The promise of excess mobility analysis: measuring episodic-mobility with geotagged social media data

ABSTRACT

Human mobility studies have become increasingly important and diverse in the past decade with the support of social media big data that enables human mobility to be measured in a harmonized and rapid manner. However, what is less explored in the current scholarship is episodic mobility as a special type of human mobility defined as the abnormal mobility triggered by episodic events excess to the normal range of mobility at large. Drawing on a large-scale systematic collection of 1.9 billion geotagged Twitter data from 2017 to 2020, this study contributes to the first empirical study of episodic mobility by producing a daily Twitter census of visitors at the U.S. county level and proposing multiple statistical approaches to identify and quantify episodic mobility. It is followed by four case studies of episodic mobility in U.S. national wide to showcase the great potential of Twitter data and our proposed method to detect episodic mobility subject to episodic events that occur both regularly and sporadically. This study provides new insights on episodic mobility in terms of its conceptual and methodological framework and empirical knowledge, which enriches the current mobility research paradigm.

KEYWORDS:

• Twitter
• human mobility
• episodic events
• big data

Acknowledgments

We thank Dr. Eric Delmelle and the three anonymous reviewers for their insightful comments that significantly improved the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The computed daily modified Z scores and the derived trend components at the US county level for the 4 years (from 2017 to 2020) are provided as manuscript supplementary material. The Twitter data was collected using Twitter’s public Streaming API from the public domain following Twitter’s Developer Agreement. Following Twitter’s policy on “Redistribution of Twitter content” (https://developer.twitter.com/en/developer-terms/moreon-restricted-use-cases), the geotagged tweet IDs used in this analysis will be provided upon request. The Social Distancing Metrics was live during the peak of the COVID-19 pandemic and is no longer supported (https://docs.safegraph.com/docs/social-distancing-metrics).

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This research is in part supported by the National Science Foundation (NSF) under grant 2028791 and the University of South Carolina ASPIRE program under grant Office of the Vice President for Research, University of South Carolina 135400-20-53382.