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A spatial autoregressive multinomial probit model for anticipating land-use change in Austin, Texas

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  • Yiyi Wang
  • Kara Kockelman
  • Paul Damien

Abstract

This paper develops an estimation strategy for and then applies a spatial autoregressive multinomial probit model to account for both spatial clustering and cross-alternative correlation. Estimation is achieved using Bayesian techniques with Gibbs and the generalized direct sampling (GDS). The model is applied to analyze land development decisions for undeveloped parcels over a 6-year period in Austin, Texas. Results suggest that GDS is a useful method for uncovering parameters whose draws may otherwise fail to converge using standard Metropolis-Hastings algorithms. Estimation results suggest that residential and commercial/civic development tends to favor more regularly shaped and smaller parcels, which may be related to parcel conversion costs and aesthetics. Longer distances to Austin’s central business district increase the likelihood of residential development, while reducing that of commercial/civic and office/industrial uses. Everything else constant, distances to a parcel’s nearest minor, and major arterial roads are estimated to increase development likelihood of commercial/civic and office/industry uses, perhaps because such development is more common in less densely developed locations (as proxied by fewer arterials). As expected, added soil slope is estimated to be negatively associated with residential development, but positively associated with commercial/civic and office/industry uses (perhaps due to some steeper terrains offering view benefits). Estimates of the cross-alternative correlations suggest that a parcel’s residential use “utility” or attractiveness tends to be negatively correlated with that of commercial/civic, but positively associated with that of office/industrial uses, while the latter two land uses exhibit some negative correlation. Using an inverse-distance weight matrix for each parcel’s closest 50 neighbors, the spatial autocorrelation coefficient is estimated to be 0.706, indicating a marked spatial clustering pattern for land development in the selected region. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Yiyi Wang & Kara Kockelman & Paul Damien, 2014. "A spatial autoregressive multinomial probit model for anticipating land-use change in Austin, Texas," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(1), pages 251-278, January.
  • Handle: RePEc:spr:anresc:v:52:y:2014:i:1:p:251-278
    DOI: 10.1007/s00168-013-0584-y
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    1. Taraldsen, Gunnar & Lindqvist, Bo Henry, 2010. "Improper Priors Are Not Improper," The American Statistician, American Statistical Association, vol. 64(2), pages 154-158.
    2. Raja Chakir & Olivier Parent, 2009. "Determinants of land use changes: A spatial multinomial probit approach," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 327-344, June.
    3. Daniel McFadden, 1986. "The Choice Theory Approach to Market Research," Marketing Science, INFORMS, vol. 5(4), pages 275-297.
    4. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
    5. Imai, Kosuke & Van Dyk, David A., 2005. "MNP: R Package for Fitting the Multinomial Probit Model," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i03).
    6. Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December.
    7. Munroe, Darla K. & Southworth, Jane & Tucker, Catherine M., 2002. "The dynamics of land-cover change in western Honduras: exploring spatial and temporal complexity," Agricultural Economics, Blackwell, vol. 27(3), pages 355-369, November.
    8. Manfred M. Fischer & Jinfeng Wang, 2011. "Spatial Data Analysis," SpringerBriefs in Regional Science, Springer, number 978-3-642-21720-3, March.
    9. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    10. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    11. 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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    Keywords

    C31;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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