The impact of interactions in spatial simulation of the dynamics of urban sprawl

This study investigated the modeling process for simulating the spatial dynamics of an urban ecosystem. Logistic regression is a common method for empirically modeling and analyzing land use and land use change. In most conventional applications of logistic regression, only the individual factors of the system are considered in the development of the logistic regression functions. However, this does not consider the relationships among factors that potentially occur within most ecosystems. Factors in a system, especially an urban system, are usually not fixed and not independent of each other, but rather are influenced by each other. Based on this point of view, the interactions of factors are introduced into a logistic regression in this study. This technique has been tested with a case study using historical land use maps and a spatially explicit dynamic cellular automata urban sprawl model. Using historical land use data, a logistic regression was used to analytically weight the scores of the driving factors of an urban sprawl model for predicting probability maps of land use change. The results of the case study have verified that interactions of factors can significantly improve the prediction of spatial dynamics of urban sprawl, and can provide a means to improve cellular automata models for simulation of the dynamics of urban and other ecosystems.