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Int. J. Environ. Res. Public Health 2018, 15(9), 1868; https://doi.org/10.3390/ijerph15091868

Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China

1
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
2
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
3
Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UK
*
Authors to whom correspondence should be addressed.
Received: 22 July 2018 / Revised: 26 August 2018 / Accepted: 27 August 2018 / Published: 29 August 2018
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Abstract

Although the impacts of built environment on car ownership and use have been extensively studied, limited evidence has been offered for the role of spatial effects in influencing the interaction between built environment and travel behavior. Ignoring the spatial effects may lead to misunderstanding the role of the built environment and providing inconsistent transportation policies. In response to this, we try to employ a two-step modeling approach to investigate the impacts of built environment on car ownership and use by combining multilevel Bayesian model and conditional autocorrelation (CAR) model to control for spatial autocorrelation. In the two-step model, the predicting car ownership status in the first-step model is used as a mediating variable in the second-step car use model. Taking Changchun as a case study, this paper identifies the presence of spatial effects in influencing the effects of built environment on car ownership and use. Meanwhile, the direct and cascading effects of built environment on car ownership and use are revealed. The results show that the spatial autocorrelation exists in influencing the interaction between built environment and car dependency. The results suggest that it is necessary for urban planners to pay attention to the spatial effects and make targeted policy according to local land use characteristics. View Full-Text
Keywords: car ownership; car use; built environment; spatial autocorrelation; multilevel Bayesian model car ownership; car use; built environment; spatial autocorrelation; multilevel Bayesian model
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Wang, X.; Shao, C.; Yin, C.; Zhuge, C. Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China. Int. J. Environ. Res. Public Health 2018, 15, 1868.

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