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- Title
基于社交媒体数据的北京市游客与居民签到差异研究.
- Authors
屈树学; 董 琪; 秦嘉徽; 刘雨思; 张 晶
- Abstract
The study of urban spatial differentiation is of great significance to urban planning, resource allocation of tourist destination and optimization of public transportation. In recent years, with the wide application of big data, social media data with geographic tags provide new directions and threads for urban research. Based on the microblogging check-in data of the six core districts of Beijing in 2016, this paper classifies tourists and local residents according to the time characteristics, location characteristics and check-in frequency characteristics through machine learning method. Next, Anselin Local Moran7s I and hierarchical clustering based on check-in POI types are used to identify the check-in area types in fine-grained check-in clusters and explore the differences between the spatial distribution and check-in types of the two groups. The results showed that the eigenvalues ol all the evaluation indexes of the classification model adopted in this paper are above 0・ 9, which is greatly improved compared with previous classification results・ The social media check-in characteristics ol tourists and residents based on this model are significantly different・ Tourists7 check-in mainly focuses on scenic spots, sports leisure and catering services around the Forbidden City, while residents7 check-in is scattered and scientific, educational and cultural services and commercial housing are prominent check-in areas. It also found that n Changpu River Parkn and other significant difference areas with more residents7 check-in and fewer tourists7 check-in・ The use of social media data in group classification can accurately capture the activity types, characteristics and contrasts of different groups, which provides reference and help for revealing the urban spatial differentiation, exploring the activity differences within the city, and promoting urban development.
- Subjects
BEIJING (China); URBAN research; FORBIDDEN City (Beijing, China); URBAN growth; HIERARCHICAL clustering (Cluster analysis); CATERING services; MACHINE learning; URBAN planning
- Publication
Geography & Geographic Information Science, 2022, Vol 38, Issue 1, p37
- ISSN
1672-0504
- Publication type
Article
- DOI
10.3969/j.issn.1672--0504.2022.01.006