Supporting SLEUTH – Enhancing a cellular automaton with support vector machines for urban growth modeling

Abstract

In recent years, urbanization has been one of the most striking change processes in the socioecological system of Central Europe. Cellular automata (CA) are a popular and robust approach for the spatially explicit simulation of land-use and land-cover changes. The CA SLEUTH simulates urban growth using four simple but effective growth rules. Although the performance of SLEUTH is very high, the modeling process still is strongly influenced by stochastic decisions resulting in a variable pattern. Besides, it gives no information about the human and ecological forces driving the local suitability of urban growth. Hence, the objective of this research is to combine the simulation skills of CA with the machine learning approach called support vector machines (SVM). SVM has the basic idea to project input vectors on a higher-dimensional feature space, in which an optimal hyperplane can be constructed for separating the data into two or more classes. By using a forward feature selection, important features can be identified and separated from unimportant ones. The anchor point of coupling both methods is the exclusion layer of SLEUTH. It will be replaced by a SVM-based probability map of urban growth. As a kind of litmus test, we compare the approach with the combination of CA and binomial logistic regression (BLR), a frequently used technique in urban growth studies. The integrated models are applied to an area in the federal state of North Rhine-Westphalia involving a highly urbanized region along the Rhine valley (Cologne, Düsseldorf) and a rural, hilly region (Bergisches Land) with a dispersed settlement pattern. Various geophysical and socio-economic driving forces are included, and comparatively evaluated. The validation shows that the quantity and the allocation performance of SLEUTH are augmented clearly when coupling SLEUTH with a BLR- or SVM-based probability map. The combination enables the dynamical simulation of different growth types on the one hand as well as the analyses of various geophysical and socio-economic driving forces on the other hand. The SVM approach needs less variables than the BLR model and SVM-based probabilities exhibit a higher certainty compared to those derived by BLR.