A hierarchical Bayesian logit model for spatial multivariate choice data

Yuki Oyama, Daisuke Murakami and Rico Krueger

Journal of choice modelling, 2024, vol. 52, issue C

Abstract: Spatial perceptions mediate human–environment interaction, and understanding spatial perceptions of humans can play a key role in the planning of activities. This study aims to analyze spatial multivariate binary choice data representing if an individual perceives a spatial unit to belong to a certain category (e.g., her neighborhood or set of potential activity places). To reasonably analyze such data, we present a spatial autoregressive mixed logit (SAR-MXL) model that accounts for both inter-individual heterogeneity and spatial dependence. We rely on the Bayesian approach for posterior inference of model parameters, where Pólya-Gamma data augmentation (PG-DA) is adopted to address the non-conjugacy of the logit kernel. The PG-DA technique eliminates the need for the Metropolis–Hastings step during the Markov Chain Monte Carlo process and allows for fast and efficient posterior inference. The high efficiency of the Bayesian SAR-MXL model is demonstrated through a numerical experiment. The proposed framework is applied to street-based neighborhood perception data, and we empirically analyzed the factors associated with the street perception probability of individuals. The result suggests a clear improvement of the model fit by incorporating spatial dependence and random parameters.

Keywords: Spatial choice data; Spatial autoregressive model; Mixed logit; Bayesian estimation; Pólya-Gamma data augmentation; Neighborhood perception (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:52:y:2024:i:c:s1755534524000356

DOI: 10.1016/j.jocm.2024.100503

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