The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data

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• 作者：Xiaokun (Cara) Wang ; Kara M. Kockelman ; Jason D. Lemp
• 关键词：Multinomial probit ; Spatial autocorrelation ; Land use change ; Discrete panel data
• 刊名：Journal of Transport Geography
• 出版年：2012
• 出版时间：September, 2012
• 年：2012
• 卷：24
• 期：Complete
• 页码：77-88
• 全文大小：1883 K

文摘

Many transportation-related behaviors involve multinomial discrete response in a temporal and spatial context. These include quality of paved roadway sections over time, evolution of land use at the parcel level, vehicle purchases by socially networked households, and mode choices by individuals residing across adjacent homes or neighborhoods. Such responses depend on various influential factors, and can have temporal and spatial dependence or autocorrelation. In many cases, dynamic spatial-model specifications based on maximum fitness, profit or utility may be most appropriate.

This study develops a dynamic spatial multinomial probit (DSMNP) model by pivoting off the ordinary MNP model while incorporating spatial and temporal dependencies. The study adds value to existing work by addressing polytomous outcomes and space-time data. (Most spatial models rely on cross-sectional data sets and/or binary outcomes.) The paper first explains how the model reflects behaviors at play, and then describes estimation using Bayesian methods, which are of great interest in multiple fields. Simulated data sets containing both generic and alternative-specific explanatory variables are used to validate the model¡¯s performance (and that of its associated code). Estimation efficiency issues and identification issues are discussed. The model is then applied to analyze parcel-level land use changes in Austin, Texas. It is found that better accessibility boosts the potential of residential development while hampering non-residential development. The effects of job and population density, neighborhood income and soil slope are also explored, and found to exert variable effects across space. It is also found that land development tends to cluster when existing development intensity in a neighborhood is low.