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

doi:10.3390/ijgi6070216

	A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data
	Peng, Xia 1,2; Huang, Zhou 3
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2017-07-01
卷号	6 期号:7 页码:16
关键词	social media geographical big data tourist attraction popularity analysis
ISSN号	2220-9964
DOI	10.3390/ijgi6070216
通讯作者	Huang, Zhou(huangzhou@pku.edu.cn)
英文摘要	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.
资助项目	National Key Research and Development Program of China[2017YFB0503602] ; National Natural Science Foundation of China[41501162] ; National Natural Science Foundation of China[41401449] ; National Natural Science Foundation of China[41625003] ; Scientific Research Key Program of Beijing Municipal Commission of Education[KM201611417004] ; New Starting Point Program of Beijing Union University[ZK10201501] ; State Key Laboratory of Resources and Environmental Information System
WOS关键词	PHOTOS
WOS研究方向	Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000407506900033
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Scientific Research Key Program of Beijing Municipal Commission of Education ; New Starting Point Program of Beijing Union University ; State Key Laboratory of Resources and Environmental Information System
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/61398]
专题	中国科学院地理科学与资源研究所
通讯作者	Huang, Zhou
作者单位	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
推荐引用方式 GB/T 7714	Peng, Xia,Huang, Zhou. A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(7):16.
APA	Peng, Xia,&Huang, Zhou.(2017).A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(7),16.
MLA	Peng, Xia,et al."A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.7(2017):16.