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

Guanpeng Dong, Jing Ma*, Mei Po Kwan, Yiming Wang, Yanwei Chai

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)
332 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)2189-2208
Number of pages20
JournalInternational Journal of Geographical Information Science
Volume32
Issue number11
Early online date3 Sep 2018
DOIs
Publication statusPublished - Nov 2018

Keywords

  • Multi-level modelling
  • Spatial econometrics
  • GPS data
  • Subjective well-being
  • Semantic trajectories

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