Chiang Mai Urban Growth Model

This dataset includes the inputs and outputs of Chiang Mai Urban Growth Model. To predict the urbanization probabilities of Chiang Mai in 2050, a cellular automata-based urban growth model called SLEUTH was used. The details of SLEUTH model can be found in the Project Gigalopolis website (available at http://www.ncgia.ucsb.edu/projects/gig/About/about.html). Five growth parameters (i.e. Diffusion, Breed, Spread, Slope, and Road Gravity) are used by the SLEUTH model. As the name implies, the inputs are slope, land use, excluded area, urban area, transportation and hillshade. In this model the change in land use was not considered as the focus is only on the change of urban (i.e. built up) areas. The urban area inputs for four years (i.e. 1975, 1990, 2000, 2014) were obtained from the European Commission's GHS-BUILT datasets (available at https://ghsl.jrc.ec.europa.eu/ghs_bu2019.php). The slope and hillshade layers were obtained from the ASTER GDEM datasets (available at https://earthexplorer.usgs.gov/). Two inputs for road networks in 2010 and 2014 were obtained through digitization of Landsat images (available at https://earthexplorer.usgs.gov/). Excluded area includes the military zones, airport, religion institutions, golf courses and national parks, and it was extracted from Sangawongse and Kowsuvon (2011). Two different approaches, namely the brute force and genetic algorithm (GA), were employed for the calibration of growth parameters. The codes corresponding to these two models are also available in Project Gigalopolis website (available at http://www.ncgia.ucsb.edu/projects/gig/Dnload/download.htm). The Diffusion, Breed and Spread parameters are calculated as 1 in both calibration methods. Slope and Road Gravity parameters were calculated as 100 and 21 in brute force calibration, while they were calculated as 84 and 45 using GA calibration. This translates to a dominant road influenced growth in the city. During the calibrations, Lee-Sallee metric was used to measure the spatial urban fit. It was calculated as around 0.7 after the brute force calibration, and as around 0.75 after the GA calibration. Since a value between 0.3 and 0.6 is acceptable, both Lee-Sallee values indicate that the models can be considered successful. The outputs reveal that the highest probability of urbanization in brute force-based model is 39%, and it is 42% in GA-based model. <br> [1] Sangawongse, S. and Kowsuvon, N. (2011) “Impact Assessment of Urbanization on Environmental Quality in Chiang Mai-Lamphun Valley, Northern Thailand” Japan Aerospace Exploration Agency (JAXA)-AIT Final Report.