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The SLEUTH model provides a framework for understanding land use evolution around urban areas. Calibration of SLEUTH’s behavioral coefficients can be impacted by scale and nonlinear transitions due to the SLEUTH land use deltatron module’s assumption of linear Markov change probabilities. This study attempted to establish what spatial resolution and temporal scale produces the most accurate forecasts given the linear change assumption. The impact of tiling the input data was also examined. To determine these, SLEUTH was calibrated at four spatial and three temporal scales for Ibadan, Nigeria using both untiled and tiled data. Calibration results were evaluated using accuracy metrics including Figure of Merit (FOM) and mean uncertainty. The best mix of calibration metrics (FOM 0.26) and mean uncertainty (11.64) was achieved at 30 m resolution and an intermediate temporal interval. Tiling input data led to overfitting, allowing good model fit within individual tiles but a reduction in trend recognition across land use types. Subsequently, a 2040 projection that is as accurate as possible, and scientifically justifiable given the available data, was produced. The findings provide a framework for understanding the effect of spatiotemporal scale on SLEUTH inputs that require tiling particularly for urban areas in the global south.

Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns