Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age

Abstract

In this research, we present a spatio-temporal analytical framework including spatio-temporal visualization (STV), space-time kernel density estimation (STKDE), and spatio-temporal-autocorrelation-analysis (STAA), to explore human mobility patterns and intra-urban communication dynamics. Experiments were conducted using large-scale detailed records of mobile phone calls in a city. The space-time path, time series graphs, vertical Bézier curves, STKDE, STAA, and related techniques in 3D GIS as well as statistical tests have been suggested for different spatio-temporal analysis tasks. We also investigated several statistical measures that extend the classic spatial association indices for spatio-temporal autocorrelation analysis. The spatial order of weighted matrix was found to have more significant effects than the temporal neighbors on influencing the autocorrelation strength of hourly phone calls.

Keywords::

• human mobility
• mobile phone data
• space-time GIS
• spatio-temporal analytics
• spatio-temporal visualization
• space-time kernel density estimation
• spatio-temporal autocorrelation analysis

Acknowledgments

The author would like to thank Dr. Yu Liu at Peking University for providing data support and thank colleagues Dr. Krzysztof Janowicz, Dr. Phaedon C. Kyriakidis and Yingjie Hu at UCSB Geography for insightful discussions and suggestions on this work. Thanks to guest editors Dr. Christophe Claramunt and Dr. Kathleen Stewart and reviewers for their comments that helped to improve this manuscript.