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

ABSTRACT

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.

KEYWORDS:

• Land use
• urban growth
• spatial analysis
• cellular automata
• Ibadan
• SLEUTH model

Acknowledgement

This research was supported in part by summer funding from the Department of Geography at the University of California at Santa Barbara.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available at https://datadryad.org/stash/dataset/doi:10.25349/D9SP5G

Additional information

Funding

This work was supported by summer research funding from the Department of Geography at the University of California at Santa Barbara.

Notes on contributors

Damilola Eyelade

Damilola Eyelade is a PhD candidate in Hydrology at the University of California Santa Barbara. He earned the M.S. degree in Geographic information science from the University of Redlands, California and the B.S. in Geography from the University of Ibadan, Nigeria. His research examines land use, urbanization and climate effects on hydrologic processes with the aid of geospatial analysis.

Keith C. Clarke

Keith C. Clarke is a research Cartographer and Professor of Geography at the University of California Santa Barbara. He received his MA and PhD degrees from the University of Michigan specializing in Analytical Cartography. His research covers environment simulation modeling, modeling urban growth, terrain mapping and visualization.

Ighodalo Ijagbone

Ighodalo Ijagbone is a M.S. student in Human-Computer Interaction at the College of Computing and Digital Media, DePaul University, Chicago, USA; and holds a B.S. in Geography from the University of Ibadan, Nigeria. His work focuses on improving user interfaces and user experience for emerging digital media.