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A New Spatial Multiple Discrete-Continuous Modeling Approach To Land Use Change Analysis

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  • Chandra R. Bhat
  • Subodh K. Dubey
  • Mohammad Jobair Bin Alam
  • Waleed H. Khushefati

Abstract

type="main"> This paper formulates a multiple discrete-continuous probit (MDCP) land use model within a spatially explicit economic structural framework for land use change decisions. The spatial MDCP model is capable of predicting both the type and intensity of urban development patterns over large geographic areas, while also explicitly acknowledging geographic proximity-based spatial dependencies in these patterns. At a methodological level, the paper focuses on specifying and estimating a spatial MDCP model that allows the dependent variable to exist in multiple discrete states with an intensity associated with each discrete state. The formulation also accommodates spatial dependencies, as well as spatial heterogeneity and heteroskedasticity, in the dependent variable, and should be applicable in a wide variety of fields where social and spatial dependencies between decision agents (or observation units) lead to spillover effects in multiple discrete-continuous choices (or states). A simulation exercise is undertaken to evaluate the ability of the proposed maximum approximate composite marginal likelihood (MACML) approach to recover parameters from a cross-sectional spatial MDCP model. The results show that the MACML approach does well in recovering parameters. An empirical demonstration of the approach is undertaken using the city of Austin parcel level land use data.

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  • 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.
    • Handle: RePEc:bla:jregsc:v:55:y:2015:i:5:p:801-841
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    File URL: http://hdl.handle.net/10.1111/jors.12201
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    Cited by:

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    2. Kuriyama, Koichi & Shoji, Yasushi & Tsuge, Takahiro, 2020. "The value of leisure time of weekends and long holidays: The multiple discrete–continuous extreme value (MDCEV) choice model with triple constraints," Journal of choice modelling, Elsevier, vol. 37(C).
    3. Zhou, Yiwei & Wang, Xiaokun & Holguín-Veras, José, 2016. "Discrete choice with spatial correlation: A spatial autoregressive binary probit model with endogenous weight matrix (SARBP-EWM)," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 440-455.
    4. Dubey, Subodh & Sharma, Ishant & Mishra, Sabyasachee & Cats, Oded & Bansal, Prateek, 2022. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 63-95.
    5. 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.
    6. Emre Tepe, 2024. "History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model," Environment and Planning B, , vol. 51(1), pages 7-22, January.
    7. Subodh Dubey & Ishant Sharma & Sabyasachee Mishra & Oded Cats & Prateek Bansal, 2021. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Papers 2109.06169, arXiv.org.

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