{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:43:23Z","timestamp":1791819803827,"version":"4.3.4"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T00:00:00Z","timestamp":1499904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In the big data era, the social media data that contain users\u2019 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\u2019 collective wisdom, build novel services and bring convenience to people. Through spatial clustering of the original user locations, both the \u2018natural\u2019 boundaries and the human activity information of the tourist attractions are generated, which facilitate performing popularity analysis of tourist attractions and extracting the travelers\u2019 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 \u2018smart tourism\u2019. 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\u2019s tourist attractions under different temporal and weather contexts.<\/jats:p>","DOI":"10.3390\/ijgi6070216","type":"journal-article","created":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T10:31:57Z","timestamp":1499941917000},"page":"216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data"],"prefix":"10.3390","volume":"6","author":[{"given":"Xia","family":"Peng","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of eTourism, Institute of Tourism, Beijing Union University, Beijing 100096, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1255-1913","authenticated-orcid":false,"given":"Zhou","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing &amp; GIS, Peking University, Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cao, L., Luo, J., Gallagher, A., Jin, X., Han, J., and Huang, T.S. 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