{"title":"基于社交媒体签到数据的城市旅游研究","authors":"Kai Yang, W. Wan, Tianyu Xia, Xuan He","doi":"10.1049/CP.2017.0124","DOIUrl":null,"url":null,"abstract":"In this paper we develop a methodology for measuring visitors who come from other country using social media check-in data. Based on a review of the literature on this topic we propose an urban tourism check algorithm which can find the people who are the tourists and find out where the people come from and the route of their visit. We get the data from the Sina Weibo, and the data is composed of the POI data, user check-in data, place check-in data and so on. We choose Shanghai as the research target and analyze the tourism palace Renmin Square, Yu Garden, the Bund and Chenhuang Temple in 2016. We get the number of POI data 518, the user number 213091, and the check-in number 291295. The experimental results show that the number of outside visit is 177718, the rate is 83.4% in all the user, and 151060 visits are real tourist. The accuracy rate is 85.1%. And the distribution of the tourist shows that the most of tourists come from Zhe Jiang, Jiang Su and Bei Jing.","PeriodicalId":424212,"journal":{"name":"4th International Conference on Smart and Sustainable City (ICSSC 2017)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Urban tourism research based on the social media check-in data\",\"authors\":\"Kai Yang, W. Wan, Tianyu Xia, Xuan He\",\"doi\":\"10.1049/CP.2017.0124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we develop a methodology for measuring visitors who come from other country using social media check-in data. Based on a review of the literature on this topic we propose an urban tourism check algorithm which can find the people who are the tourists and find out where the people come from and the route of their visit. We get the data from the Sina Weibo, and the data is composed of the POI data, user check-in data, place check-in data and so on. We choose Shanghai as the research target and analyze the tourism palace Renmin Square, Yu Garden, the Bund and Chenhuang Temple in 2016. We get the number of POI data 518, the user number 213091, and the check-in number 291295. The experimental results show that the number of outside visit is 177718, the rate is 83.4% in all the user, and 151060 visits are real tourist. The accuracy rate is 85.1%. And the distribution of the tourist shows that the most of tourists come from Zhe Jiang, Jiang Su and Bei Jing.\",\"PeriodicalId\":424212,\"journal\":{\"name\":\"4th International Conference on Smart and Sustainable City (ICSSC 2017)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"4th International Conference on Smart and Sustainable City (ICSSC 2017)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1049/CP.2017.0124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"4th International Conference on Smart and Sustainable City (ICSSC 2017)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/CP.2017.0124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Urban tourism research based on the social media check-in data
In this paper we develop a methodology for measuring visitors who come from other country using social media check-in data. Based on a review of the literature on this topic we propose an urban tourism check algorithm which can find the people who are the tourists and find out where the people come from and the route of their visit. We get the data from the Sina Weibo, and the data is composed of the POI data, user check-in data, place check-in data and so on. We choose Shanghai as the research target and analyze the tourism palace Renmin Square, Yu Garden, the Bund and Chenhuang Temple in 2016. We get the number of POI data 518, the user number 213091, and the check-in number 291295. The experimental results show that the number of outside visit is 177718, the rate is 83.4% in all the user, and 151060 visits are real tourist. The accuracy rate is 85.1%. And the distribution of the tourist shows that the most of tourists come from Zhe Jiang, Jiang Su and Bei Jing.