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.

  • Article
  • Published:

Untapped capacity of place-based peer-to-peer resource sharing for community resilience

Nature Cities volume 2, pages 47–57 (2025)Cite this article

Subjects

Abstract

During and after a disaster, people share resources with family, friends and neighbors to tide them over difficult times. The conventional top-down approach for disaster relief overlooks the wealth of critical resources that exist within communities. Here we explicitly model place-based peer-to-peer (P2P) resource sharing and evaluate its impact on community resilience to disasters. Using data from two urban communities in Seattle, Washington State, we confirm substantial untapped capacity for enhanced community resilience through place-based P2P resource sharing. Under a 5-day isolation scenario, place-based P2P sharing can reduce a community’s resilience loss by 13.4–100%; on average, 22–44 social ties per household support an 80% sharing rate of surplus resources. These findings suggest that place-based P2P sharing could be a viable strategy for disaster response across US communities, in addition to the current, government-led effort. Our methodological framework is transferable to other urban communities interested in enhancing disaster resilience.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Study area.
Fig. 2: Methodology.
Fig. 3: Resilience loss comparison.
Fig. 4: Effects of social ties.
Fig. 5: Number of social ties and survival rates.

Similar content being viewed by others

Data availability

The survey data collected in the study contain household mailing addresses and names, and this part of the data is not available due to privacy issues. However, the generated aggregated data during the current study are available via GitHub at https://github.com/UW-THINKlab/P2P_sharing_open_source.

Code availability

The code used for the analysis is available via GitHub at https://github.com/UW-THINKlab/P2P_sharing_open_source.

References

  1. Twigg, J. & Mosel, I. Emergent groups and spontaneous volunteers in urban disaster response. Environ. Urban. 29, 443–458 (2017).

    Article  Google Scholar 

  2. Prociv, K. Pic of the week: people helping people after Hurricane Matthew. The Washington Post https://www.washingtonpost.com/news/capital-weather-gang/wp/2016/10/28/pic-of-the-week-people-helping-people-after-hurricane-matthew/ (2016).

  3. Martinez, M. Texans used mutual aid to help their communities through a devastating winter storm. The Texas Tribune https://www.texastribune.org/2021/02/23/mutual-aid-texas-storm/#:~:text=What%20started%20as%20an%20oatmeal,help%20them%20escape%20the%20cold (2021).

  4. Greenfield, N. Mutual aid and disaster justice: ‘we keep us safe’. NRDC https://www.nrdc.org/stories/mutual-aid-and-disaster-justice-we-keep-us-safe (2022).

  5. National Incident Management System (Federal Emergency Management Agency, US Department of Homeland Security, 2017).

  6. Whole Community Approach to Emergency Management: Principles, Themes, and Pathways for Action (Federal Emergency Management Agency, US Department of Homeland Security, 2011).

  7. Kim, K., Ghimire, J. & Yamashita, E. Sharing during Disasters: Learning from Islands Preliminary Findings and Initial Implications for Action (Natural Hazards Center Research Resources, 2021).

  8. Wong, S. D., Yu, M., Kuncheria, A., Shaheen, S. A. & Walker, J. L. Willingness of Hurricane Irma evacuees to share resources: a multi-modeling approach. Transportmetrica A 19, 2017064 (2023).

    Article  Google Scholar 

  9. Idziorek, K., Abramson, D. B., Kitagawa, N., Yamamoto, T. & Chen, C. Factors influencing willingness to share resources postdisaster: a cross-cultural comparison between US and Japanese communities. Nat. Hazards Rev. 24, 04023044 (2023).

    Article  Google Scholar 

  10. Zimmerman, R. Social implications of infrastructure network interactions. J. Urban Technol. 8, 97–119 (2001).

    Article  Google Scholar 

  11. Chang, S. E. & Shinozuka, M. Measuring improvements in the disaster resilience of communities. Earthq. Spectra 20, 739–755 (2004).

    Article  Google Scholar 

  12. Davis, C. A. Water system service categories, post-earthquake interaction, and restoration strategies. Earthq. Spectra 30, 1487–1509 (2014).

    Article  Google Scholar 

  13. Ouyang, M. & Duenas-Osorio, L. Multi-dimensional hurricane resilience assessment of electric power systems. Struct. Safety 48, 15–24 (2014).

    Article  Google Scholar 

  14. Wang, Y., Chen, C., Wang, J. & Baldick, R. Research on resilience of power systems under natural disasters—a review. IEEE Trans. Power Syst. 31, 1604–1613 (2015).

    Article  Google Scholar 

  15. Miller-Hooks, E., Zhang, X. & Faturechi, R. Measuring and maximizing resilience of freight transportation networks. Comput. Oper. Res. 39, 1633–1643 (2012).

    Article  Google Scholar 

  16. Gu, Y., Fu, X., Liu, Z., Xu, X. & Chen, A. Performance of transportation network under perturbations: reliability, vulnerability, and resilience. Transport. Res. Part E 133, 101809 (2020).

    Article  Google Scholar 

  17. Yu, D. et al. Disruption of emergency response to vulnerable populations during floods. Nat. Sustain. 3, 728–736 (2020).

    Article  Google Scholar 

  18. Hanifan, L. J. The rural school community center. Ann. Am. Acad. Polit. Social Sci. 67, 130–138 (1916).

    Article  Google Scholar 

  19. Coleman, J. S. Social capital in the creation of human capital. Am. J. Sociol. 94, 95–120 (1988).

    Article  Google Scholar 

  20. Dynes, R. Social capital: Dealing with community emergencies. Homel. Secur. Aff. 2, 5 (2006).

  21. Aldrich, D. P. & Meyer, M. A. Social capital and community resilience. Am. Behav. Sci. 59, 254–269 (2015).

    Article  Google Scholar 

  22. Koliou, M. et al. State of the research in community resilience: progress and challenges. Sustain. Resil. Infrastruct. 5, 131–151 (2020).

    Article  Google Scholar 

  23. Sanyal, S. & Routray, J. K. Social capital for disaster risk reduction and management with empirical evidences from sundarbans of india. Int. J. Disaster Risk Reduct. 19, 101–111 (2016).

    Article  Google Scholar 

  24. Takano, R. & Nomura, M. Strengthened social ties in disasters: threat-awe encourages interdependent worldviews via powerlessness. PLoS ONE 18, 0285049 (2023).

    Article  Google Scholar 

  25. Sadri, A. M. et al. The role of social capital, personal networks, and emergency responders in post-disaster recovery and resilience: a study of rural communities in Indiana. Nat. Hazards 90, 1377–1406 (2018).

    Article  Google Scholar 

  26. Liu, W., Gerber, E., Jung, S. & Agrawal, A. The role of human and social capital in earthquake recovery in Nepal. Nat. Sustain. 5, 167–173 (2022).

    Article  Google Scholar 

  27. IHME United States – King County, Washington Life Expectancy and Cause-Specific Mortality by Census Tract 1990–2014 (University of Washington, 2023).

  28. Dillman, D. A. Mail and Internet Surveys: The Tailored Design Method—2007 Update with New Internet, Visual, and Mixed-Mode Guide (John Wiley & Sons, 2011).

  29. Breza, E., Chandrasekhar, A. G., McCormick, T. H. & Pan, M. Using aggregated relational data to feasibly identify network structure without network data. Am. Econ. Rev. 110, 2454–2484 (2020).

    Article  Google Scholar 

  30. Otte, E. & Rousseau, R. Social network analysis: a powerful strategy, also for the information sciences. J. Inf. Sci. 28, 441–453 (2002).

    Article  Google Scholar 

  31. Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P. & Tomkins, A. Geographic routing in social networks. Proc. Natl Acad. Sci. USA 102, 11623–11628 (2005).

    Article  Google Scholar 

  32. Xu, Y., Santi, P. & Ratti, C. Beyond distance decay: discover homophily in spatially embedded social networks. Ann. Am. Assoc. Geogr. 112, 505–521 (2022).

    Google Scholar 

  33. Adger, W. N., Hughes, T. P., Folke, C., Carpenter, S. R. & Rockstrom, J. Social-ecological resilience to coastal disasters. Science 309, 1036–1039 (2005).

    Article  Google Scholar 

  34. Bruneau, M. et al. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq. Spectra 19, 733–752 (2003).

    Article  Google Scholar 

  35. Moreno, J., Lara, A. & Torres, M. Community resilience in response to the 2010 tsunami in Chile: the survival of a small-scale fishing community. Int. J. Disaster Risk Reduct. 33, 376–384 (2019).

    Article  Google Scholar 

  36. Bělín, M., Jelínek, T. & Jurajda, Š. Preexisting social ties among Auschwitz prisoners support Holocaust survival. Proc. Natl Acad. Sci. USA 120, 2221654120 (2023).

    Article  Google Scholar 

  37. Granovetter, M. S. The strength of weak ties. Am. J. Sociol. 78, 1360–1380 (1973).

    Article  Google Scholar 

  38. Jones, B. & Rachlin, H. Social discounting. Psychol. Sci. 17, 283–286 (2006).

    Article  Google Scholar 

  39. Strombach, T. et al. Charity begins at home: cultural differences in social discounting and generosity. J. Behav. Decision Making 27, 235–245 (2014).

    Article  Google Scholar 

  40. Schreiner, N., Pick, D. & Kenning, P. To share or not to share? Explaining willingness to share in the context of social distance. J. Consum. Behav. 17, 366–378 (2018).

    Article  Google Scholar 

  41. Mooallem, J. Enrico L. Quarantelli: he proved that disasters bring out the best in us. The New York Times https://www.nytimes.com/interactive/2017/12/28/magazine/the-lives-they-lived-enrico-l-quarantelli.html (2017).

  42. Quarantelli, E. L. Conventional beliefs and counterintuitive realities. Social Res. 75, 873–904 (2008).

    Article  Google Scholar 

  43. Seattle Emergency Communication Hubs. Seattle Emergency Hubs https://seattleemergencyhubs.org/ (2023).

  44. SNAP Seattle neighborhoods actively prepare – emergency management. Seattle Government https://www.seattle.gov/emergency-management/prepare/prepare-your-neighborhood/seattle-neighborhoods-actively-prepare#onlinetoolkit (2023).

  45. Shirado, H., Iosifidis, G., Tassiulas, L. & Christakis, N. A. Resource sharing in technologically defined social networks. Nat. Commun. 10, 1079 (2019).

    Article  Google Scholar 

  46. Yabe, T., Rao, P. S. C., Ukkusuri, S. V. & Cutter, S. L. Toward data-driven, dynamical complex systems approaches to disaster resilience. Proc. Natl Acad. Sci. USA 119, 2111997119 (2022).

    Article  Google Scholar 

  47. Schor, J. Debating the sharing economy. J. Self-govern. Manag. Econ. 4, 7–22 (2016).

    Google Scholar 

  48. Arcidiacono, D., Gandini, A. & Pais, I. Sharing what? The ‘sharing economy’in the sociological debate. Sociol. Rev. 66, 275–288 (2018).

    Article  Google Scholar 

  49. Mont, O., Curtis, S. K. & Palgan, Y. V. Organisational response strategies to COVID-19 in the sharing economy. Sustain. Prod. Consum. 28, 52–70 (2021).

    Article  Google Scholar 

  50. Shmidt, M. Sharing economy as a field: revisiting debates and introducing new research avenues. Sociol. Compass 17, 13120 (2023).

    Article  Google Scholar 

  51. Newman, M. E., Watts, D. J. & Strogatz, S. H. Random graph models of social networks. Proc. Natl Acad. Sci. USA 99, 2566–2572 (2002).

    Article  Google Scholar 

  52. Catanzaro, M., Boguná, M. & Pastor-Satorras, R. Generation of uncorrelated random scale-free networks. Phys. Rev. E 71, 027103 (2005).

    Article  Google Scholar 

  53. Guan, X. & Chen, C. General methodology for inferring failure-spreading dynamics in networks. Proc. Natl Acad. Sci. USA 115, 8125–8134 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

C.C. and K.I. were supported by the US National Science Foundation (NSF) (1951418) and the US Department of Transportation (69A3551747116). C.C. was also supported by NSF (2311405). Z.L. and A.C. were funded by the National Natural Science Foundation of China (72071174), the Research Grants Council of the Hong Kong Special Administrative Region (PolyU 15222221), the Research Institute of Land and Space (1-CD7N), the Department of Civil and Environmental Engineering (WZ06) at the Hong Kong Polytechnic University, and the Research Student Attachment Programme (RSAP) at the Hong Kong Polytechnic University. The modeling and analysis conducted in this study was done by Z.L. during his visit to C.C.’s THINK lab (https://sites.uw.edu/thinklab) at the University of Washington in 2023. The empirical surveys were carried out by K.I. for her PhD dissertation.

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    Zhengyang Li & Anthony Chen

  2. Department of Civil & Environmental Engineering, University of Washington, Seattle, WA, USA

    Zhengyang Li & Cynthia Chen

  3. Department of Earth, Environmental, and Geographical Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA

    Katherine Idziorek

  4. Department of Industrial & Systems Engineering, University of Washington, Seattle, WA, USA

    Cynthia Chen

Authors
  1. Zhengyang Li
    View author publications

    Search author on:PubMed Google Scholar

  2. Katherine Idziorek
    View author publications

    Search author on:PubMed Google Scholar

  3. Anthony Chen
    View author publications

    Search author on:PubMed Google Scholar

  4. Cynthia Chen
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Study conception: C.C. and K.I.; research design: C.C. and Z.L.; data collection: K.I.; data analysis: Z.L.; paper preparation—original writing: Z.L. and C.C.; paper preparation—editing and review: C.C., Z.L., K.I. and A.C. All authors reviewed and approved the paper.

Corresponding author

Correspondence to Cynthia Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

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 notes on the study area, survey, social network construction, and additional sensitivity analyses.

Reporting Summary

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

Li, Z., Idziorek, K., Chen, A. et al. Untapped capacity of place-based peer-to-peer resource sharing for community resilience. Nat Cities 2, 47–57 (2025). https://doi.org/10.1038/s44284-024-00175-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44284-024-00175-w

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