| Autor: |
Xiao Huang, Yago Martin, Siqin Wang, Mengxi Zhang, Xi Gong, Yue Ge, Zhenlong Li |
| Rok vydání: |
2022 |
| Předmět: |
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| DOI: |
10.6084/m9.figshare.19131625.v1 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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