The promise of excess mobility analysis: measuring episodic-mobility with geotagged social media data
The promise of excess mobility analysis: measuring episodic-mobility with geotagged social media data
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DOI：
10.1080/15230406.2021.2023366
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发表时间：
2022-02
影响因子：
2.5
通讯作者：
Xiao Huang;Yago Martín;Siqin Wang;Mengxi Zhang;X. Gong;Y. Ge;Zhenlong Li
中科院分区：
文献类型：
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作者：
Xiao Huang;Yago Martín;Siqin Wang;Mengxi Zhang;X. Gong;Y. Ge;Zhenlong Li
在过去十年中，在社交媒体大数据的支持下，人类流动性研究变得越来越重要和多样化，使人类流动性能够以协调和快速的方式得到衡量。然而，目前学术界较少探讨的是情景活动，它是一种特殊的人类活动类型，被定义为由超过正常活动范围的情景事件引发的异常活动。这项研究利用了从2017年到2020年的19亿个带有地理标记的Twitter数据的大规模系统收集，通过对美国县级Twitter访问者进行每日调查，并提出了多种统计方法来识别和量化情节流动性，从而有助于对情节流动性进行第一次实证研究。然后是美国全国范围内的四个情景流动性案例研究，展示了Twitter数据的巨大潜力，以及我们提出的检测周期性和偶发性事件影响的情景流动性的方法。本研究从概念、方法框架和实证知识两个方面对情景迁移研究提供了新的见解，丰富了当前的迁移研究范式。
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.