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Spatial Random Slope Multilevel Modeling Using Multivariate Conditional Autoregressive Models: A Case Study of Subjective Travel Satisfaction in Beijing

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  • Guanpeng Dong
  • Jing Ma
  • Richard Harris
  • Gwilym Pryce

Abstract

This article explores how to incorporate a spatial dependence effect into the standard multilevel modeling (MLM). The proposed method is particularly well suited to the analysis of geographically clustered survey data where individuals are nested in geographical areas. Drawing on multivariate conditional autoregressive models, we develop a spatial random slope MLM approach to account for the within-group dependence among individuals in the same area and the spatial dependence between areas simultaneously. Our approach improves on recent methodological advances in the integrated spatial and MLM literature, offering greater flexibility in terms of model specification by allowing regression coefficients to be spatially varied. Bayesian Markov chain Monte Carlo (MCMC) algorithms are derived to implement the proposed model. Using two-level travel satisfaction data in Beijing, we apply the proposed approach as well as the standard nonspatial random slope MLM to investigate subjective travel satisfaction of residents and its determinants. Model comparison results show strong evidence that the proposed method produces a significant improvement against a nonspatial random slope MLM. A fairly large spatial correlation parameter suggests strong spatial dependence in district-level random effects. Moreover, spatial patterns of district-level random effects of locational variables have been identified, with high and low values clustering together.

Suggested Citation

  • Guanpeng Dong & Jing Ma & Richard Harris & Gwilym Pryce, 2016. "Spatial Random Slope Multilevel Modeling Using Multivariate Conditional Autoregressive Models: A Case Study of Subjective Travel Satisfaction in Beijing," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(1), pages 19-35, January.
    • Handle: RePEc:taf:raagxx:v:106:y:2016:i:1:p:19-35
    DOI: 10.1080/00045608.2015.1094388
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    Cited by:

    1. Eduardo Pérez-Molina, 2022. "Exploring a multilevel approach with spatial effects to model housing price in San José, Costa Rica," Environment and Planning B, , vol. 49(3), pages 987-1004, March.
    2. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    3. Abenoza, Roberto F. & Liu, Chengxi & Cats, Oded & Susilo, Yusak O., 2019. "What is the role of weather, built-environment and accessibility geographical characteristics in influencing travelers’ experience?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 34-50.
    4. Mauricio Sarrias, 2020. "Random Parameters and Spatial Heterogeneity using Rchoice in R," REGION, European Regional Science Association, vol. 7, pages 1-19.
    5. Keyu Zhai & Xing Gao & Yuerong Zhang & Meiling Wu, 2019. "Perceived Sustainable Urbanization Based on Geographically Hierarchical Data Structures in Nanjing, China," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    6. Wu, Wenjie & Dong, Guanpeng & SUN, Yeran & Yun, Yanwen, 2020. "Contextualized effects of Park access and usage on residential satisfaction: A spatial approach," Land Use Policy, Elsevier, vol. 94(C).
    7. Sue Easton & Gwilym Pryce, 2019. "Not so welcome here? Modelling the impact of ethnic in-movers on the length of stay of home-owners in micro-neighbourhoods," Urban Studies, Urban Studies Journal Limited, vol. 56(14), pages 2847-2862, November.
    8. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    9. Van Acker, Veronique & Ho, Loan & Mulley, Corinne, 2021. "“Satisfaction lies in the effort”. Is Gandhi’s quote also true for satisfaction with commuting?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 214-227.

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