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Application of the dynamic spatial ordered probit model: Patterns of land development change in Austin, Texas

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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. Resumen La evolución del desarrollo del suelo en áreas urbanas ha sido de gran interés para formuladores de políticas y urbanistas. Debido a la complejidad del proceso de desarrollo urbano, se considera que los estudios existentes no son lo suficientemente sofisticados. Este estudio utiliza el modelo probit ordenado espacial dinámico (DSOP, por sus siglas en inglés) para analizar los patrones de intensidad de uso del suelo sobre un panel de 4 puntos. Las unidades de estudio son celdas en una malla de 300m x 300 m a partir de imágenes de satélite. La muestra contiene 2,771 de estas celdas, distribuidas entre 57 regiones de códigos postales. Los efectos marginales de las variables de control sugieren que los incrementos en la duración de los desplazamientos al distrito central de negocios (CBD, por sus siglas en inglés) reducen sustancialmente la intensidad del desarrollo urbano del suelo. Con mayor importancia, los efectos de autocorrelación temporal y espacial son significativamente positivos, mostrando la superioridad del modelo DSOP. Los parámetros derivados son utilizados para predecir patrones futuros de desarrollo urbano del suelo, junto con la incertidumbre asociada a la predicción para cada celda de la malla.

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  • Xiaokun Wang & Kara M. Kockelman, 2009. "Application of the dynamic spatial ordered probit model: Patterns of land development change in Austin, Texas," 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
    DOI: 10.1111/j.1435-5957.2009.00249.x
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    3. Hone-Jay Chu & Chen-Fa Wu & Yu-Pin Lin, 2013. "Incorporating Spatial Autocorrelation with Neural Networks in Empirical Land-Use Change Models," Environment and Planning B, , vol. 40(3), pages 384-404, June.
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    6. 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.
    7. William Greene, 2014. "Models for ordered choices," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 15, pages 333-362, Edward Elgar Publishing.
    8. Feng Li & Guangdong Li & Weishan Qin & Jing Qin & Haitao Ma, 2018. "Identifying Economic Growth Convergence Clubs and Their Influencing Factors in China," Sustainability, MDPI, vol. 10(8), pages 1-21, July.
    9. Hui Zhao & Ao Lei & Yuhui Li & Dingjun Hong, 2023. "The Sectoral and Regional Peer Influences on Heavy-Pollution Corporate Environmental, Social, and Governance Performance," Sustainability, MDPI, vol. 15(17), pages 1-42, August.
    10. Meena Badade & T. V. Ramanathan, 2020. "Probabilistic frontier regression model for multinomial ordinal type output data," Journal of Productivity Analysis, Springer, vol. 53(3), pages 339-354, June.
    11. Richard Iovanna & Colin Vance, 2013. "Land conversion and market equilibrium: insights from a simulated landscape," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 50(1), pages 169-184, February.
    12. Carrión-Flores, Carmen E. & Flores-Lagunes, Alfonso & Guci, Ledia, 2018. "An estimator for discrete-choice models with spatial lag dependence using large samples, with an application to land-use conversions," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 77-93.
    13. Daniel P. McMillen & Elizabeth T. Powers, 2017. "The eldercare landscape: Evidence from California," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 139-157, September.
    14. Giuseppe Arbia, 2011. "A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association (2007--2011)," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 377-395, July.
    15. Wang, Yiyi & Kockelman, Kara M. & Wang, Xiaokun (Cara), 2013. "Understanding spatial filtering for analysis of land use-transport data," Journal of Transport Geography, Elsevier, vol. 31(C), pages 123-131.
    16. Si, Chengyu & Nadolnyak, Denis, 2018. "The Effects of Government Payments on Agricultural Land Use," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266628, Southern Agricultural Economics Association.
    17. 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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    19. Páez, Antonio & López, Fernando A. & Ruiz, Manuel & Morency, Catherine, 2013. "Development of an indicator to assess the spatial fit of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 217-233.
    20. Wang, Xiaokun (Cara) & Kockelman, Kara M. & Lemp, Jason D., 2012. "The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data," Journal of Transport Geography, Elsevier, vol. 24(C), pages 77-88.

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