Tracking employment shocks using mobile phone data

Toole, Jameson L.; Lin, Yu‐Ru; Muehlegger, Erich; Shoag, Daniel; González, Marta C.; Lazer, David

doi:10.1098/rsif.2015.0185

Cited by 80 publications

(58 citation statements)

References 65 publications

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“…Certainly more work is needed to understand the connection between demographics and predictability. For future research, we suggest considering demographic factors when trying to characterize human mobility, as it has been done, for example, by linking changes in mobility patterns with unemployment status [42].…”

Section: Discussionmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

Self Cite

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Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors -in terms of modeling approaches and spatio-temporal characteristics of the data sources -have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.

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Section: Discussionmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

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“…For example, a recent study highlights the potential of mobile phone data to improve forecasts of critical economic indicators such as unemployment rates up to 4 months before the release of official reports, and more accurately than using historical data alone. 15 Our aim will be to compute home/work commuting figures between regions in the country. This objective has been considered one of the simplest involving mobility and has been chosen to start with.…”

Section: Methodological Foundationsmentioning

confidence: 99%

An Exercise in Exploring Big Data for Producing Reliable Statistical Information

Rey-del-Castillo

Cardeñosa

2016

Big Data

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The availability of copious data about many human, social, and economic phenomena is considered an opportunity for the production of official statistics. National statistical organizations and other institutions are more and more involved in new projects for developing what is sometimes seen as a possible change of paradigm in the way statistical figures are produced. Nevertheless, there are hardly any systems in production using Big Data sources. Arguments of confidentiality, data ownership, representativeness, and others make it a difficult task to get results in the short term. Using Call Detail Records from Ivory Coast as an illustration, this article shows some of the issues that must be dealt with when producing statistical indicators from Big Data sources. A proposal of a graphical method to evaluate one specific aspect of the quality of the computed figures is also presented, demonstrating that the visual insight provided improves the results obtained using other traditional procedures.

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“…Research from David Lazer and his colleagues has shown how mobile phones can lead to better predictions of unemployment rates (7). On the surface, the two don't seem to have anything in common.…”

Section: Mining the Social Networkmentioning

confidence: 99%

Computational social science

Mann¹

2016

Proc. Natl. Acad. Sci. U.S.A.

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Cell phone tower data predicts which parts of London can expect a spike in crime (1). Google searches for polling place information on the day of an election reveal the consequences of different voter registration laws (2). Mathematical models explain how interactions among financial investors produce better yields, and even how they generate economic bubbles (3).These are just a few examples of how a suite of technologies is helping bring sociology, political science, and economics into the digital age. Such social science fields have historically relied on interviews and survey data, as well as censuses and other government databases, to answer important questions about human behavior. These tools often produce results based on individuals-showing, for example, that a wealthy, well-educated, white person is statistically more likely to vote (4)-but struggle to deal with complex situations involving the interactions of many different people.A growing field called "computational social science" is now using digital tools to analyze the rich and interactive lives we lead. The discipline uses powerful computer simulations of networks, data collected from cell phones and online social networks, and online experiments involving hundreds of thousands of individuals to answer questions that were previously impossible to investigate. Humans are fundamentally social creatures and these new tools and huge datasets are giving social scientists insights into exactly how connections among people create societal trends or heretofore undetected patterns, related to everything from crime to economic fortunes to political persuasions. Although the field provides powerful ways to study the world, it's an ongoing challenge to ensure Using cell-phone and taxi GPS data, researchers classified people in San Francisco into "tribal networks," clustering them according to their behavioral patterns. Student's, tourists, and businesspeople all travel through the city in various ways, congregating and socializing in different neighborhoods. Image courtesy of Alex Pentland (Massachusetts Institute of Technology, Cambridge, MA).

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Tracking employment shocks using mobile phone data

Cited by 80 publications

References 65 publications

Understanding predictability and exploration in human mobility

Understanding predictability and exploration in human mobility

An Exercise in Exploring Big Data for Producing Reliable Statistical Information

Computational social science

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