{"title":"用大数据衡量旅游业?通过比较被动GPS数据和被动移动数据得出的经验见解","authors":"Dirk Schmücker , Julian Reif","doi":"10.1016/j.annale.2022.100061","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper we aim to classify digital data sources for the measurement of tourist mobility, to establish a set of assessment indicators, and to compare two Big Data sources to gain empirical insights into how we can measure tourism with Big Data. For three holiday destinations in Germany, passive mobile data and passive global positioning systems (GPS) data are compared with reference data from the destinations for twelve weeks in the summer of 2019. Results show that mobile network data are on a plausible level compared to the local reference data and are able to predict the temporal pattern to a very high degree. GPS app-based data also perform well, but are less plausible and precise than mobile network data.</p></div>","PeriodicalId":34520,"journal":{"name":"Annals of Tourism Research Empirical Insights","volume":"3 2","pages":"Article 100061"},"PeriodicalIF":4.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666957922000295/pdfft?md5=35a9bd00014ac8075215ee7bfe534ac8&pid=1-s2.0-S2666957922000295-main.pdf","citationCount":"4","resultStr":"{\"title\":\"Measuring tourism with big data? Empirical insights from comparing passive GPS data and passive mobile data\",\"authors\":\"Dirk Schmücker , Julian Reif\",\"doi\":\"10.1016/j.annale.2022.100061\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper we aim to classify digital data sources for the measurement of tourist mobility, to establish a set of assessment indicators, and to compare two Big Data sources to gain empirical insights into how we can measure tourism with Big Data. For three holiday destinations in Germany, passive mobile data and passive global positioning systems (GPS) data are compared with reference data from the destinations for twelve weeks in the summer of 2019. Results show that mobile network data are on a plausible level compared to the local reference data and are able to predict the temporal pattern to a very high degree. GPS app-based data also perform well, but are less plausible and precise than mobile network data.</p></div>\",\"PeriodicalId\":34520,\"journal\":{\"name\":\"Annals of Tourism Research Empirical Insights\",\"volume\":\"3 2\",\"pages\":\"Article 100061\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666957922000295/pdfft?md5=35a9bd00014ac8075215ee7bfe534ac8&pid=1-s2.0-S2666957922000295-main.pdf\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Tourism Research Empirical Insights\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666957922000295\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HOSPITALITY, LEISURE, SPORT & TOURISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Tourism Research Empirical Insights","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666957922000295","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
Measuring tourism with big data? Empirical insights from comparing passive GPS data and passive mobile data
In this paper we aim to classify digital data sources for the measurement of tourist mobility, to establish a set of assessment indicators, and to compare two Big Data sources to gain empirical insights into how we can measure tourism with Big Data. For three holiday destinations in Germany, passive mobile data and passive global positioning systems (GPS) data are compared with reference data from the destinations for twelve weeks in the summer of 2019. Results show that mobile network data are on a plausible level compared to the local reference data and are able to predict the temporal pattern to a very high degree. GPS app-based data also perform well, but are less plausible and precise than mobile network data.