Mobile Phone Data for Children on the Move: Challenges and Opportunities

Sekara, Vedran; Omodei, Elisa; Healy, Laura; Beise, Jan; Hansen, Claus D.; You, Danzhen; Blume, Saskia; García-Herranz, Manuel

doi:10.1007/978-3-030-12554-7_3

Cited by 7 publications

(4 citation statements)

References 57 publications

(57 reference statements)

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“…In that region, men are more likely to be mobile phone owners, phone sharing is common among rural women, and many individuals use multiple SIM cards due to non-overlapping provider coverage. Similar biases have shown to be a key challenge in tracking children in Turkey [84]. Other issues related to the representativeness of mobile phone data are investigated in [81] by Arai et al.…”

Section: Technical Challengesmentioning

confidence: 95%

“…Mobile phone data are known to be biased: users are unevenly distributed by demography, geography, and socio-economic groups [81,82,83]. For example, Sekara et al [84] highlighted that in Turkey, in 2018, the mobile phone subscribers penetration was the 65% showing that the 35% of Turkish does not have a SIM registered to their name. In other terms, we have individuals with multiple SIMs and others with no SIM.…”

Section: Technical Challengesmentioning

confidence: 99%

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Leveraging Mobile Phone Data for Migration Flows

Luca¹,

Barlacchi²,

Oliver³

et al. 2021

Preprint

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Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap -up to a few years -between the data collection process and the computation and publication of relevant statistics. This gap is a significant drawback for the analysis of a phenomenon that is continuously and rapidly changing. Alternative data sources, such as surveys and field observations, also suffer from reliability, costs, and scale limitations. The ubiquity of mobile phones enables an accurate and efficient collection of up-to-date data related to migration. Indeed, passively collected data by the mobile network infrastructure via aggregated, pseudonymized Call Detail Records (CDRs) is of great value to understand human migrations. Through the analysis of mobile phone data, we can shed light on the mobility patterns of migrants, detect spontaneous settlements and understand the daily habits, levels of integration, and human connections of such vulnerable social groups. This Chapter discusses the importance of leveraging mobile phone data as an alternative data source to gather precious and previously unavailable insights on various aspects of migration. Also, we highlight pending challenges that would need to be addressed before we can effectively benefit from the availability of mobile phone data to help make better decisions that would ultimately improve millions of people's lives.

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Section: Technical Challengesmentioning

confidence: 95%

Section: Technical Challengesmentioning

confidence: 99%

Leveraging Mobile Phone Data for Migration Flows

Luca¹,

Barlacchi²,

Oliver³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, with knowledge of the degree to which a local job network will be able to promote or constrain diffusion and the nodes which are most influential in this network, policy-makers can more effectively drive the labour market to a desired state. For example, the adoption of companywide policies protective of children and young people has been found to preferentially occur along network connections defined by supply chains [25].…”

Section: Introductionmentioning

confidence: 99%

The dynamic resilience of urban labour networks

Feng

Rutherford

2023

R. Soc. open sci.

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Both cities and markets are well understood as complex systems which are amenable to analysis using physically inspired methods. Cities have shown fascinating universality with size, while labour markets modelled as networks have considerable explanatory power. Labour markets are a particularly attractive domain of study in this context due to societal importance, the influx of high-resolution data as well as exogenous influence of automation. While much previous work has studied the economic characteristics of cities as a function of size and examined the exposure of urban economies to automation, this has often been from a static perspective. In this work, we examine the diffusive properties of labour markets and examine their variance across cities. More specifically, we identify the occupations which are most important in promoting the diffusion of beneficial or deleterious properties. To this end, we propose a new measure of node centrality empSI. We find that these properties of influence vary considerably with city size.

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“…It is widely recognized that access to, and usage of, big-data technologies is heterogeneous across populations [22,23,24]. Differences in technology usage can lead to disparities in how, or whether, individuals are captured in digitally collected datasets [25]. For instance, it has been established that in certain countries mobile phone ownership is biased towards predominantly wealthier, better educated, and for the most part male populations [26,27], meaning that mobility datasets captured in this context will mainly contain the travels of these demographics.…”

Section: Introductionmentioning

confidence: 99%

Biases in human mobility data impact epidemic modeling

Schlosser¹,

Sekara²,

Brockmann³

et al. 2021

Preprint

Self Cite

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Large-scale human mobility data is a key resource in data-driven policy making and across many scientific fields. Most recently, mobility data was extensively used during the COVID-19 pandemic to study the effects of governmental policies and to inform epidemic models. Large-scale mobility is often measured using digital tools such as mobile phones. However, it remains an open question how truthfully these digital proxies represent the actual travel behavior of the general population. Here, we examine mobility datasets from multiple countries and identify two fundamentally different types of bias caused by unequal access to, and unequal usage of mobile phones. We introduce the concept of data generation bias, a previously overlooked type of bias, which is present when the amount of data that an individual produces influences their representation in the dataset. We find evidence for data generation bias in all examined datasets in that high-wealth individuals are overrepresented, with the richest 20% contributing over 50% of all recorded trips, substantially skewing the datasets. This inequality is consequential, as we find mobility patterns of different wealth groups to be structurally different, where the mobility networks of high-wealth users are denser and contain more long-range connections. To mitigate the skew, we present a framework to debias data and show how simple techniques can be used to increase representativeness. Using our approach we show how biases can severely impact outcomes of dynamic processes such as epidemic simulations, where biased data incorrectly estimates the severity and speed of disease transmission. Overall, we show that a failure to account for biases can have detrimental effects on the results of studies and urge researchers and practitioners to account for data-fairness in all future studies of human mobility.

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Mobile Phone Data for Children on the Move: Challenges and Opportunities

Cited by 7 publications

References 57 publications

Leveraging Mobile Phone Data for Migration Flows

Leveraging Mobile Phone Data for Migration Flows

The dynamic resilience of urban labour networks

Biases in human mobility data impact epidemic modeling

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