Calibrating SLEUTH with big data: Projecting California's land use to 2100 |
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| Affiliation: | 1. College of the Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Basij square, Gorgan, Golestan Provice, Iran;1. University of Florence, DISEI Dep. of Economics and Management, Italy;2. University of Basilicata, School of Engineering, Italy;3. University of California – Santa Barbara, USA;1. Department of Forestry and Natural Resources, Purdue University, 195 Marsteller Street, West Lafayette, IN 47907, USA;2. Department of Entomology, University of Wisconsin, Madison, WI 53706, USA;3. Institute for Conservation Research, San Diego Zoo Global, 15600 San Pasqual Valley Road, Escondido, CA 92027, USA;4. Rosen Center for Advanced Computing, Information Technology Division, Purdue University, West Lafayette, IN 47907, USA;5. Thavron Solutions, Kokomo, IN 46906, USA;6. Worldwide Construction and Foresy Division, John Deere, 1515 5th Avenue, Moine, IL, 61265, USA;1. School of Engineering, University of Basilicata, Potenza, Italy;2. Urban Systems Lab, The New School, New York, USA;3. LEMA, Urban and Environmental Engineering Department, Liège University, Belgium |
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| Abstract: | This study investigated the spatial consistency of the SLEUTH urban growth and land use change model using a massive data set. The research asks whether SLEUTH can yield both a reliable forecast of land use in the state of California for the year 2100 CE, and an assessment of the forecast's reliability. Data were prepared, and SLEUTH calibrated for 174 tiles made by partitioning the data within the 6 California State Plane Zones. A null hypothesis that all data divisions of California would give similar calibration outcomes so that a uniform simulated rate of growth would apply to statewide future simulations was proven false by mapping and Moran's I values. Spatial autocorrelation was found to propagate forward into the SLEUTH forecasts, resulting in major differences within the state in land use change and change rates. We also explored the spatial distribution of the rules that changed pixels between land use classes, finding that almost 99% of forecast growth in California comes from outward spread from new and existing settlements. The paper concludes with an examination of the uncertainty inherent within, and displayed by the SLEUTH forecasts. |
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