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估算游客的流动性。基于推特的新程序。

Estimating mobility of tourists. New Twitter-based procedure.

作者信息

Muñoz-Dueñas Pilar, Martínez-Comesaña Miguel, Martínez-Torres Javier, Bastos-Costas Guillermo

机构信息

Department of Financial Economics and Accounting, Faculty of Economics and Business Sciences, University of Vigo (Universidade de Vigo), 36310 Vigo, Spain.

CINTECX - Research Center in Technologies, Energy, and Industrial Processes, Universidade de Vigo, Rúa Maxwell s/n, 36310 Vigo, Spain.

出版信息

Heliyon. 2023 Feb 13;9(2):e13718. doi: 10.1016/j.heliyon.2023.e13718. eCollection 2023 Feb.

DOI:10.1016/j.heliyon.2023.e13718
PMID:36865461
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9971121/
Abstract

Twitter has been actively researched as a human mobility proxy. Tweets can contain two classes of geographical metadata: the location from which a tweet was published, and the place where the tweet is estimated to have been published. Nevertheless, Twitter also presents tweets without any geographical metadata when querying for tweets on a specific location. This study presents a methodology which includes an algorithm for estimating the geographical coordinates to tweets for which Twitter doesn't assign any. Our objective is to determine the origin and the route that a tourist followed, even if Twitter doesn't return geographically identified data. This is carried out through geographical searches of tweets inside a defined area. Once a tweet is found inside an area, but its metadata contains no explicit geographical coordinates, its coordinates are estimated by iteratively performing geographical searches, with a decreasing geographical searching radius. This algorithm was tested in two touristic villages of Madrid (Spain) and a major city in Canada. A set of tweets without geographical coordinates in these areas were found and processed. The coordinates of a subset of them were successfully estimated.

摘要

推特已被作为人类移动性的代理进行了积极研究。推文可包含两类地理元数据:推文发布的位置以及据估计推文发布的地点。然而,在查询特定位置的推文时,推特也会呈现没有任何地理元数据的推文。本研究提出了一种方法,其中包括一种算法,用于为推特未分配地理坐标的推文估计地理坐标。我们的目标是确定游客的出发地和所走路线,即使推特未返回地理标识数据。这是通过在定义区域内对推文进行地理搜索来实现的。一旦在某个区域内找到一条推文,但其元数据中不包含明确的地理坐标,就通过以递减的地理搜索半径迭代执行地理搜索来估计其坐标。该算法在西班牙马德里的两个旅游村庄和加拿大的一个主要城市进行了测试。在这些区域中发现并处理了一组没有地理坐标的推文。其中一部分推文的坐标被成功估计出来。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/a401ce0f0b4b/gr008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/4735fee5ece3/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/1954f69a99f9/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/09dc1ae1ed42/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/a401ce0f0b4b/gr008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/5f3b66396d5b/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/98596c384a17/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/ad7eb9cc8695/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/8b5fa3993b06/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/4735fee5ece3/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/1954f69a99f9/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/09dc1ae1ed42/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e81/9971121/a401ce0f0b4b/gr008.jpg

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本文引用的文献

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Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges.新兴地理数据源揭示新冠疫情期间的人类流动动态:机遇与挑战
Comput Urban Sci. 2021;1(1):22. doi: 10.1007/s43762-021-00022-x. Epub 2021 Sep 26.
2
Disaster-resilient communication ecosystem in an inclusive society - A case of foreigners in Japan.包容性社会中的抗灾通信生态系统——以在日外国人为例
Int J Disaster Risk Reduct. 2020 Dec;51:101804. doi: 10.1016/j.ijdrr.2020.101804. Epub 2020 Aug 15.
3
Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas.
照片墙、Flickr 或推特:评估社交媒体数据在保护区游客监测中的可用性。
Sci Rep. 2017 Dec 14;7(1):17615. doi: 10.1038/s41598-017-18007-4.
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Geo-located Twitter as proxy for global mobility patterns.基于地理位置的推特作为全球流动模式的代理指标。
Cartogr Geogr Inf Sci. 2014 May 27;41(3):260-271. doi: 10.1080/15230406.2014.890072. Epub 2014 Feb 26.
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Understanding Human Mobility from Twitter.从推特理解人类移动性。
PLoS One. 2015 Jul 8;10(7):e0131469. doi: 10.1371/journal.pone.0131469. eCollection 2015.
6
Collective human mobility pattern from taxi trips in urban area.城市出租车出行的群体人类移动模式。
PLoS One. 2012;7(4):e34487. doi: 10.1371/journal.pone.0034487. Epub 2012 Apr 18.
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The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic.利用 Twitter 追踪美国甲型 H1N1 流感大流行期间的疾病活动和公众关注水平。
PLoS One. 2011 May 4;6(5):e19467. doi: 10.1371/journal.pone.0019467.