A New Spatial Multiple Discrete‐continuous Modeling Approach to Land Use Change Analysis

Abstract

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 heteroscedasticity, 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.

Spatial Limited Dependent Variable Models: A Review Focused on Specification, Estimation, and Health Economics Applications

“…Finally, Bhat et al . () developed a spatial multiple discrete‐continuous probit model to specify and estimate a model of land use change that is capable of predicting both the type and intensity of urban development patterns over large geographic areas. Their formulation also accommodates spatial heterogeneity and heteroskedasticity in the dependent variable, and should be applicable in a wide variety of fields where social and spatial dependencies between decisions' agents lead to spillover effects in multiple discrete‐continuous choices (or states).…”

“…The estimation procedures of the GEV class of models rely on MSL estimation, which is time‐consuming as mentioned in the previous section, while Bhat et al . () consider the composite marginal likelihood proposed in Bhat ().…”

“…Pinjari (2011) has formally obtained the class of multiple discrete-continuous generalized extreme value models, and in particular he tested the existence and extracted the general form of the consumption probability in a closed form, with an application in a household expenditure analysis. Finally, Bhat et al (2015) developed a spatial multiple discrete-continuous probit model to specify and estimate a model of land use change that is capable of predicting both the type and intensity of urban development patterns over large geographic areas. Their formulation also accommodates spatial heterogeneity and heteroskedasticity in the dependent variable, and should be applicable in a wide variety of fields where social and spatial dependencies between decisions' agents lead to spillover effects in multiple discrete-continuous choices (or states).…”

“…Their formulation also accommodates spatial heterogeneity and heteroskedasticity in the dependent variable, and should be applicable in a wide variety of fields where social and spatial dependencies between decisions' agents lead to spillover effects in multiple discrete-continuous choices (or states). The estimation procedures of the GEV class of models rely on MSL estimation, which is time-consuming as mentioned in the previous section, while Bhat et al (2015) consider the composite marginal likelihood proposed in Bhat (2011).…”

Cumulative attraction and spatial dependence in a destination choice model for beach recreation

“…When sites share unobserved attributes that influence choice behaviour this also violates the assumption of independence of error terms in the widely-used multinomial logit model for discrete choices. Spatial heterogeneity, if ignored, may cause substantial bias in model parameters (Bhat, Dubey, Alam, & Khushefati, 2015).…”