Multi-level temporal autoregressive modelling of daily activity satisfaction using GPS-integrated activity diary data

ABSTRACT

In this research, we match web-based activity diary data with daily mobility information recorded by GPS trackers for a sample of 709 residents in a 7-day survey in Beijing in 2012 to investigate activity satisfaction. Given the complications arising from the irregular time intervals of GPS-integrated diary data and the associated complex dependency structure, a direct application of standard (spatial) panel data econometric approaches is inappropriate. This study develops a multi-level temporal autoregressive modelling approach to analyse such data, which conceptualises time as continuous and examines sequential correlations via a time or space-time weights matrix. Moreover, we manage to simultaneously model individual heterogeneity through the inclusion of individual random effects, which can be treated flexibly either as independent or dependent. Bayesian Markov chain Monte Carlo (MCMC) algorithms are developed for model implementation. Positive sequential correlations and individual heterogeneity effects are both found to be statistically significant. Geographical contextual characteristics of sites where activities take place are significantly associated with daily activity satisfaction, controlling for a range of situational characteristics and individual socio-demographic attributes. Apart from the conceivable urban planning and development implications of our study, we demonstrate a novel statistical methodology for analysing semantic GPS trajectory data in general.

KEYWORDS:

• Multi-level modelling
• spatial econometrics
• GPS data
• subjective well-being
• semantic trajectories

Acknowledgments

The authors are much grateful for the comments of the reviewers and the editor, which have greatly improved the content of the article. We also thanks Dr Xingjian Liu and Dr Ying Long for their great efforts of making the fine-resolution land use data in Beijing publicly available.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grants No.41601148 and No.41529101; Economic and Social Research Council [ES/P009301/1].

Notes on contributors

Guanpeng Dong

Guanpeng Dong is a lecturer in Geographic Data Science at the Department of Geography and Planning, University of Liverpool. His core research interests include spatial/spatiotemporal statistics and multi-level modeling methodological development and the application in urban studies. E-mail: [email protected]. 

Jing Ma

Jing Ma is an associate professor of Human Geography in Faculty of Geographical Science, Beijing Normal University. Her main research interests include activity-travel behaviour, subjective well-being, environmental justice and health. E-mail: [email protected] or [email protected].

Mei-Po Kwan

Mei-Po Kwan is a professor at the Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign. Her research addresses health, transport, environmental, and social issues through the application of innovative geographic information system (GIS) methods.

Yiming Wang

Yiming Wang is a senior lecturer at the School for Policy Studies, University of Bristol. His research involves analysing economic and social policies through the application of both quantitative and qualitative methods.

Yanwei Chai

Yanwei Chai is a professor at the College of Urban and Environmental Sciences, Peking University. His research interests include behavioural geography and time geography.