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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

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  • 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

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    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.
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    Cited by:

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    3. 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.
    4. 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.
    5. 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.
    6. António Xavier & Rui Fragoso & Maria De Belém Costa Freitas & Maria Do Socorro Rosário & Florentino Valente, 2018. "A Minimum Cross-Entropy Approach to Disaggregate Agricultural Data at the Field Level," Land, MDPI, Open Access Journal, vol. 7(2), pages 1-16, May.
    7. Emre Tepe & Jean-Michel Guldmann, 2020. "Spatio-temporal multinomial autologistic modeling of land-use change: A parcel-level approach," Environment and Planning B, , vol. 47(3), pages 473-488, March.
    8. Qian Qian & Haiyan Liu & Xinqi Zheng, 2019. "A Regional Sustainable Intensive Land Use Evaluation Based on Ecological Constraints: A Case Study in Jinan City," Sustainability, MDPI, Open Access Journal, vol. 11(5), pages 1-20, March.
    9. Bhat, Chandra R., 2018. "New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 238-256.
    10. Faghih-Imani, Ahmadreza & Eluru, Naveen, 2016. "Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system," Journal of Transport Geography, Elsevier, vol. 54(C), pages 218-227.

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    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;
    All these keywords.

    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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