Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling

Nature Computational Science volume 4, pages 398–411 (2024)Cite this article

Subjects

Abstract

Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges—some related to accessing and processing these data, and some related to data quality—and propose several research directions to address them moving forward.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Broad overview of the data-processing pipeline.
Fig. 2: Fraction of the population in Philadelphia staying at home all day for 2019–2020 (7-day rolling average).
Fig. 3: Sources of error in the estimation of metrics from GPS location data.
Fig. 4: Ratio of DAU to MAU for five samples of GPS human-mobility data.
Fig. 5: Effect of stop-detection algorithm parameters on a synthetic trajectory.

Similar content being viewed by others

Data availability

Mobile phone mobility data from SafeGraph depicted in Fig. 2 were made available to the research community through SafeGraph’s COVID-19 Data Consortium (https://www.safegraph.com/covid-19-data-consortium). The data for Fig. 4 can be accessed from the following sources: SafeGraph, https://www.eff.org/deeplinks/2021/08/illinois-bought-invasive-phone-location-data-banned-broker-safegraph; X-mode57; Kochava, https://www.ftc.gov/news-events/news/press-releases/2022/08/ftc-sues-kochava-selling-data-tracks-people-reproductive-health-clinics-places-worship-other; Factori, https://datarade.ai/data-providers/lifesight/profile; Quadrant, https://www.quadrant.io/solutions/data-quality-dashboard. We have archived the websites referenced in this paper for future reference (Supplementary Table 1).

Code availability

Code for the evaluation of stop-detection on synthetic trajectories as shown in Fig. 5, as well as the visualization in Fig. 2, is available at https://github.com/Watts-Lab/stop-detection-validation.

References

  1. Colizza, V., Barrat, A., Barthélemy, M. & Vespignani, A. The role of the airline transportation network in the prediction and predictability of global epidemics. Proc. Natl Acad. Sci. USA 103, 2015–2020 (2006).

    Article  Google Scholar 

  2. Balcan, D. et al. Modeling the spatial spread of infectious diseases: the global epidemic and mobility computational model. J. Comput. Sci. 1, 132–145 (2010).

    Article  Google Scholar 

  3. Merler, S., Ajelli, M., Pugliese, A. & Ferguson, N. M. Determinants of the spatiotemporal dynamics of the 2009 H1N1 pandemic in Europe: implications for real-time modelling. PLoS Comput. Biol. 7, e1002205 (2011).

    Article  Google Scholar 

  4. Chinazzi, M. et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368, 395–400 (2020).

    Article  Google Scholar 

  5. Eubank, S. et al. Modelling disease outbreaks in realistic urban social networks. Nature 429, 180–184 (2004).

    Article  Google Scholar 

  6. Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925–973 (2015).

    Article  MathSciNet  Google Scholar 

  7. Zhang, Q. et al. Spread of Zika virus in the Americas. Proc. Natl Acad. Sci. USA 114, E4334–e4343 (2017).

    Google Scholar 

  8. Rockett, R. J. et al. Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling. Nat. Med. 26, 1398–1404 (2020).

    Article  Google Scholar 

  9. Shamil, M. S., Farheen, F., Ibtehaz, N., Khan, I. M. & Rahman, M. S. An agent-based modeling of COVID-19: validation, analysis, and recommendations. Cognit. Comput. 13, 1–12 (2021).

    Google Scholar 

  10. Kishore, N. et al. Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis. Lancet Digit. Health 2, e622–e628 (2020).

    Google Scholar 

  11. Pappalardo, L., Manley, E., Sekara, V. & Alessandretti, L. Future directions in human mobility science. Nat. Comput. Sci. 3, 588–600 (2023).

    Article  Google Scholar 

  12. Chafetz, H., Zahuranec, A. J., Marcucci, S., Davletov, B. & Verhulst, S. The #Data4COVID19 Review: assessing the use of non-traditional data during a pandemic crisis. SSRN https://doi.org/10.2139/ssrn.4273229 (2022).

    Article  Google Scholar 

  13. Jia, J. S. et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 582, 389–394 (2020).

    Article  Google Scholar 

  14. Kogan, N. E. et al. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Sci. Adv. 7, eabd6989 (2021).

    Article  Google Scholar 

  15. Chang, S. et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589, 82–87 (2021).

    Article  Google Scholar 

  16. Aleta, A. et al. Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas. Proc. Natl Acad. Sci. USA 119, e2112182119 (2022).

    Article  Google Scholar 

  17. Moro, E., Calacci, D., Dong, X. & Pentland, A. Mobility patterns are associated with experienced income segregation in large US cities. Nat. Commun. 12, 4633 (2021).

    Article  Google Scholar 

  18. Allcott, H. et al. Polarization and public health: partisan differences in social distancing during the coronavirus pandemic. J. Public Econ. 191, 104254 (2020).

    Article  Google Scholar 

  19. Painter, M. & Qiu, T. Political beliefs affect compliance with government mandates. J. Econ. Behav. Organ. 185, 688–701 (2021).

    Article  Google Scholar 

  20. Weill, J. A., Stigler, M., Deschenes, O. & Springborn, M. R. Social distancing responses to COVID-19 emergency declarations strongly differentiated by income. Proc. Natl Acad. Sci. USA 117, 19658–19660 (2020).

    Article  Google Scholar 

  21. Roberts, D. C. & Utych, S. M. Polarized social distancing: residents of republican-majority counties spend more time away from home during the COVID-19 crisis. Soc. Sci. Q. 102, 2516–2527 (2021).

    Article  Google Scholar 

  22. Iio, K., Guo, X., Kong, X. & Rees, K. & Wang, XiubinBruce. COVID-19 and social distancing: disparities in mobility adaptation between income groups. Transp. Res. Interdiscip. Perspect. 10, 100333 (2021).

    Google Scholar 

  23. Pei, S., Kandula, S. & Shaman, J. Differential effects of intervention timing on COVID-19 spread in the United States. Sci. Adv. 6, eabd6370 (2020).

    Article  Google Scholar 

  24. Yan, Y. et al. Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic. Proc. Natl Acad. Sci. USA 118, e2008814118 (2021).

    Article  Google Scholar 

  25. Lai, S. et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature 585, 410–413 (2020).

    Article  Google Scholar 

  26. Kishore, N. Mobility data as a proxy for epidemic measures. Nat. Comput. Sci. 1, 567–568 (2021).

    Article  Google Scholar 

  27. Grantz, K. H. et al. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat. Commun. 11, 4961 (2020).

    Article  Google Scholar 

  28. Zheng, Y. et al. GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33, 32–39 (2010).

    Google Scholar 

  29. Hariharan, R. & Toyama, K. Project Lachesis: parsing and modeling location histories. In International Conference on Geographic Information Science (eds M.J. Egenhofer, C. Freksa & H.J. Miller) 106–124 (Springer, 2004).

  30. Torous, J. et al. Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps. Curr. Psychiatry Rep. 20(7), 51 (2018).

    Article  Google Scholar 

  31. Barnett, I. & Onnela, Jukka-Pekka Inferring mobility measures from GPS traces with missing data. Biostatistics 21, e98–e112 (2020).

    Article  Google Scholar 

  32. Wesolowski, A. et al. Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Sci. Rep. 4, 5678 (2014).

    Article  Google Scholar 

  33. Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W. & Buckee, C. O. The impact of biases in mobile phone ownership on estimates of human mobility. J. R. Soc. Interface 10, 20120986 (2013).

    Article  Google Scholar 

  34. Schlosser, F., Sekara, V., Brockmann, D. & Garcia-Herranz, M. Biases in human mobility data impact epidemic modeling. Preprint at https://arxiv.org/abs/2112.12521 (2021).

  35. Pappalardo, L., Ferres, L., Sacasa, M., Cattuto, C. & Bravo, L. Evaluation of home detection algorithms on mobile phone data using individual-level ground truth. EPJ Data Sci. 10, 29 (2021).

    Article  Google Scholar 

  36. Malik, M. M., Lamba, H., Nakos, C. & Pfeffer, J. Population bias in geotagged tweets. In 9th International AAAI Conference on Web Logs and Social Media Workshop - Technical Report, WS-15-18, 18–27 (AAAI, 2015).

  37. Location Intelligence Market Size, Share & Trends Analysis Report by Application (Sales & Marketing Optimization, Remote Monitoring), by Service (Consulting, System Integration), by Vertical, and Segment Forecasts, 2023–2030 (Grand View Research, 2023).

  38. Keegan, J. & Ng, A. There’s a multibillion-dollar market for your phone’s location data. The Markup (30 September 2021); https://themarkup.org/privacy/2021/09/30/theres-a-multibillion-dollar-market-for-your-phones-location-data

  39. Gonzalez, M. C., Hidalgo, C. A. & Barabasi, A.-L. Understanding individual human mobility patterns. Nature 453, 779–782 (2008).

    Article  Google Scholar 

  40. Pepe, E. et al. COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Sci. Data 7, 230 (2020).

    Article  Google Scholar 

  41. Gauvin, L. et al. Gender gaps in urban mobility. Humanit. Soc. Sci. Commun. 7, 11 (2020).

    Article  Google Scholar 

  42. Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. ászló Limits of predictability in human mobility. Science 327, 1018–1021 (2010).

    Article  MathSciNet  Google Scholar 

  43. Alessandretti, L., Sapiezynski, P., Sekara, V., Lehmann, S. & Baronchelli, A. Evidence for a conserved quantity in human mobility. Nat. Hum. Behav. 2, 485–491 (2018).

    Article  Google Scholar 

  44. Gao, S., Rao, J., Kang, Y., Liang, Y. & Kruse, J. Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSpatial Spec. 12, 16–26 (2020).

    Article  Google Scholar 

  45. Hill, T. D., Gonzalez, K. & Burdette, A. M. The blood of Christ compels them: state religiosity and state population mobility during the coronavirus (COVID-19) pandemic. J. Relig. Health 59, 2229–2242 (2020).

    Article  Google Scholar 

  46. Chen, Y., Jiao, J., Bai, S. & Lindquist, J. Modeling the spatial factors of COVID-19 in New York City. SSRN https://doi.org/10.2139/ssrn.3606719 (2020).

    Article  Google Scholar 

  47. Adjodah, D. et al. Association between COVID-19 outcomes and mask mandates, adherence, and attitudes. PLoS ONE 16, e0252315 (2021).

    Article  Google Scholar 

  48. Yuan, Y., Jahani, E., Zhao, S., Ahn, Y.-Y. & Pentland, A. Implications of COVID-19 vaccination heterogeneity in mobility networks. Commun. Phys. 6, 206 (2023).

    Article  Google Scholar 

  49. Kang, Y. et al. Multiscale dynamic human mobility flow dataset in the US during the COVID-19 epidemic. Sci. Data 7, 390 (2020).

    Article  Google Scholar 

  50. Barreras, F., Hayhoe, M., Hassani, H. & Preciado, V. M. AutoEKF: scalable system identification for COVID-19 forecasting from large-scale GPS data. Preprint at https://arxiv.org/abs/2106.14357 (2021).

  51. Verma, R., Yabe, T. & Ukkusuri, S. V. Spatiotemporal contact density explains the disparity of COVID-19 spread in urban neighborhoods. Sci. Rep. 11, 10952 (2021).

    Article  Google Scholar 

  52. Levin, R., Chao, D. L., Wenger, E. A. & Proctor, J. L. Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning. Nat. Comput. Sci. 1, 588–597 (2021).

    Article  Google Scholar 

  53. Lasry, A. et al. Timing of community mitigation and changes in reported COVID-19 and community mobility—four US metropolitan areas, February 26–April 1, 2020. Morb. Mort. Wkly Rep. 69, 451–457 (2020).

    Article  Google Scholar 

  54. Jay, J. et al. Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States. Nat. Hum. Behav. 4, 1294–1302 (2020).

    Article  Google Scholar 

  55. Ross, S., Breckenridge, G., Zhuang, M. & Manley, E. Household visitation during the COVID-19 pandemic. Sci. Rep. 11, 12 (2021).

    Article  Google Scholar 

  56. Couture, V., Dingel, J. I., Green, A., Handbury, J. & Williams, K. R. JUE Insight: measuring movement and social contact with smartphone data: a real-time application to COVID-19. J. Urban Econ. 127, 103328 (2021).

    Article  Google Scholar 

  57. Crawford, F. W. et al. Impact of close interpersonal contact on COVID-19 incidence: evidence from 1 year of mobile device data. Sci. Adv. 8, eabi5499 (2022).

    Article  Google Scholar 

  58. Klein, B. et al. Assessing Changes in Commuting and Individual Mobility in Major Metropolitan Areas in the United States during the COVID-19 Outbreak (Northeastern University Network Science Institute, 2020).

  59. Klein, B. et al. Reshaping a Nation: Mobility, Commuting, and Contact Patterns during the COVID-19 Outbreak (Northeastern University-Network Science Institute, 2020).

  60. Malik, M. M. Bias and Beyond in Digital Trace Data. PhD thesis, Carnegie Mellon Univ., Pittsburgh (2018).

  61. Malik, M. & Pfeffer, J. Identifying platform effects in social media data. In Proc. International AAAI Conference on Web and Social Media Vol. 10, 241–249 (AAAI Press, 2016).

  62. Mahrt, M. & Scharkow, M. The value of big data in digital media research. J. Broadcast. Electron. Media 57, 20–33 (2013).

    Article  Google Scholar 

  63. Sarpanah, Z., Mosavi, M. R. & Shafiee, E. GPS receivers spoofing detection based on subtractive, FCM and DBScan clustering algorithms. J. Circuits Syst. Comput. 32, 2350152 (2023).

    Article  Google Scholar 

  64. Zhao, B. & Sui, D. Z. True lies in geospatial big data: detecting location spoofing in social media. Ann. GIS 23, 1–14 (2017).

    Article  Google Scholar 

  65. Zheng, Y. Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6, 29 (2015).

    Article  Google Scholar 

  66. Gelman, A. & Loken, E. The Garden of Forking Paths: Why Multiple Comparisons can be a Problem, Even When There is no ‘Fishing Expedition’ or ‘P-hacking’ and the Research Hypothesis was Posited Ahead of Time (Department of Statistics, Columbia Univ. 2013).

  67. Ester, M. et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996, 226–231 (AAAI, 1996).

  68. Pappalardo, L., Simini, F., Barlacchi, G. & Pellungrini, R. scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data. Preprint at https://arxiv.org/abs/1907.07062 (2019).

  69. Martin, H., Hong, Y., Wiedemann, N., Bucher, D. & Raubal, M. trackintel: an open-source Python library for human mobility analysis. Comput. Environ. Urban Syst. 101, 101938 (2023).

    Article  Google Scholar 

  70. Vanhoof, M., Reis, F., Ploetz, T. & Smoreda, Z. Assessing the quality of home detection from mobile phone data for official statistics. J. Off. Stat. 34, 935–960 (2018).

    Article  Google Scholar 

  71. Aslak, U. & Alessandretti, L. Infostop: scalable stop-location detection in multi-user mobility data. Preprint at https://arxiv.org/abs/2003.14370 (2020).

  72. Lucchini, L. et al. Living in a pandemic: changes in mobility routines, social activity and adherence to COVID-19 protective measures. Sci. Rep. 11, 24452 (2021).

    Article  Google Scholar 

  73. Alessandretti, L., Aslak, U. & Lehmann, S. The scales of human mobility. Nature 587, 402–407 (2020).

    Article  Google Scholar 

  74. Horn, A. L. et al. Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations. npj Digit. Med. 6, 208 (2023).

    Article  Google Scholar 

  75. Yang, Y., Pentland, A. & Moro, E. Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics. EPJ Data Sci. 12, 15 (2023).

    Article  Google Scholar 

  76. Noi, E., Rudolph, A. & Dodge, S. Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework. Int. J. Geogr. Inf. Sci. 36, 585–616 (2022).

    Article  Google Scholar 

  77. Berman, G. et al. Ethical Considerations When Using Geospatial Technologies for Evidence Generation Unicef Official Research-Innocenti Discussion Papers, DP-2018-02 (Unicef, 2018); https://doi.org/10.18356/60c0e27b-en

  78. Cuttone, A., Lehmann, S. & González, M. C. Understanding predictability and exploration in human mobility. EPJ Data Sci. 7, 1–17 (2018).

    Article  Google Scholar 

  79. Zang, H. & Bolot, J. Anonymization of location data does not work: a large-scale measurement study. In Proc. 17th Annual International Conference on Mobile Computing and Networking 145–156 (2011).

  80. de Montjoye, YA., Hidalgo, C., Verleysen, M. et al. Unique in the Crowd: the privacy bounds of human mobility. Sci. Rep. 3, 1376 (2013).

    Article  Google Scholar 

  81. Xu, F. et al. Trajectory recovery from ash: User privacy is not preserved in aggregated mobility data. In Proc. 26th International Conference on World Wide Web 1241–1250, (2017).

  82. NAI’s Enhanced Standards For Precise Location Information Demonstrate Industry Leadership (Network Advertising Initiative, 2021); https://thenai.org/nais-enhanced-standards-for-precise-location-information-demonstrate-industry-leadership/

  83. Zhang, M. et al. Human mobility and COVID-19 transmission: a systematic review and future directions. Ann. GIS 28, 1–14 (2022).

    Article  Google Scholar 

  84. Hu, T. et al. Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges. Int. J. Digit. Earth 14, 1126–1147 (2021).

    Article  Google Scholar 

  85. Gruteser, M. & Grunwald, D. Anonymous usage of location-based services through spatial and temporal cloaking. In Proc. 1st International Conference on Mobile Systems, Applications and Services 31–42 (2003).

  86. Pestre, G., Letouzé, E. & Zagheni, E. The ABCDE of big data: assessing biases in call-detail records for development estimates. World Bank Econ. Rev. 34, S89–S97 (2020).

    Article  Google Scholar 

  87. Fiore, M. et al. Privacy in trajectory micro-data publishing: a survey. Trans. Data Priv. 13, 91–149 (2020).

    Google Scholar 

  88. Rossi, L., Walker, J. & Musolesi, M. Spatio-temporal techniques for user identification by means of GPS mobility data. EPJ Data Sci. 4(1), 1–16 (2015).

    Article  Google Scholar 

  89. Dwork, C. et al. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9, 211–407 (2014).

    Article  MathSciNet  Google Scholar 

  90. Dwork, C., McSherry, F., Nissim, K. & Smith, A. in Theory of Cryptography, TCC 2006 Lecture Notes in Computer Science Vol. 3876 (eds Halevi, S. & Rabin, T.) 265–284 (Springer, 2006).

  91. Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J. & Vilhuber, L. Privacy: theory meets practice on the map. In 2008 IEEE 24th International Conference on Data Engineering 277–286 (IEEE, 2008).

  92. Savi, M. K. et al. A standardised differential privacy framework for epidemiological modelling with mobile phone data. PLOS Digital Health 2 (10), e0000233 (2023).

    Article  Google Scholar 

  93. Chatzikokolakis, K., Palamidessi, C. & Stronati, M. in Privacy Enhancing Technologies, PETS 2014 Lecture Notes in Computer Science Vol. 8555 (eds De Cristofaro, E. & Murdoch, S.J.) 21–41 (Springer, 2014).

  94. Vietri, G. Generating Differentially Private Synthetic Data. PhD thesis, Univ. Minnesota (2023).

  95. Torkzadehmahani, R., Kairouz, P. & Paten, B. DP-CGAN: differentially private synthetic data and label generation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 98–104 (IEEE, 2019).

  96. Tamura, N., Urano, K., Aoki, S., Yonezawa, T. & Kawaguchi, N. Synthetic people flow: privacy-preserving mobility modeling from large-scale location data in urban areas. In International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services 553–567 (Springer, 2021).

  97. Kim, JongWook & Jang, B. Deep learning-based privacy-preserving framework for synthetic trajectory generation. J. Netw. Comput. Appl. 206, 103459 (2022).

    Article  Google Scholar 

  98. Pang, Y., Tsubouchi, K., Yabe, T. & Sekimoto, Y. Replicating urban dynamics by generating human-like agents from smartphone GPS data. In Proc. 26th ACM SIGSpatial International Conference on Advances in Geographic Information Systems 440–443 (2018).

  99. Graham, M., Milne, R., Fitzsimmons, P. & Sheehan, M. Trust and the Goldacre review: why trusted research environments are not about trust. J. Med. Ethics 49, 670–673 (2023).

    Article  Google Scholar 

  100. Kavianpour, S., Sutherland, J., Mansouri-Benssassi, E., Coull, N. & Jefferson, E. Next-generation capabilities in trusted research environments: interview study. J. Med. Internet Res. 24, e33720 (2022).

    Article  Google Scholar 

  101. Williamson, E. J. et al. Factors associated with COVID-19-related death using opensafely. Nature 584, 430–436 (2020).

    Article  Google Scholar 

  102. Whitepaper on Differential Privacy (Spectus, 2022).

  103. Budd, J. et al. Digital technologies in the public-health response to COVID-19. Nat. Med. 26, 1183–1192 (2020).

    Article  Google Scholar 

  104. Munzert, S., Selb, P., Gohdes, A., Stoetzer, L. F. & Lowe, W. Tracking and promoting the usage of a COVID-19 contact tracing app. Nat. Hum. Behav. 5, 247–255 (2021).

    Article  Google Scholar 

  105. Nahmias-Biran, Bat-hen et al. Enriching activity-based models using smartphone-based travel surveys. Transp. Res. Rec. 2672, 280–291 (2018).

    Article  Google Scholar 

  106. Baldauf, M. & Tomitsch, M. Pervasive displays for public transport: an overview of ubiquitous interactive passenger services. In Proc. 9TH ACM International Symposium on Pervasive Displays 37–45 (2020).

  107. Independent Evaluation Group et al. Data for Development: An Evaluation of World Bank Support for Data and Statistical Capacity (World Bank, 2017).

  108. Warren, M. S. & Skillman, S. W. Mobility changes in response to COVID-19. Preprint at https://arxiv.org/abs/2003.14228 (2020).

  109. Santana, C. et al. COVID-19 is linked to changes in the time–space dimension of human mobility. Nat. Hum. Behav. 7, 1729–1739 (2023).

    Article  Google Scholar 

  110. ArcGIS Pro (Esri, 2023); https://pro.arcgis.com/en/pro-app/index-geonet-allcontent.html

  111. Tracktable: Trajectory Analysis and Rendering (National Technology and Engineering Solutions of Sandia, LLC, 2023); https://tracktable.readthedocs.io/en/latest/index.html

  112. Calenge, C., Dray, S. & Royer, M. adehabitatLT: Analysis of Animal Movements. https://cran.r-project.org/web/packages/adehabitatLT/index.html (2023).

  113. Monteiro, D. TrajDataMining: Trajectories Data Mining (CRAN, 2018); https://cran.r-project.org/web/packages/TrajDataMining/index.html

  114. De Montjoye, Yves-Alexandre & Rocher, L. & Pentland, AlexSandy bandicoot: a Python toolbox for mobile phone metadata. J. Mach. Learn. Res. 17, 6100–6104 (2016).

    Google Scholar 

  115. Ubaldi, E. et al. Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics. Journal of Open Source Software 9(95), 5201 (2024).

    Article  Google Scholar 

  116. Luo, T., Zheng, X., Xu, G., Fu, K. & Ren, W. An improved DBScan algorithm to detect stops in individual trajectories. ISPRS Int. J. Geoinf. 6, 63 (2017).

    Article  Google Scholar 

  117. Huang, X. et al. Time-series clustering for home dwell time during COVID-19: what can we learn from it? ISPRS Int. J. Geoinf. 9, 675 (2020).

    Article  Google Scholar 

  118. Woody, S. et al. Projections for first-wave COVID-19 deaths across the US using social-distancing measures derived from mobile phones. Preprint at https://doi.org/10.1101/2020.04.16.20068163 (2020).

  119. Kashem, S., Baker, D. M., González, S. R. & Lee, C. A. Exploring the nexus between social vulnerability, built environment, and the prevalence of COVID-19: a case study of Chicago. Sustain. Cities Soc. 75, 103261 (2021).

    Article  Google Scholar 

  120. Banerjee, T. & Nayak, A. US county level analysis to determine if social distancing slowed the spread of COVID-19. Rev. Panam. Salud Publica 44, e90 (2020).

    Article  Google Scholar 

  121. Andersen, M. Early evidence on social distancing in response to COVID-19 in the United States. SSRN https://doi.org/10.2139/ssrn.3569368 (2020).

  122. Savaris, R. F., Pumi, G., Dalzochio, J. & Kunst, R. Retracted article: Stay-at-home policy is a case of exception fallacy: an internet-based ecological study. Sci. Rep. 11, 5313 (2021).

    Article  Google Scholar 

  123. Panigutti, C., Tizzoni, M., Bajardi, P., Smoreda, Z. & Colizza, V. Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models. R. Soc. Open Sci. 4, 160950 (2017).

    Article  MathSciNet  Google Scholar 

  124. Tongsinoot, L. & Muangsin, V. Exploring home and work locations in a city from mobile phone data. In 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS) 123–129 (IEEE, 2017).

  125. Mamei, M., Bicocchi, N., Lippi, M., Mariani, S. & Zambonelli, F. Evaluating origin–destination matrices obtained from CDR data. Sensors 19, 4470 (2019).

    Article  Google Scholar 

  126. Li, L., Goodchild, M. F. & Xu, B. Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartogr. Geogr. Inf. Sci. 40, 61–77 (2013).

    Article  Google Scholar 

  127. Jiang, S. et al. A review of urban computing for mobile phone traces: current methods, challenges and opportunities. In Proc. 2nd ACM SIGKDD International Workshop on Urban Computing 1–9 (2013).

  128. Engebretsen, S. et al. Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh. J. R. Soc. Interface 17, 20190809 (2020).

    Article  Google Scholar 

  129. Eagle, N. & Pentland, AlexSandy Eigenbehaviors: identifying structure in routine. Behav. Ecol. Sociobiol. 63, 1057–1066 (2009).

    Article  Google Scholar 

  130. Budak, C. & Watts, D. J. Dissecting the spirit of Gezi: influence vs. selection in the Occupy Gezi movement. Sociol. Sci. 2, 370–397 (2015).

    Article  Google Scholar 

  131. Chen, G., Viana, Aline Carneiro., Fiore, M. & Sarraute, C. Complete trajectory reconstruction from sparse mobile phone data. EPJ Data Sci. 8(1), 1–24 (2019).

    Article  Google Scholar 

  132. Liao, Y., Ek, K., Wennerberg, E., Yeh, S. & Gil, J. A mobility model for synthetic travel demand from sparse traces. IEEE Open J. Intell. Transp. Syst. 3, 665–678 (2022).

    Article  Google Scholar 

  133. Särndal, C.-E. & Lundström, S. Estimation in Surveys with Nonresponse (John Wiley & Sons, 2005).

  134. Mobile Fact Sheet Technical Report (Pew Research Center, 2021).

  135. Schlosser, F. et al. COVID-19 lockdown induces disease-mitigating structural changes in mobility networks. Proc. Natl Acad. Sci. USA 117, 32883–32890 (2020).

    Article  Google Scholar 

  136. Wesolowski, A., Eagle, N., Noor, A. M., Snow, R. W. & Buckee, C. O. Heterogeneous mobile phone ownership and usage patterns in Kenya. PLoS ONE 7, e35319 (2012).

    Article  Google Scholar 

  137. Blumenstock, J. & Eagle, N. Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda. In Proc. 4th ACM/IEEE International Conference on Information and Communication Technologies and Development 1–10 (2010).

  138. Li, Z., Ning, H., Jing, F. & Lessani, M. N. Understanding the bias of mobile location data across spatial scales and over time: a comprehensive analysis of SafeGraph data in the United States. SSRN https://doi.org/10.2139/ssrn.4383333 (2023).

  139. Nande, A. et al. The effect of eviction moratoria on the transmission of SARS-CoV-2. Nat. Commun. 12, 2274 (2021).

    Article  Google Scholar 

  140. Chen, M. K. & Pope, D. G. Geographic Mobility in America: Evidence from Cell Phone Data Technical Report (National Bureau of Economic Research, 2020).

  141. Aleta, A. et al. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nat. Hum. Behav. 4, 964–971 (2020).

    Article  Google Scholar 

  142. Wang, F., Wang, J., Cao, J. & Chen, C. & Ban, XuegangJeff Extracting trips from multi-sourced data for mobility pattern analysis: an app-based data example. Transp. Res. Part C 105, 183–202 (2019).

    Article  Google Scholar 

  143. Deng, H., Du, J., Gao, J. & Wang, Q. Network percolation reveals adaptive bridges of the mobility network response to COVID-19. PLoS ONE 16, e0258868 (2021).

    Article  Google Scholar 

  144. Squire, R. F. Quantifying Sampling Bias in SafegGraph Patterns Technical Report (SafeGraph, 2019).

  145. Coston, A. et al. Leveraging administrative data for bias audits: assessing disparate coverage with mobility data for COVID-19 policy. In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 173–184 (2021).

  146. Miller, G. The smartphone psychology manifesto. Perspect. Psychol. Sci. 7, 221–237 (2012).

    Article  Google Scholar 

  147. Getting the User’s Location (Apple, 2020).

  148. Android Developers Review How Your App Collects and Shares User Data (Google, 2021).

  149. Hoy, MarieaGrubbs & Milne, G. Gender differences in privacy-related measures for young adult Facebook users. J. Interact. Advert. 10, 28–45 (2010).

    Article  Google Scholar 

  150. Ioannou, A., Tussyadiah, I., Miller, G., Li, S. & Weick, M. Privacy nudges for disclosure of personal information: a systematic literature review and meta-analysis. PLoS ONE 16, e0256822 (2021).

    Article  Google Scholar 

  151. Yeh, C.-H. et al. What drives internet users’ willingness to provide personal information? Online Inf. Rev. 42, 923–939 (2018).

    Article  Google Scholar 

  152. Baek, Y. M., Bae, Y., Jeong, I., Kim, E. & Rhee, J. W. Changing the default setting for information privacy protection: what and whose personal information can be better protected? Soc. Sci. J. 51, 523–533 (2014).

    Article  Google Scholar 

  153. Exodus—the Privacy Audit Platform for Android Applications (Exodus, 2021).

  154. Arai, A., Fan, Z., Matekenya, D. & Shibasaki, R. Comparative perspective of human behavior patterns to uncover ownership bias among mobile phone users. ISPRS Int. J. Geoinf. 5, 85 (2016).

    Article  Google Scholar 

  155. Yuan, G., Sun, P., Zhao, J., Li, D. & Wang, C. A review of moving object trajectory clustering algorithms. Artif. Intell. Rev. 47, 123–144 (2017).

    Article  Google Scholar 

  156. Waksman, A. Phones, lambdas, and the joy of snap-to-place technology. Foursquare Blog https://location.foursquare.com/resources/blog/developer/phones-lambdas-and-the-joy-of-snap-to-place-technology/ (2021).

  157. Ankerst, M. & Breunig, M. M. Kriegel, Hans-Peter & Sander, J. örg Optics: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28, 49–60 (1999).

    Article  Google Scholar 

  158. Yang, Y., Cai, J., Yang, H., Zhang, J. & Zhao, X. TAD: a trajectory clustering algorithm based on spatial-temporal density analysis. Expert Syst. Appl. 139, 112846 (2020).

    Article  Google Scholar 

  159. Chen, W., Ji, M. H. & Wang, J. M. T-DBScan: a spatiotemporal density clustering for GPS trajectory segmentation. Int. J. Online Eng. 10, (2014).

  160. Deng, Z., Hu, Y., Zhu, M., Huang, X. & Du, B. A scalable and fast optics for clustering trajectory big data. Clust. Comput. 18, 549–562 (2015).

    Article  Google Scholar 

  161. Birant, D. & Kut, A. ST-DBScan: an algorithm for clustering spatial–temporal data. Data Knowl. Eng. 60, 208–221 (2007).

    Article  Google Scholar 

  162. Li, T., Barwick, PanleJia, Deng, Y., Huang, X. & Li, S. The COVID-19 pandemic and unemployment: evidence from mobile phone data from China. J. Urban Econ. 135, 103543 (2023).

    Article  Google Scholar 

  163. Kim, K., Kim, S. & Lee, D. & Park, Cyn-Young Impacts of social distancing policy and vaccination during the COVID-19 pandemic in the Republic of Korea. J. Econ. Dyn. Control 150, 104642 (2023).

    Article  Google Scholar 

  164. Huang, X. et al. The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the US during the COVID-19 pandemic. Int. J. Digit. Earth 14, 424–442 (2021).

    Article  Google Scholar 

  165. Coven, J. & Gupta, A. Disparities in Mobility Responses to COVID-19 (New York Univ., 2020).

  166. Wardle, J., Bhatia, S., Kraemer, M. U. G., Nouvellet, P. & Cori, A. Gaps in mobility data and implications for modelling epidemic spread: a scoping review and simulation study. Epidemics 42, 100666 (2023).

    Article  Google Scholar 

  167. Heimlich, J. P. & Jackson, C. Air Travelers in America: Findings of a Survey Conducted by IPSOS (Airlins for America, 2018).

  168. Lorengo, M. Three years of TSA throughput data. (3 January 2023); https://mikelor.github.io/three-years-of-tsathroughput

  169. Mohamed, K., Côme, E., Oukhellou, L. & Verleysen, M. Clustering smart card data for urban mobility analysis. IEEE Trans. Intell. Transp. Syst. 18, 712–728 (2016).

    Google Scholar 

  170. Zhong, C., Manley, E., Arisona, S. M., Batty, M. & Schmitt, G. Measuring variability of mobility patterns from multiday smart-card data. J. Comput. Sci. 9, 125–130 (2015).

    Article  Google Scholar 

  171. Fotheringham, A. S. & Wong DavidW. S. The modifiable areal unit problem in multivariate statistical analysis. Environ. Plann. A 23, 1025–1044 (1991).

    Article  Google Scholar 

  172. Chang, M.-C. et al. Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan. BMC Public Health 21, 226 (2021).

    Article  Google Scholar 

  173. Yuan, Y., Jahani, E., Zhao, S., Ahn, Y.-Y. & Pentland, A. S. Implications of COVID-19 vaccination heterogeneity in mobility networks. Commun. Phys. 6, 206 (2023).

    Article  Google Scholar 

  174. Birge, J. R., Candogan, O. & Feng, Y. Controlling epidemic spread: reducing economic losses with targeted closures. Manage. Sci. 68(5), 3175–3195 (2022).

    Article  Google Scholar 

  175. Onakpoya, I. J. et al. SARS-CoV-2 and the role of fomite transmission: a systematic review. F1000Research 10, 233 (2021).

    Article  Google Scholar 

  176. Brodeur, A., Gray, D., Islam, A. & Bhuiyan, S. A literature review of the economics of COVID-19. J. Econ. Surv. 35, 1007–1044 (2021).

    Article  Google Scholar 

Download references

Acknowledgements

We thank B. Lake, M. Whiting, H. Hosseinmardi, and B. Hsiao for helpful discussions, and SafeGraph for making their data easily available. This work was supported by the National Science Foundation XSEDE program (grants SBE200005 and CIS210096 awarded to D.J.W.) as part of the COVID-19 HPC Consortium (https://covid19-hpc-consortium.org/).

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA

    Francisco Barreras & Duncan J. Watts

  2. Operations, Information and Decisions Department, Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    Duncan J. Watts

  3. Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA

    Duncan J. Watts

Authors
  1. Francisco Barreras
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Duncan J. Watts
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.B. and D.J.W. conceived the overall structure of the paper, wrote the paper and approved the final version of the paper. F.B. reviewed the literature, contributed to figures and coded the experiments.

Corresponding authors

Correspondence to Francisco Barreras or Duncan J. Watts.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Computational Science thanks Nishant Kishore and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Table 1.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barreras, F., Watts, D.J. The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling. Nat Comput Sci 4, 398–411 (2024). https://doi.org/10.1038/s43588-024-00637-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-024-00637-0

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing