Abstract
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.
Highlights
Capturing the space-time dynamics of human activities is essential for understanding human security issues, such as climate change impacts, pandemic spreading, or urban sustainability [1,2,3,4]
Along with the vigorous development of big data in daily life, real time data such as mobile phone signaling data, GPS data, and location-based services are regarded as the main source for tracing human mobility [5]
Total population, the human mobility data generated by Weibo data represent a tiny frac tion of the totaldescription data
Summary
Capturing the space-time dynamics of human activities is essential for understanding human security issues, such as climate change impacts, pandemic spreading, or urban sustainability [1,2,3,4]. Open geotagged social media data has become an important and popular source of spatial analysis, providing spatiotemporal dynamics of user activities [6]. Results of such studies may be largely affected by the quality of the data. Studies have pointed out that human mobility patterns derived from social media data have an obvious bias compared with the real world and may be limited by the characteristics of social media users and their tweets-posting behaviors such as gender [7], posting habits [8,9], and various user ratios in different geographic locations [10]. The geotags may be different from the real location of the user [13]
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