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Infrequent activities predict economic outcomes in major American cities

Nature Cities volume 1, pages 305–314 (2024)Cite this article

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Abstract

Many studies have revealed the predictive power of the most frequent, regular and habitual mobility patterns. However, it remains unclear which components of the mobility patterns contain the most informative signals for predicting disparate economic development across urban areas. Here we use machine learning to predict economic outcomes by analyzing the heterogeneous mobility networks of 687 activities from more than 560,000 anonymized users in Boston, Chicago and Miami. We find that mobility patterns are highly predictive of the current and future economic development in major American cities but, surprisingly, the high predictive power is concentrated on infrequent, irregular and exploratory activities. These predictive activities account for only less than 2% of total visits but successfully explain more than 50% of variation in economic outcomes. Future research should shift more attention from regular visits to irregular activities, and policymakers could leverage these infrequent yet informative activities to manage urban economic development.

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Fig. 1: Diverse activities in mobility networks.
Fig. 2: Mobility activities predict economic outcomes in Boston, Chicago and Miami.
Fig. 3: Predictive activities are associated with infrequent mobility patterns.
Fig. 4: Predictive activities are associated with irregular mobility patterns.

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Data availability

The data that support the findings of this study are available from Cuebiq through their Data for Good program, but restrictions apply to the availability of these data, which were used under the licence for the current study and are therefore not publicly available. Information on how to request access to the data, and its conditions and limitations, can be found at https://www.cuebiq.com/about/data-for-good/. The locations and activity categories of visits were obtained via Foursquare using their Public Search API. The public data source of this study (for example, ACS) is available in the Github repository at https://github.com/cjsyzwsh/economic_growth_usa.git.

Code availability

The analysis was conducted using Python. The scripts that support the findings of this study are also available via the Github repository at https://github.com/cjsyzwsh/economic_growth_usa.git.

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Acknowledgements

We thank Cuebiq, who kindly provided us with the mobility dataset for this research through their Data for Good program. S.W. acknowledges partial support from a University of Florida ROSF-2023 grant. G.W. is partially supported by the NSF (grant no. 1952096). E.M. acknowledges support by Ministerio de Ciencia e Innovación/Agencia Española de Investigación (MCIN/AEI/10.13039/501100011033) through grant no. PID2019-106811GB-C32, and the NSF under grant no. 2218748.

Author information

Author notes

  1. These authors contributed equally: Shenhao Wang, Yunhan Zheng.

  2. These authors jointly supervised this work: Shenhao Wang, Esteban Moro.

Authors and Affiliations

  1. Department of Urban and Regional Planning, University of Florida, Gainesville, Florida, USA

    Shenhao Wang

  2. Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Shenhao Wang, Esteban Moro & Alex ‘Sandy’ Pentland

  3. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yunhan Zheng

  4. Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA

    Guang Wang, Takahiro Yabe, Esteban Moro & Alex ‘Sandy’ Pentland

  5. Department of Computer Science, Florida State University, Tallahassee, USA

    Guang Wang

  6. Tandon School of Engineering, New York University, Brooklyn, NY, USA

    Takahiro Yabe

  7. Network Science Institute, Northeastern University, Boston, MA, USA

    Esteban Moro

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Contributions

S.W., G.W., T.Y., E.M. and A.S.P. conceptualized the work. S.W. and Y.Z. performed the methodology, and designed and implemented the experiments. S.W. wrote the original draft, which was reviewed and edited by S.W., Y.Z., G.W. and T.Y. S.W., E.M. and Y.Z. curated the data, which were visualized by S.W. and Y.Z. S.W. and A.S.P. supervised and administered the project.

Corresponding authors

Correspondence to Shenhao Wang or Esteban Moro.

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Nature Cities thanks Yang Yue and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Wang, S., Zheng, Y., Wang, G. et al. Infrequent activities predict economic outcomes in major American cities. Nat Cities 1, 305–314 (2024). https://doi.org/10.1038/s44284-024-00051-7

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