{"title":"通过维基百科页面浏览量估算旅游统计数据","authors":"C. Alis, Adrian Letchford, H. S. Moat, T. Preis","doi":"10.1145/2786451.2786925","DOIUrl":null,"url":null,"abstract":"Decision makers depend on socio-economic indicators to shape the world we inhabit. Reports of these indicators are often delayed due to the effort involved in gathering and aggregating the underlying data. Our increasing interactions with large scale technological systems are generating vast datasets on global human behaviour which are immediately accessible. Here we analyse whether data on how often people view Wikipedia articles might help us to improve estimates of the current number of tourists leaving the UK. Our analyses suggest that in the absence of sufficient history, Wikipedia page views provide an advantage. We conclude that when using adaptive models, Wikipedia usage opens up the possibility to improve estimates of tourism demand.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"126 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Estimating tourism statistics with Wikipedia page views\",\"authors\":\"C. Alis, Adrian Letchford, H. S. Moat, T. Preis\",\"doi\":\"10.1145/2786451.2786925\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Decision makers depend on socio-economic indicators to shape the world we inhabit. Reports of these indicators are often delayed due to the effort involved in gathering and aggregating the underlying data. Our increasing interactions with large scale technological systems are generating vast datasets on global human behaviour which are immediately accessible. Here we analyse whether data on how often people view Wikipedia articles might help us to improve estimates of the current number of tourists leaving the UK. Our analyses suggest that in the absence of sufficient history, Wikipedia page views provide an advantage. We conclude that when using adaptive models, Wikipedia usage opens up the possibility to improve estimates of tourism demand.\",\"PeriodicalId\":93136,\"journal\":{\"name\":\"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference\",\"volume\":\"126 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2786451.2786925\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2786451.2786925","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimating tourism statistics with Wikipedia page views
Decision makers depend on socio-economic indicators to shape the world we inhabit. Reports of these indicators are often delayed due to the effort involved in gathering and aggregating the underlying data. Our increasing interactions with large scale technological systems are generating vast datasets on global human behaviour which are immediately accessible. Here we analyse whether data on how often people view Wikipedia articles might help us to improve estimates of the current number of tourists leaving the UK. Our analyses suggest that in the absence of sufficient history, Wikipedia page views provide an advantage. We conclude that when using adaptive models, Wikipedia usage opens up the possibility to improve estimates of tourism demand.