[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-113395-en":3,"doc-seo-113395-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},113395,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Assessing Bias in Smartphone Mobility Estimates in Low Income Countries","Governments and practitioners increasingly estimate population mobility using smartphone data, especially during the COVID-19 pandemic. In low-income settings with limited smartphone ownership or mobile internet use, smartphone movement may not represent population movement. The work develops a framework to analyze two biases—selection into technology ownership and selective technology use—and evaluates them using mobile operator records in Uganda, comparing smartphone users with basic/feature phone users.","Public Disclosure Authorized Public Disclosure Authorized  \nAUTHOR ACCEPTED MANUSCRIPT  \nFINAL PUBLICATION INFORMATION  \nAssessing Bias in Smartphone Mobility Estimates in Low Income Countries  \nThe definitive version of the text was subsequently published in  \nProceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies,, 2021-06-28 Published by Association for Computing Machinery and found at [http://dx.doi.org/10.1145/3460112.3471968](http://dx.doi.org/10.1145/3460112.3471968)  \nTHE FINAL PUBLISHED VERSION OF THIS MANUSCRIPT IS AVAILABLE ON THE PUBLISHER’S PLATFORM  \nThis Author Accepted Manuscript is copyrighted by World Bank and published by Association for Computing Machinery. It is posted here by agreement between them. Changes resulting from the publishing process—such as editing, corrections, structural formatting, and other quality control mechanisms—may not be reflected in this version of the text.  \nYou may download, copy, and distribute this Author Accepted Manuscript for noncommercial purposes. Your license is limited by the following restrictions:  \n(1) You may use this Author Accepted Manuscript for noncommercial purposes only under a CC BY-NC-ND  \n3.0 IGO license [http://creativecommons.org/licenses/by-nc-nd/3.0/igo](http://creativecommons.org/licenses/by-nc-nd/3.0/igo).  \n(2) The integrity of the work and identification of the author, copyright owner, and publisher must be preserved in any copy.  \n(3) You must attribute this Author Accepted Manuscript in the following format: This is an Author Accepted  \nManuscript by Milusheva, Sveta; Björkegren, Daniel; Viotti, Leonardo Assessing Bias in Smartphone Mobility Estimates in Low Income Countries © World Bank, published in the Proceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies 2021-06-28 CC BY-NC-ND 3.0 IGO [http://](http://)[ ](http://)[creativecommons.org/licenses/by-nc-nd/3.0/igo](creativecommons.org/licenses/by-nc-nd/3.0/igo) [http://dx.doi.org/10.1145/3460112.3471968](http://dx.doi.org/10.1145/3460112.3471968)  \n© 2022 World Bank  \nAssessing Bias in Smartphone Mobility Estimates in Low Income  \nCountries  \nSveta Milusheva, Daniel Björkegren, Leonardo Viotti  \n2021  \nIt has become common for governments and practitioners to measure mobility using data from smartphones, especially during the COVID-19 pandemic. Yet in countries where few people have smartphones, or use mobile internet, the movement of smartphones may not be a good indicator of the movement of the population. This paper develops a framework for approaching potential bias that can arise when measuring mobility with smartphones. Using mobile phone operator records in Uganda, we compare the mobility of smartphones and the basic and feature phones that are more common. Smartphones have different travel patterns, and decrease mobility substantially more in response to a COVID-19 lockdown. This suggests caution when interpreting smartphone mobility estimates in contexts with low adoption.  \n1 Introduction  \nUnderstanding the mobility of populations is crucial for transportation (35; 23 ; 15 ; 19), the spread of disease (2; 7 ; 25 ; 28 ; 31 ; 37 ; 38 ; 45 ; 39 ; 46), natural disasters (14; 36 ; 22 ; 8), and–during a pandemic–measuring social contact (1; 18 ; 20) . A wide array of recent work uses data collected from the motion of smartphones to infer how people in a society move (9; 11 ; 12 ; 27 ; 30 ; 29) . Under the COVID-19 pandemic this type of analysis has crossed into the mainstream, with a proliferation of analysis using providers like Google Mobility Reports, Facebook, Unacast, Cuebiq, SafeGraph, and Baidu. But this raises the question: do smartphones move in the same way as the population? This is a concern particularly in societies where few people own smartphones. Smartphone owners are likely to be wealthier, may live in different areas, and may move differently. If so, smartphone mobility estimates may be misleading about how populatio","cbCaisbtTXSDT1Ow","https://ap.wps.com/l/cbCaisbtTXSDT1Ow","pdf",3592378,1,19,"English","en",105,"# Introduction\n## Mobility measurement challenges in low smartphone adoption contexts\n## Bias framework: selection and selective technology use\n# Methods and data\n## Uganda operator records and user identification\n# Results\n## Baseline mobility differences between data and non-data users\n## Mobility response differences after COVID-19 lockdown policies","[{\"question\":\"Why can smartphone mobility estimates be misleading in low-income countries?\",\"answer\":\"Because many people do not own smartphones or use mobile internet, smartphone movement may not track population movement. Smartphone users are also more likely to differ in wealth, location, and mobility patterns.\"},{\"question\":\"What two biases does the paper focus on when inferring mobility from digital data?\",\"answer\":\"Selection into ownership of the technology and selective use of the technology. These biases affect how smartphone-based measures relate to broader population mobility.\"},{\"question\":\"What does the Uganda analysis find about smartphone users versus basic/feature phone users?\",\"answer\":\"Smartphone (data) users show different mobility patterns, including more longer-distance travel at baseline. They also decrease mobility substantially more after COVID-19 lockdown policies, especially in highly affected counties.\"}]",1784503959,48,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"assessing-bias-in-smartphone-mobility-estimates-in-low-income-countries","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/assessing-bias-in-smartphone-mobility-estimates-in-low-income-countries/113395/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-20","2026-07-19",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why can smartphone mobility estimates be misleading in low-income countries?","Question",{"text":75,"@type":76},"Because many people do not own smartphones or use mobile internet, smartphone movement may not track population movement. Smartphone users are also more likely to differ in wealth, location, and mobility patterns.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What two biases does the paper focus on when inferring mobility from digital data?",{"text":80,"@type":76},"Selection into ownership of the technology and selective use of the technology. These biases affect how smartphone-based measures relate to broader population mobility.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the Uganda analysis find about smartphone users versus basic/feature phone users?",{"text":84,"@type":76},"Smartphone (data) users show different mobility patterns, including more longer-distance travel at baseline. 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