Measuring spatio-temporal autocorrelation in time series data of collective human mobility

ABSTRACT

Massive spatio-temporal big data about human mobility have become increasingly available. Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment. Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time. The most widely used spatial autocorrelation measurements, such as Moran’s I and local indicators of spatial association (LISA), only apply to static data, so are powerless to spatio-temporal big data about human mobility. Thus, we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data. Then the method was applied to taxi ride data in Beijing, China to reveal the spatial pattern of collective human mobility. The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road, weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road, and almost no spatio-temporal autocorrelation between the roads. Local spatial patterns of taxi travel were also recognized. This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.

KEYWORDS:

• Taxi rides
• human mobility
• spatio-temporal autocorrelation
• Moran index

Additional information

Funding

This work is funded by the National Natural Science Foundation of China [grant numbers 41830645 and 41625003].

Notes on contributors

Yong Gao

Yong Gao received his Ph.D. in Geographic Information Science from Peking University, China. He is currently an associate professor at the Institute of Remote Sensing and Geographic Information System, Peking University. His research interests are in spatial data mining and geographic information science.

Jing Cheng

Jing Cheng received her M.S. degree at the Institute of Remote Sensing and Geographic Information System, Peking University. Her research interest is spatial data analysis.

Haohan Meng

Haohan Meng received his B.S. degree at the School of Earth and Space Sciences, Peking University. He is now an M.S. student at the Institute of Remote Sensing and Geographic Information System, Peking University. His research interests include spatial data analysis and geographic Information science.

Yu Liu

Yu Liu received his Ph.D. degree from Peking University. He is currently a professor of GIScience at the Institute of Remote Sensing and Geographic Information System, Peking University. His research interest mainly concentrates in humanities and social sciences based on big geo-data.