{"title":"空间多变量选择数据的分层贝叶斯逻辑模型","authors":"Yuki Oyama , Daisuke Murakami , Rico Krueger","doi":"10.1016/j.jocm.2024.100503","DOIUrl":null,"url":null,"abstract":"<div><p>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 (<em>e.g.</em>, 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.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"52 ","pages":"Article 100503"},"PeriodicalIF":2.8000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755534524000356/pdfft?md5=9f9d9bc0d37a8a1083ea705dbc2dc28b&pid=1-s2.0-S1755534524000356-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A hierarchical Bayesian logit model for spatial multivariate choice data\",\"authors\":\"Yuki Oyama , Daisuke Murakami , Rico Krueger\",\"doi\":\"10.1016/j.jocm.2024.100503\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 (<em>e.g.</em>, 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.</p></div>\",\"PeriodicalId\":46863,\"journal\":{\"name\":\"Journal of Choice Modelling\",\"volume\":\"52 \",\"pages\":\"Article 100503\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1755534524000356/pdfft?md5=9f9d9bc0d37a8a1083ea705dbc2dc28b&pid=1-s2.0-S1755534524000356-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Choice Modelling\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1755534524000356\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Choice Modelling","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755534524000356","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
A hierarchical Bayesian logit model for spatial multivariate choice data
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.