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New Approaches to Human Mobility: Using Mobile Phones for Demographic Research

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Demography

Abstract

This article explores new methods for gathering and analyzing spatially rich demographic data using mobile phones. It describes a pilot study (the Human Mobility Project) in which volunteers around the world were successfully recruited to share GPS and cellular tower information on their trajectories and respond to dynamic, location-based surveys using an open-source Android application. The pilot study illustrates the great potential of mobile phone methodology for moving spatial measures beyond residential census units and investigating a range of important social phenomena, including the heterogeneity of activity spaces, the dynamic nature of spatial segregation, and the contextual dependence of subjective well-being.

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Notes

  1. The difference is between selecting people and observing the space through which they move and selecting places and observing the people who move through them. This is a basic choice that must be confronted in studying any collection of moving objects, and it is often the case that the first approach (which mobile phones facilitate) is more illuminating but also harder to implement (Ōkubo and Levin 2001).

  2. See www.mappiness.org.uk.

  3. See www.trackyourhappiness.org.

  4. We use “race” throughout the article to refer to both racial and ethnic self-identification. Subjects could identify as “White,” “Black, African, African American,” “Spanish/Hispanic/Latino,” “Asian,” “American Indian or Alaska Native,” “Pacific Islander,” or “Other,” and we treat these as nonoverlapping categories for simplicity.

  5. We gave subjects the ability to select the interval.

  6. We discarded estimates with Android-reported accuracy measurements of more than 5 km and those that would have required subjects to have been traveling faster than 54 m per second.

  7. The centroid is calculated as the mean longitude and mean latitude for each subject. A small amount of random noise is added to each centroid to aid in visualization and protect subjects’ privacy, using the random perturbation masking technique described in Armstrong et al. (1999).

  8. We did this by applying what Armstrong et al. (1999) referred to as a “scale transformation mask.” We multiplied each subject’s matrix of location estimates by a randomly generated number. Although one might assume that it is sufficient to simply remove coordinates from the axes, the transformation is necessary because depending on how the graphs are generated, their files may contain underlying coordinate data that can be extracted. We used a multiplicative transformation, rather than an additive one, to make it impossible to reconstruct the original coordinates using scale information. Although this transformation does not preserve actual distances between points, it is possible to retain approximate map scale by measuring and transforming the actual distances between each subject’s maximum and minimum latitude and longitude coordinates.

  9. When there was more than one such block, we used the one that occurred most frequently in the data.

  10. In Figs. 5 and 6, location estimates within the home block are marked with squares.

  11. In Figs. 3, 4, 5 and 6, this point is identified by the large circle drawn around it.

  12. In addition to excluding responses from subjects whose home locations were not estimated (meaning all outside the United States), we also excluded one extreme outlier in terms of distance from home.

  13. These included variables for sex, race (black, white, and Latino were the only responses given in the data analyzed), distance from home, and the interaction between distance from home and sex. Additional models were tested using variables for time of day, weekend, working hours (9:00 a.m.–5:00 p.m.), and census block racial characteristics, but these variables did not have clear relationships to the survey responses or sufficiently improve model fit to warrant inclusion in the final analysis.

  14. These conclusions are based on our estimated coefficients on the variable interacting sex with distance from home.

  15. See footnotes 7 and 8 and accompanying text. For an overview of masking techniques, see Armstrong et al. (1999) and Gutmann et al. (2008).

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Acknowledgments

The authors thank Hazer Inaltekin, Spencer Lucian, and David Potere for their contributions to this project during its initial phases, and Matthew Salganik for his invaluable guidance and contributions throughout. The work was funded by a grant from the Center for Information Technology Policy at Princeton University. Institutional support was provided by National Institutes of Health Training Grant T32HD07163 and Infrastructure Grant R24HD047879.

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Authors and Affiliations

  1. Woodrow Wilson School of Public and International Affairs and Office of Population Research, Princeton University, 284 Wallace Hall, Princeton, NJ, 08544, USA

    John R. B. Palmer

  2. Office of Population Research and Department of Sociology, Princeton University, Princeton, NJ, USA

    Thomas J. Espenshade

  3. Center for Advanced Studies of Blanes, CEAB-CSIC, Blanes, Spain

    Frederic Bartumeus

  4. Office of Population Research, Princeton University, Princeton, NJ, USA

    Chang Y. Chung

  5. Syncsort, Woodcliff Lake, NJ, USA

    Necati Ercan Ozgencil

  6. Google, New York, NY, USA

    Kathleen Li

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Palmer, J.R.B., Espenshade, T.J., Bartumeus, F. et al. New Approaches to Human Mobility: Using Mobile Phones for Demographic Research. Demography 50, 1105–1128 (2013). https://doi.org/10.1007/s13524-012-0175-z

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