Browsing by Subject "spatial analysis"
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Item A Copula-Based Closed-Form Binary Logit Choice Model for Accommodating Spatial Correlation Across Observational Units(Springer, 2009) Bhat, Chandra R.; Sener, Ipek N.This study focuses on accommodating spatial dependency in data indexed by geographic location. In particular, the emphasis is on accommodating spatial error correlation across observational units in binary discrete choice models. We propose a copula-based approach to spatial dependence modeling based on a spatial logit structure rather than a spatial probit structure. In this approach, the dependence between the logistic error terms of different observational units is directly accommodated using a multivariate logistic distribution based on the Farlie-Gumbel-Morgenstein (FGM) copula. The approach represents a simple and powerful technique that results in a closedform analytic expression for the joint probability of choice across observational units, and is straightforward to apply using a standard and direct maximum likelihood inference procedure. There is no simulation machinery involved, leading to substantial computation gains relative to current methods to address spatial correlation. The approach is applied to teenagers' physical activity participation levels, a subject of considerable interest in the public health, transportation, sociology, and adolescence development fields. The results indicate that failing to accommodate heteroscedasticity and spatial correlation can lead to inconsistent and inefficient parameter estimates, as well as incorrect conclusions regarding the elasticity effects of exogenous variables.Item A multi-level cross-classified model for discrete response variables(Elsevier, 2000) Bhat, Chandra R.In many spatial analysis contexts, the variable of interest is discrete and there is spatial clustering of observations. This paper formulates a model that accommodates clustering along more than one dimension in the context of a discrete response variable. For example, in a travel mode choice context, individuals are clustered by both the home zone in which they live as well as by their work locations. The model formulation takes the form of a mixed logit structure and is estimated by maximum likelihood using a combination of Gaussian quadrature and quasi- Monte Carlo simulation techniques. An application to travel mode choice suggests that ignoring the spatial context in which individuals make mode choice decisions can lead to an inferior data fit as well as provide inconsistent evaluations of transportation policy measures.Item The spatial analysis of activity stop generation(Elsevier, 2002) Bhat, Chandra R.; Zhao, HuiminTravel demand analysis is intrinsically spatial; yet spatial analysis considerations are seldom recognized and accommodated in travel modeling. The objective of this paper is to identify the spatial issues that need to be recognized in demand modeling, and to propose a multi-level, mixed logit, formulation to address these spatial issues in the context of activity stop generation. The multi-level model is estimated using the maximum simulated likelihood method. Empirical results obtained from applying the model to study shopping activity stop generation in the Boston metropolitan area are presented and discussed.