The value of network data confirmed by the Covid-19 epidemic and its expanded usages

IF 1.8 Q3 PUBLIC ADMINISTRATION
Data & policy Pub Date : 2022-01-19 DOI:10.1017/dap.2021.31
P. Chambreuil, Ju Y. Jeon, Thierry Barba
{"title":"The value of network data confirmed by the Covid-19 epidemic and its expanded usages","authors":"P. Chambreuil, Ju Y. Jeon, Thierry Barba","doi":"10.1017/dap.2021.31","DOIUrl":null,"url":null,"abstract":"Abstract Data driven analysis is proven to create a competitive advantage to business. Governments and nonprofit organizations also turn to Big Data to harness its benefits and use it for social good. Among different types of data sources, location data collected from mobile networks is especially valuable for its representativeness, real-time observation, and versatility. There is a distinction between mobile positioning data (MPD) generated by the exchanges between mobile devices and the core network; versus over-the-top or system-level location data collecting individual GPS location. MPD is composed of all mobile network events regardless of the mobile phone brand, operating system, app usage, frequency bands or mobile generation; it is uniform and ubiquitous. Getting the best out of MPD relies on the knowledge of how to create an advanced algorithm for homogeneously processing this massive, complex data into insightful indicators. Anonymized and aggregated MPD enables the testing of multiple combinations with other data sources, fully abiding by GDPR, to arrive at innovative solutions. These unique insights can help tackle societal challenges (the state of mobile data for social good June 2017 GSMA, UN Global pulse). It can help to establish accurate statistics about population movements, density, location, social patterns, finances, and ambient environmental conditions. This article demonstrates how MPD has been used to help combat Covid-19 in Europe, the Middle East, and Africa. Furthermore, depending on the future direction, MPD and data analysis can serve powering economic development as well as working toward the Sustainable Development Goals, whilst respecting data privacy.","PeriodicalId":93427,"journal":{"name":"Data & policy","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data & policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dap.2021.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC ADMINISTRATION","Score":null,"Total":0}
引用次数: 2

Abstract

Abstract Data driven analysis is proven to create a competitive advantage to business. Governments and nonprofit organizations also turn to Big Data to harness its benefits and use it for social good. Among different types of data sources, location data collected from mobile networks is especially valuable for its representativeness, real-time observation, and versatility. There is a distinction between mobile positioning data (MPD) generated by the exchanges between mobile devices and the core network; versus over-the-top or system-level location data collecting individual GPS location. MPD is composed of all mobile network events regardless of the mobile phone brand, operating system, app usage, frequency bands or mobile generation; it is uniform and ubiquitous. Getting the best out of MPD relies on the knowledge of how to create an advanced algorithm for homogeneously processing this massive, complex data into insightful indicators. Anonymized and aggregated MPD enables the testing of multiple combinations with other data sources, fully abiding by GDPR, to arrive at innovative solutions. These unique insights can help tackle societal challenges (the state of mobile data for social good June 2017 GSMA, UN Global pulse). It can help to establish accurate statistics about population movements, density, location, social patterns, finances, and ambient environmental conditions. This article demonstrates how MPD has been used to help combat Covid-19 in Europe, the Middle East, and Africa. Furthermore, depending on the future direction, MPD and data analysis can serve powering economic development as well as working toward the Sustainable Development Goals, whilst respecting data privacy.
新冠肺炎疫情确认的网络数据价值及其扩展用途
数据驱动分析已被证明可以为企业创造竞争优势。政府和非营利组织也转向利用大数据的好处,并将其用于社会公益。在不同类型的数据源中,从移动网络收集的位置数据因其代表性、实时性和通用性而具有特别的价值。移动设备与核心网之间交换产生的移动定位数据(MPD)是有区别的;与收集个人GPS位置的顶级或系统级位置数据相比。MPD由所有移动网络事件组成,无论手机品牌、操作系统、应用程序使用情况、频段或移动一代;它是统一的,无处不在的。充分利用MPD依赖于如何创建一种先进的算法,将这些大量复杂的数据均匀地处理成有洞察力的指标。匿名和聚合MPD可以测试与其他数据源的多种组合,完全遵守GDPR,从而得出创新的解决方案。这些独特的见解有助于应对社会挑战(2017年6月GSMA, UN Global pulse,移动数据用于社会公益的状态)。它可以帮助建立关于人口流动、密度、位置、社会模式、财务和环境条件的准确统计数据。本文展示了MPD如何用于帮助欧洲、中东和非洲抗击Covid-19。此外,根据未来的发展方向,MPD和数据分析可以在尊重数据隐私的同时,为经济发展和可持续发展目标提供动力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
3.10
自引率
0.00%
发文量
0
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信