A spatial zoning approach to calibrate and validate urban growth models

Calibration and validation of models predicting urban growth have been largely developed using internal variables. Further investigation is required to improve model's calibration and validation mixing internal and external variables. To reach this objective, a spatial zoning approach simulating long-term expansion of Mashhad, the second largest city of Iran, was presented in this study. Spatial zoning approaches distinguish local-scale urban dynamics in districts with different socioeconomic characteristics. Thiessen polygons were used to identify districts with different morphology and functional attributes. Urban growth was subsequently simulated for each district using a Multi-Layer Perceptron (MLP) neural network and Markov chains (MC) analysis. MLP and MC algorithms were respectively used to derive transition maps from non-urban to urban use of land and to determine spatial evolution of built-up areas at the metropolitan scale. Results of simulations based on spatial zoning were compared with outcomes of traditional urban growth models. Spatial zoning improved significantly model's accuracy in respect to more traditional simulation modes. The approach proposed here is appropriate when simulating land-use changes under discontinuous urban expansion.