文献类型：期刊文献

英文题名：Perceiving Beijing's "City Image" Across Different Groups Based on Geotagged Social Media Data

作者：Peng, Xia[1,2];Bao, Yi[3,4,5];Huang, Zhou[3,4,5]

第一作者：Peng, Xia;彭霞

通讯作者：Huang, Z[1];Huang, Z[2];Huang, Z[3]

机构：[1]Beijing Union Univ, Tourism Coll, Collaborat Innovat Ctr Tourism, Beijing 100101, Peoples R China;[2]Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;[3]Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China;[4]Peking Univ, Beijing Key Lab Spatial Informat Integrat & Its A, Beijing 100871, Peoples R China;[5]Peking Univ, Minist Educ, Engn Res Ctr Earth Observat & Nav, Beijing 100871, Peoples R China

第一机构：北京联合大学旅游学院

通讯机构：[1]corresponding author), Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China;[2]corresponding author), Peking Univ, Beijing Key Lab Spatial Informat Integrat & Its A, Beijing 100871, Peoples R China;[3]corresponding author), Peking Univ, Minist Educ, Engn Res Ctr Earth Observat & Nav, Beijing 100871, Peoples R China.

年份：2020

卷号：8

起止页码：93868-93881

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20202408806539);Scopus(收录号：2-s2.0-85086012512);WOS:【SSCI(收录号:WOS:000541123400004)，SCI-EXPANDED(收录号:WOS:000541123400004)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB0503605 and Grant 2017YFE0196100, in part by the National Natural Science Foundation of China under Grant 41771425, and in part by the Beijing Philosophy and Social Science Foundation under Grant 17JDGLB002.

语种：英文

外文关键词：Urban areas; Social network services; Cultural differences; Big Data; Radio spectrum management; Text analysis; City image; geotagged data; hotspots; social media; text analysis

摘要：City image in general refers to the perception, the feeling, and the opinion of a city, which contributes great importance to urban management, urban planning, urban cultural perceptions, and tourism resource development. Traditionally, city image is often inferred by the & x2018;five-element & x2019; model of physical factors while lacking the consideration of subjective perception. With the rising penetration of smart mobile devices and social media, massive data of location-related texts has been generated for a variety of urban areas. The accessibility to the big data leads to a new approach of understanding the subjective perception of city image, which is important since the new approach takes the subjective heterogeneity into account. Based on the Beijing & x2019;s Weibo (microblog) data in the year of 2016, we use a random forest model to categorize user backgrounds into locals and non-locals. Meanwhile, spatial clustering is applied to identify hotspots. Then two text analysis methods & x2013;term frequency-inverse document frequency (TF-IDF) and latent Dirichlet allocation (LDA)& x2013;are adopted to abstract topics regarding the different geographical hotspots in the city across the different groups of individuals. Our research shows text mining on geotagged big data for city image makes it possible to accommodate the heterogeneity of the activities of different groups of people and to understand their preferences for different points of interests in the city, and thereby reveals the socio-cultural and functional features for the city.