Geographically and temporally weighted likelihood regression: Exploring the spatiotemporal determinants of land use change

• We propose a spatiotemporal nonparametric estimation technique.
• We apply the technique in a series of Monte Carlo experiments.
• We apply the technique to a real-world panel data set of historical land use.

Urban areas possess complex spatial configurations. These patterns are produced by cumulative changes in land use and land cover as human and natural environments are influenced by market forces, policy, and changes in the natural landscape. To understand the mechanisms underlying these complex patterns, it is important to develop models that can capture the complexity of the underlying economic process. This includes spatiotemporal variation in the variables as well as spatiotemporal heterogeneity or non-stationarity in the model. The objective of this paper is to build on previous work in spatial nonparametric modeling and propose a spatiotemporal technique for nonlinear panel data models. Using a series of Monte Carlo experiments, we demonstrate how extending a geographically weighted likelihood regression (GWLR) model to account for temporal heterogeneity can improve the performance of the model when heterogeneity exists in the spatial and temporal dimensions. We also show how the technique can be used in modeling real world land use changes by applying our proposed technique to a panel of historical subdivision development from an urbanizing county in the Baltimore/Towson Metropolitan Statistical Area (MSA). Our results demonstrate that the method provides better performance than a standard parametric model. We also demonstrate how the spatiotemporal marginal effects from the model can be used to conduct policy analysis at multiple spatial and temporal scales, which is not possible using the standard global parameter estimates. Our proposed technique is simple to execute and can be implemented using any statistical software package.