文献类型：期刊文献

英文题名：A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data

作者：Peng, Xia[1,2];Huang, Zhou[3]

第一作者：Peng, Xia;彭霞

通讯作者：Huang, Z[1]

机构：[1]Beijing Union Univ, Collaborat Innovat Ctr eTourism, Inst Tourism, Beijing 100096, Peoples R China;[2]Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;[3]Peking Univ, Inst Remote Sensing & GIS, Beijing 100080, 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 & GIS, Beijing 100080, Peoples R China.

年份：2017

卷号：6

期号：7

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

收录：;Scopus(收录号：2-s2.0-85026361818);WOS:【SCI-EXPANDED(收录号:WOS:000407506900033)】；

基金：This research was supported by grants from the National Key Research and Development Program of China (2017YFB0503602), the National Natural Science Foundation of China (41501162, 41401449, 41625003), the Scientific Research Key Program of Beijing Municipal Commission of Education (KM201611417004), New Starting Point Program of Beijing Union University (ZK10201501), and State Key Laboratory of Resources and Environmental Information System.

语种：英文

外文关键词：social media; geographical big data; tourist attraction; popularity analysis

摘要：In the big data era, the social media data that contain users' geographical locations are growing explosively. These kinds of spatiotemporal data provide a new perspective for us to observe the human movement behavior. By mining such spatiotemporal data, we can incorporate the users' collective wisdom, build novel services and bring convenience to people. Through spatial clustering of the original user locations, both the 'natural' boundaries and the human activity information of the tourist attractions are generated, which facilitate performing popularity analysis of tourist attractions and extracting the travelers' spatio-temporal patterns or travel laws. On the one hand, the potential extracted knowledge could provide decision supports to the tourism management department in both tourism planning and resource development; on the other hand, the travel preferences are able to be extracted from the clustering-generated attractions, and thus, intelligent tourism recommendation services could be developed for the tourist to promote the realization of 'smart tourism'. Hence, this paper proposes a new method for discovering popular tourist attractions, which extracts hotspots through integrating spatial clustering and text mining approaches. We carry out tourist attraction discovery experiments based on the Flickr geotagged images within the urban area of Beijing from 2005 to 2016. The results show that compared with the traditional DBSCAN method, this novel approach can distinguish adjacent high-density areas when discovering popular tourist attractions and has better adaptability in the case of an uneven density distribution. In addition, based on the finding results of scenic hotspots, this paper analyzes the popularity distribution laws of Beijing's tourist attractions under different temporal and weather contexts.