Spatial analysis and modelling of land use distributions in Belgium
When statistical analyses of land use drivers are performed, they rarely deal explicitly with spatial autocorrelation. Most studies are undertaken on autocorrelation-free data samples. By doing this, a great deal of information that is present in the dataset is lost. This paper presents a spatially explicit, cross-sectional analysis of land use drivers in Belgium. It is shown that purely regressive logistic models only identify trends or global relationships between socio-economic or physico-climatic drivers and the precise location of each land use type. However, when the goal of a study is to obtain the best statistical model fit of land use distribution, a purely autoregressive model is appropriate. It is shown that this type of model deals appropriately with spatial autocorrelation as measured by the lack of autocorrelation in the deviance residuals of the model. More specifically, three types of autoregressive models are compared: (1) a set of binomial logistic regression models (one for each modelled land use) accounting only for the proportion of the modelled land use within the neighbourhood of a cell; (2) a multinomial autologistic regression that accounts for the composition of a cells neighbourhood; and (3) a state-of-the-art Bayesian Maximum Entropy (BME) based model that accounts fully for the spatial organization of the land uses within the neighbourhood of a cell. The comparative analysis shows that the BME approach has no advantages over the other methods, for our specific application, but that accounting for the composition of a cells neighbourhood is essential in obtaining an optimal fit.
land use drivers logistic regression bayesian maximum entropy spatial autocorrelation neighbourhood effects