Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility
Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility
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DOI：
10.3390/ijgi11020145
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发表时间：
2022-02-01
影响因子：
3.4
通讯作者：
Liu, Hongqiang
中科院分区：
文献类型：
--
作者：
Liu, Lingbo;Wang, Ru;Liu, Hongqiang
了解人类活动的时空动态对于研究气候变化影响、流行病蔓延或城市可持续性等人类安全问题至关重要。带地理标记的社交媒体帖子提供了一个开放的、时空连续的数据源和用户位置，这为研究人类运动提供了便利。然而，中国带有地理标记的社交媒体数据代表人类流动性的可靠性尚不清楚。这项研究比较了新浪微博和百度千玺的人体运动数据。新浪微博是中国最大的社交媒体软件之一，百度千玺是中国的高分辨率人体运动数据库。百度地图是中国的一项基于位置的服务，在中国拥有13亿用户。从时间周期(周和月)、地理尺度(省市)和流向(流入和流出)多个维度进行相关性分析，并利用这些数据进一步探讨新冠肺炎传播的案例研究。结果显示，新浪微博数据可以呈现出与百度千玺相似的规律，且省级高于市级，月级高于周级。这项研究还揭示了这两个来源之间相似程度的空间差异。本研究的结果揭示了从微博上提取的人类流动性数据的价值和性质以及时空异质性，为正确使用社交媒体帖子作为人类流动性研究的数据源提供了参考。
Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from 'Baidu Map', a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.