accessibility-related variables – such as the public transportation network, education and health equipments and the availability of employment. In order to represent a phenomenon that varies over time and space, the study proposes a dynamic modeling via cellular automata, using the validation of past simulations to measure the suitability of each accessibility variable to explain the observed land use transitions. The probability of each cell´s transition was calculated using the weights of evidence method considering the explanatory variables, based on the Bayes´ Theorem. The proposed model is applied in a case study that comprises districts in the southwestern sector of São Paulo city, Brazil, an area marked by the heterogeneity of its land use, with a considerable predominance of low-income dwellings. The work was based on spatial data from more than a decade (2000−2016), which besides providing information on land use, also enable the categorization of residential and retail buildings according to their standard and size. The modeling process revealed that for different ranges of each variable, transition trends usually associated with gentrification took place, such as the increasing presence of retail and services, the construction of new buildings in previously non-residential areas, the occupation of vacant land and the reduction of industrial use. However, observing the occurrence spots of those changes, it is reasonable to state that most of the occupation patterns found in each area tended to remain throughout the years, which means that low-income areas were not totally eliminated. Distinct levels of added value granted to the urban tissue were observed as a function of the predominant land use in each area – highlighting the complexity of the relationship between urban form and gentrification. The satisfactory results in the model validation confirmed the good performance of the explaining variables in modeling the urban form dynamics within the study area.