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.

  • Comment
  • Published:

Thinking fast and slow in disaster decision-making with Smart City Digital Twins

Nature Computational Science volume 1, pages 771–773 (2021)Cite this article

Subjects

Many cities are vulnerable to disaster-related mortality and economic loss. Smart City Digital Twins can be used to facilitate disaster decision-making and influence policy, but first they must accurately capture, predict, and adapt to the city’s dynamics, including the varying pace at which changes unfold.

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

Relevant articles

Open Access articles citing this article.

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: Fast and slow disaster decision-making dynamics.

References

  1. Gu, D. Exposure and Vulnerability to Natural Disasters for World’s Cities (UN Department of Economic and Social Affairs, Population Division, 2019).

  2. Wegener, M., Gnad, F. & Vannahme, M. The Time Scale of Urban Change (IRPUD, 1983).

  3. Kahneman, D. Thinking, Fast and Slow (Macmillan, 2011).

  4. Billion-Dollar Weather and Climate Disasters: Events, National Center for Environmental Information (NOAA, 2021); https://www.ncdc.noaa.gov/billions/events/US/2016–2020

  5. Mohammadi, N. & Taylor, J. E. Smart city digital twins. In Proc. IEEE Symposium Series on Computational Intelligence 1–5 (2017).

  6. Mohammadi, N. & Taylor, J. E. Smart City Digital Twins (PAS, 2020); https://www.planning.org/publications/document/9209455

  7. DesRoches, R. & Taylor, J. E. The Bridge 48, 13 (2018).

    Google Scholar 

  8. Phases of Emergency Management (FEMA, 2021); https://www.hsdl.org/?abstract&did=488295

  9. Rinehart, R. E. & Garvey, E. T. Nature 273, 287–289 (1978).

    Article  Google Scholar 

  10. Bok, B., Caratelli, D., Giannone, D., Sbordone, A. M. & Tambalotti, A. Annu. Rev. Econom 10, 615–643 (2018).

    Article  Google Scholar 

  11. D’Agostino, A., Giannone, D., Lenza, M. & Modugno, M. Nowcasting Business Cycles: A Bayesian Approach to Dynamic Heterogeneous Factor Models (Emerald Group Publishing Limited, 2016).

  12. Ravuri, S. et al. Nature 597, 672–677 (2021).

    Article  Google Scholar 

  13. Schorfheide, F. & Song, D. J. Bus. Econom. Statist 33, 366–380 (2015).

    Article  MathSciNet  Google Scholar 

  14. Richardson, A., van Florenstein Mulder, T. & Vehbi, T. Int. J. Forecast 37, 941–948 (2021).

    Article  Google Scholar 

  15. Kapteyn, M. G., Pretorius, J. V. & Willcox, K. E. Nat. Comput. Sci 1, 337–347 (2021).

    Article  Google Scholar 

Download references

Acknowledgements

This material is based upon work supported by the National Science Foundation (NSF) (grant numbers 1760645, 1837021, and 1929928 to J.E.T. and N.M.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF.

Author information

Authors and Affiliations

  1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Neda Mohammadi & John E. Taylor

Authors
  1. Neda Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John E. Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.M. and J.E.T. conceived and designed the research, wrote the first draft, edited the manuscript, contributed to revisions, and approved the manuscript.

Corresponding author

Correspondence to John E. Taylor.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammadi, N., Taylor, J.E. Thinking fast and slow in disaster decision-making with Smart City Digital Twins. Nat Comput Sci 1, 771–773 (2021). https://doi.org/10.1038/s43588-021-00174-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-021-00174-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

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