The Impact of Data Time Span on Forecast Accuracy through Calibrating the SLEUTH Urban Growth Model

Does the spacing of time intervals used for model input data have an impact on the model’s subsequent calibration and so projections of land use change and urban growth? This study evaluated the performance of the SLEUTH urban growth and land use change model through two independent model calibrations with different temporal extents (1972 to 2006 vs. 2000 to 2006) for the historical Italian cities of Pisa Province and their surroundings. The goal in performing two calibrations was to investigate the sensitivity of SLEUTH forecasts to longer or shorter calibration timelines, that is does calibrating the model over a longer time period produce better model fits and therefore forecasts? The best fit parameters from each calibration were then used in forecasting urban growth in the area up to the year 2027. The authors findings show that the spatial growth estimated by the model was strongly influenced by the physical landscape and road networks. The forecast outputs over 100 Monte Carlo trials reflect the start of newly formed detached settlements towards and along existing roads, i.e., classic urban sprawl. The authors conclude that the short term calibration was a better model fit compared to the long term calibration. Nevertheless, the absolute preference for the short-term calibration over long-term implies that time-sensitivity in calibration remains a challenge for SLEUTH applications. The Impact of Data Time Span on Forecast Accuracy through Calibrating the SLEUTH Urban Growth Model