Using spatial autologistic regression for predicting urban growth

This paper aims to contribute to the discussion around the statistical performance of spatial autologistic regressions. We provide empirical evidence using spatially explicit land use change data. The data is fitted using both the traditional logistic regression and the spatial logistic approach. Results show that the spatial autologistic regression outperforms the traditional logistic approach. We conclude that the results from the spatial autologistic regression run on our land use change data are preferable to those from the traditional logistic regression. Various estimates from the logistic regression are non-significant in the autologistic approach. This may provoke misleading actions among urban planners.