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A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns

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  • Nazneen Ferdous

    ()

  • Chandra Bhat

    ()

Abstract

This paper proposes and estimates a spatial panel ordered-response probit model with temporal autoregressive error terms to analyze changes in urban land development intensity levels over time. Such a model structure maintains a close linkage between the land owner’s decision (unobserved to the analyst) and the land development intensity level (observed by the analyst) and accommodates spatial interactions between land owners that lead to spatial spillover effects. In addition, the model structure incorporates spatial heterogeneity as well as spatial heteroscedasticity. The resulting model is estimated using a composite marginal likelihood (CML) approach that does not require any simulation machinery and that can be applied to data sets of any size. A simulation exercise indicates that the CML approach recovers the model parameters very well, even in the presence of high spatial and temporal dependence. In addition, the simulation results demonstrate that ignoring spatial dependency and spatial heterogeneity when both are actually present will lead to bias in parameter estimation. A demonstration exercise applies the proposed model to examine urban land development intensity levels using parcel-level data from Austin, Texas. Copyright Springer-Verlag 2013

Suggested Citation

  • Nazneen Ferdous & Chandra Bhat, 2013. "A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns," Journal of Geographical Systems, Springer, vol. 15(1), pages 1-29, January.
  • Handle: RePEc:kap:jgeosy:v:15:y:2013:i:1:p:1-29
    DOI: 10.1007/s10109-012-0165-0
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    References listed on IDEAS

    as
    1. 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.
    2. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    3. Kurt J. Beron & James C. Murdoch & Wim P. M. Vijverberg, 2003. "Why Cooperate? Public Goods, Economic Power, and the Montreal Protocol," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 286-297, May.
    4. Ferdous, Nazneen & Eluru, Naveen & Bhat, Chandra R. & Meloni, Italo, 2010. "A multivariate ordered-response model system for adults' weekday activity episode generation by activity purpose and social context," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 922-943, September.
    5. Elena G. Irwin, 2002. "Interacting agents, spatial externalities and the evolution of residential land use patterns," Journal of Economic Geography, Oxford University Press, vol. 2(1), pages 31-54, January.
    6. Chandra Bhat & Ipek Sener, 2009. "A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units," Journal of Geographical Systems, Springer, vol. 11(3), pages 243-272, September.
    7. Matthew Roorda & Antonio Páez & Catherine Morency & Ruben Mercado & Steven Farber, 2010. "Trip generation of vulnerable populations in three Canadian cities: a spatial ordered probit approach," Transportation, Springer, vol. 37(3), pages 525-548, May.
    8. Michael Brady & Elena Irwin, 2011. "Accounting for Spatial Effects in Economic Models of Land Use: Recent Developments and Challenges Ahead," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(3), pages 487-509, March.
    9. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521194204.
    10. Cristiano Varin & Paolo Vidoni, 2005. "A note on composite likelihood inference and model selection," Biometrika, Biometrika Trust, vol. 92(3), pages 519-528, September.
    11. Arbia, Giuseppe & Kelejian, Harry, 2010. "Advances in spatial econometrics," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 253-254, September.
    12. Carmen Carrión-Flores & Elena G. Irwin, 2004. "Determinants of Residential Land-Use Conversion and Sprawl at the Rural-Urban Fringe," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 889-904.
    13. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
    14. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
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    Citations

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    Cited by:

    1. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
    2. repec:gam:jsusta:v:11:y:2019:i:5:p:1434-:d:212114 is not listed on IDEAS
    3. repec:eee:ecolec:v:147:y:2018:i:c:p:134-154 is not listed on IDEAS
    4. Raja Chakir & Thibault Laurent & Anne Ruiz-Gazen & Christine Thomas-Agnan & Céline Vignes, 2017. "Prédiction de l’usage des sols sur un zonage régulier à différentes résolutions et à partir de covariables facilement accessibles," Revue économique, Presses de Sciences-Po, vol. 68(3), pages 435-469.
    5. Bhat, Chandra R. & Pinjari, Abdul R. & Dubey, Subodh K. & Hamdi, Amin S., 2016. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 240-263.
    6. repec:eee:transb:v:109:y:2018:i:c:p:238-256 is not listed on IDEAS
    7. Chandra R. Bhat & Subodh K. Dubey & Mohammad Jobair Bin Alam & Waleed H. Khushefati, 2015. "A New Spatial Multiple Discrete-Continuous Modeling Approach To Land Use Change Analysis," Journal of Regional Science, Wiley Blackwell, vol. 55(5), pages 801-841, November.
    8. repec:eee:jotrge:v:54:y:2016:i:c:p:218-227 is not listed on IDEAS
    9. repec:gam:jlands:v:7:y:2018:i:2:p:62-:d:145362 is not listed on IDEAS

    More about this item

    Keywords

    Spatial econometrics; Panel data; Random coefficients; Urban land development intensity; Composite marginal likelihood (CML) approach; C13; C18; C33; C35; C51; R14; R15;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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