STUDY ON THE RELATIONSHIP BETWEEN DINING ACTIVITIES OF BEIJING RESIDENTS AND URBAN SPACE BASED ON MULTI-SOURCE BIG DATA
LIU Jian1, MENG Bin2, CHEN Si-yu2, ZHAN Dong-sheng3, CHEN Zhe1
1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
2. College of Applied Arts & Sciences of Beijing Union University, Beijing 100191, China;
3. School of Management, Zhejiang University of Technology, Hangzhou 310023, China
Abstract:This paper constructs a residential spatiotemporal behavior research framework that combines text information mining technology and spatial analysis. By obtaining the Sina Weibo data of Beijing residents in 2017, using the text classification model combining BERT and fast.AI, combining with the LDA model for text theme mining. Analyze the spatial pattern of residents' daily dining activities, and use spatial analysis methods and the Geodetector to explore its influencing factors. The study concluded that the residents' dining activities can be divided into 4 categories of topics, namely friends gathering, daily catering, general catering and special catering. The spatial analysis found that the four types of theme dining activities are mainly distributed within the Third Ring Road, forming a hierarchical distribution pattern centered on the Workers' Stadium-Chaowai-CBD business district. Meanwhile, various theme dining activities also have the common characteristics of dense distribution along important business districts, famous blocks, popular attractions, and large shopping malls. Residents' dining choices have the strongest consistency with the spatial distribution of catering service facilities. It is found that there is a spatial co-location model between residents' dining activities and the urban spatial structure combined with POI data.
刘坚, 孟斌, 陈思宇, 湛东升, 陈喆. 多源大数据下的北京市居民就餐活动与城市空间关系探究[J]. 人文地理, 2021, 36(2): 63-72,183.
LIU Jian, MENG Bin, CHEN Si-yu, ZHAN Dong-sheng, CHEN Zhe. STUDY ON THE RELATIONSHIP BETWEEN DINING ACTIVITIES OF BEIJING RESIDENTS AND URBAN SPACE BASED ON MULTI-SOURCE BIG DATA. HUMAN GEOGRAPHY, 2021, 36(2): 63-72,183.
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