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Simulated village locations in Thailand: a multi-scale model including a neural network approach

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Abstract

The simulation of rural land use systems in general, and rural settlement dynamics in particular, has developed with synergies of theory and methods for decades. Three current issues are: linking spatial patterns and processes, representing hierarchical relations across scales, and considering nonlinearity to address complex non-stationary settlement dynamics. We present a hierarchical simulation model to investigate complex rural settlement dynamics in Nang Rong, Thailand. This simulation uses sub-models to allocate new villages at three spatial scales. Regional and sub-regional models, which involve a nonlinear space–time autoregressive model implemented in a neural network approach, determine the number of new villages to be established. A dynamic village niche model, establishing a pattern–process link, was designed to enable the allocation of villages into specific locations. Spatiotemporal variability in model performance indicates that the pattern of village location changes as a settlement frontier advances from rice-growing lowlands to higher elevations. Simulation experiments demonstrate that this simulation model can enhance our understanding of settlement development in Nang Rong and thus gain insight into complex land use systems in this area.

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Acknowledgments

The research reported here was primarily supported by grants from the National Science Foundation (SBR 93-10366) and the National Aeronautics and Space Administration (NAG5-6002). In addition, grants from the National Institute of Child Health and Human Development (R01-HD33570 and R01-HD25482), the Evaluation Project (USAID DPE-3060-C-00-1054), the MacArthur Foundation (95-31576A-POP), and a center grant to the Carolina Population Center from the National Institute of Child Health and Human Development supported elements of the research and the substantial data collection and processing efforts that underpin it. Numerous staff members and graduate students at the Institute for Population and Social Research, Mahidol University, Thailand and the Carolina Population Center, University of North Carolina, USA participated in the design, pretest, data collection, and the value-added processing associated with this project. The authors thank two anonymous reviewers for their insightful comments and suggestions.

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  1. Department of Geography and National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, 61801, USA

    Wenwu Tang

  2. Department of Geography and Obermann Center for Advanced Studies, University of Iowa, Iowa City, IA, 52242, USA

    George P. Malanson

  3. Department of Sociology and Carolina Population Center, University of North Carolina, Chapel Hill, NC, 27599, USA

    Barbara Entwisle

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  1. Wenwu Tang
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Correspondence to Wenwu Tang.

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Tang, W., Malanson, G.P. & Entwisle, B. Simulated village locations in Thailand: a multi-scale model including a neural network approach. Landscape Ecol 24, 557–575 (2009). https://doi.org/10.1007/s10980-009-9322-3

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