The effects of scale, space and time on the predictive accuracy of land use models

The econometric literature about modeling land use choices is highly heterogeneous with respect to the scale of the data, and to the structure of the models in terms of the effects of space and time. This paper proposes a joint evaluation of each of these three elements by estimating a broad spectrum of individual and aggregate, spatial and aspatial, short and long run econometric models on the same detailed French dataset. Considering four land use classes (arable crops, pasture, forest, and urban), all the models are compared in terms of both in- and out-of-sample predictive accuracy. We argue that the aggregate scale allows to model more effectively the effect of space by using spatial econometric models. We show that modeling spatial autocorrelation allow to have very accurate predictions which can even outperform individual models when the appropriate predictors are used. We also found some strong interactions between the effects of scale, space and time which can be of major interest for applied researchers.