Empirically Derived Neighbourhood Rules for Urban Land-Use Modelling

Abstract

Land-use modelling and spatial scenarios have gained attention as a means to meet the challenge of reducing uncertainty in spatial planning and decision making. Many of the recent modelling efforts incorporate cellular automata to accomplish spatially explicit land-use-change modelling. Spatial interaction between neighbouring land uses is an important component in urban cellular automata. Nevertheless, this component is often calibrated through trial-and-error estimation. The aim of this project has been to develop an empirically derived landscape metric supporting cellular-automata-based land-use modelling. Through access to very detailed urban land-use data it has been possible to derive neighbourhood rules empirically, and test their sensitivity to the land-use classification applied, the regional variability of the rules, and their time variance. The developed methodology can be implemented easily and thus used as a much needed replacement for the various trial-and-error approaches that are often applied in land-use modelling.