The ABCDE of Big Data: Assessing Biases in Call-Detail Records for Development Estimates

Pestre, Gabriel; Letouzé, Emmanuel; Zagheni, Emilio

doi:10.1093/wber/lhz039

Cited by 18 publications

(15 citation statements)

References 8 publications

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“…As a consequence of data availability issues, we need to consider how nontraditional data can be leveraged to complement existing sources in order to improve estimates and predictions of migration indicators over time. Previous work has explored the use of data such as call detail records (Blumenstock 2012;Pestre et al 2020), air traffic data (Gabrielli et al 2019), tax file records (Engels and Healy 1981) and other sources like billing addresses or school enrollment (Foulkes and Newbold 2008) to estimate migration. Additionally, an increasingly large body of work has investigated the use of social media data, from websites such as Twitter (Zagheni et al 2014), Facebook (Zagheni et al 2017) and LinkedIn (State et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States

Alexander

Polimis

Zagheni

2020

Popul Res Policy Rev

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Measuring and forecasting migration patterns has important implications for understanding broader population trends, for designing policy effectively and for allocating resources. However, data on migration and mobility are often lacking, and those that do exist are not available in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more traditional data sources. Facebook's Advertising Platform, for example, is a potentially rich data source of demographic information that is regularly updated. However, Facebook's users are not representative of the underlying population. This paper proposes a statistical framework to combine social media data with traditional survey data to produce timely 'nowcasts' of migrant stocks by state in the United States. The model incorporates bias adjustment of Facebook data, and a pooled principal component time series approach, to account for correlations across age, time and space. We use the model to estimate and project migrants from Mexico, India and Germany, three migrant groups with varying levels and trends of migration in the US. By comparing short-term projections with data from the American Community Survey, we show that the model predictions outperform alternatives that rely solely on either social media or survey data.

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Section: Introductionmentioning

confidence: 99%

Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States

Alexander

Polimis

Zagheni

2020

Popul Res Policy Rev

Self Cite

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“…Therefore, their use in research does not preclude those from low socioeconomic groups in the way that GPS data would, for instance. An individual’s home and work setting can be derived from CDR data, which is particularly valuable for countries where no integrated infrastructures exist for population census [ 74 ]. However, there are limitations that need to be taken into account in evaluating the use of CDRs in health research and in considering opportunities for their wider use.…”

Section: Discussionmentioning

confidence: 99%

“…Limitations inherent in the data (outlined earlier) need to be quantified and addressed for greater confidence in research findings. Encouragingly, there are reports that propose how bias in phone data can be addressed [ 74 ], but other issues remain. It would be useful to see a series of studies that have assessed the validity of CDRs in health research compared to other forms of geolocation data, for example, with Bluetooth data [ 92 ], photographic data [ 93 ], and flight data [ 94 ].…”

Section: Discussionmentioning

confidence: 99%

Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review

Jones¹,

Daniels²,

Heys³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundCall detail records (CDRs) are collected by mobile network operators in the course of providing their service. CDRs are increasingly being used in research along with other forms of big data and represent an emerging data type with potential for public good. Many jurisdictions have infrastructures for health data research that could benefit from the integration of CDRs with health data.ObjectiveThe objective of this study was to review how CDRs have been used in health research and to identify challenges and potential opportunities for their wider use in conjunction with health data.MethodsA literature review was conducted using structured search terms making use of major search engines. Initially, 4066 items were identified. Following screening, 46 full text articles were included in the qualitative synthesis. Information extracted included research topic area, population of study, datasets used, information governance and ethical considerations, study findings, and data limitations.ResultsThe majority of published studies were focused on low-income and middle-income countries. Making use of the location element in CDRs, studies often modeled the transmission of infectious diseases or estimated population movement following natural disasters with a view to implementing interventions. CDRs were used in anonymized or aggregated form, and the process of gaining regulatory approvals varied with data provider and by jurisdiction. None included public views on the use of CDRs in health research.ConclusionsDespite various challenges and limitations, anonymized mobile phone CDRs have been used successfully in health research. The use of aggregated data is a safeguard but also a further limitation. Greater opportunities could be gained if validated anonymized CDRs were integrated with routine health records at an individual level, provided that permissions and safeguards could be put in place. Further work is needed, including gaining public views, to develop an ethically founded framework for the use of CDRs in health research.

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“…Therefore, their use in research does not preclude those from low socio-economic groups in the way that GPS data would, for instance. This is also valuable for countries where there are no integrated infrastructures for population census [33]. However, there are limitations that need to be taken into account in evaluating the use of CDRs in human mobility research.…”

Section: Human Mobility Data Sourcesmentioning

confidence: 99%

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

2021

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The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

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The ABCDE of Big Data: Assessing Biases in Call-Detail Records for Development Estimates

Cited by 18 publications

References 8 publications

Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States

Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States

Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

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