Abstract
ABSTRACTIn 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.
Highlights
The study of human mobility has increasingly resorted to high spatio-temporal resolution data such as GPS trajectories with the fast development of location tracking technologies
This study extends the statistical analysis tools for investigating semantic trajectories by proposing a novel Bayesian multi-level temporal autoregressive model that deals with the issues of time and scale
In terms of geographical contextual or activity space characteristics, land-use mix and urban function are statistically significantly associated with activity satisfaction
Summary
The study of human mobility has increasingly resorted to high spatio-temporal resolution data such as GPS trajectories with the fast development of location tracking technologies. Grinberger and Shoval (2015) detailed a process to construct semantic trajectories by coupling raw GPS traces with residents’ digital activity diaries, whereby trajectory segments were characterised by activity information They demonstrated the usefulness of such semantic trajectories in producing high-level knowledge on urban dynamics and spatial structure. This study extends the statistical analysis tools for investigating semantic trajectories by proposing a novel Bayesian multi-level temporal autoregressive model that deals with the issues of time and scale. The methodology developed in this study treats temporal correlations among outcomes substantively and produces estimates on the strength of how precedent outcomes affect the current one It allows for potential interactions among individuals, which is not modelled in the above studies. We conclude with a summary of findings and discussions on potential limitations of the paper as well as future development
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