文献类型：期刊文献

英文题名：Exploring the Spatiotemporal Patterns of Residents' Daily Activities Using Text-Based Social Media Data: A Case Study of Beijing, China

作者：Liu, Jian[1];Meng, Bin[2];Wang, Juan[2];Chen, Siyu[2];Tian, Bin[2];Zhi, Guoqing[2]

第一作者：Liu, Jian

通讯作者：Meng, B[1]

机构：[1]Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China;[2]Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China

第一机构：Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China.|[114172]北京联合大学应用文理学院;[11417]北京联合大学;

年份：2021

卷号：10

期号：6

外文期刊名：ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION

收录：;Scopus(收录号：2-s2.0-85108567284);WOS:【SCI-EXPANDED(收录号:WOS:000666457200001)】；

基金：This research was funded by the National Key Research and Development Program of China (2017 YFB 0503605), the National Natural Science Foundation of China (41671165) and the Academic Research Projects of Beijing Union University (ZK40202001).

语种：英文

外文关键词：social media data; text data mining; BERT; residents' daily activities; spatiotemporal patterns; Beijing

摘要：The use of social media data provided powerful data support to reveal the spatiotemporal characteristics and mechanisms of human activity, as it integrated rich spatiotemporal and textual semantic information. However, previous research has not fully utilized its semantic and spatiotemporal information, due to its technical and algorithmic limitations. The efficiency of the deep mining of textual semantic resources was also low. In this research, a multi-classification of text model, based on natural language processing technology and the Bidirectional Encoder Representations from Transformers (BERT) framework is constructed. The residents' activities in Beijing were then classified using the Sina Weibo data in 2019. The results showed that the accuracy of the classifications was more than 90%. The types and distribution of residents' activities were closely related to the characteristics of the activities and holiday arrangements. From the perspective of a short timescale, the activity rhythm on weekends was delayed by one hour as compared to that on weekdays. There was a significant agglomeration of residents' activities that presented a spatial co-location cluster pattern, but the proportion of balanced co-location cluster areas was small. The research demonstrated that location conditions, especially the microlocation condition (the distance to the nearest subway station), were the driving factors that affected the resident activity cluster patterns. In this research, the proposed framework integrates textual semantic analysis, statistical method, and spatial techniques, broadens the application areas of social media data, especially text data, and provides a new paradigm for the research of residents' activities and spatiotemporal behavior.