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Application of the dynamic spatial ordered probit model:

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  • Xiaokun Wang
  • Kara M. Kockelman

Abstract

The evolution of land development in urban area has been of great interest to policy-makers and planners. Due to the complexity of the land development process, no existing studies are considered sophisticated enough. This research uses the dynamic spatial ordered probit (DSOP) model to analyse Austin's land use intensity patterns over a 4-point panel. The observational units are 300 m × 300 m grid cells derived from satellite images. The sample contains 2,771 such grid cells, spread among 57 zip code regions. The marginal effects of control variables suggest that increases in travel times to central business district (CBD) substantially reduce land development intensity. More important, temporal and spatial autocorrelation effects are significantly positive, showing the superiority of the DSOP model. The derived parameters are used to predict future land development patterns, along with associated uncertainty in each grid cell's prediction. Copyright (c) 2009 the author(s). Journal compilation (c) 2009 RSAI.

Suggested Citation

  • Xiaokun Wang & Kara M. Kockelman, 2009. "Application of the dynamic spatial ordered probit model:," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 345-365, June.
  • Handle: RePEc:bla:presci:v:88:y:2009:i:2:p:345-365
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    1. Gerald C. Nelson & Daniel Hellerstein, 1997. "Do Roads Cause Deforestation? Using Satellite Images in Econometric Analysis of Land Use," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 80-88.
    2. Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
    3. Bin Zhou & Kara Kockelman, 2008. "Neighborhood impacts on land use change: a multinomial logit model of spatial relationships," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(2), pages 321-340, June.
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