Social media usage reveals recovery of small businesses after natural hazard events.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
02 04 2020
02 04 2020
Historique:
received:
30
07
2019
accepted:
05
03
2020
entrez:
4
4
2020
pubmed:
4
4
2020
medline:
4
4
2020
Statut:
epublish
Résumé
The challenge of nowcasting the effect of natural hazard events (e.g., earthquakes, floods, hurricanes) on assets, people and society is of primary importance for assessing the ability of such systems to recover from extreme events. Traditional recovery estimates, such as surveys and interviews, are usually costly, time consuming and do not scale. Here we present a methodology to indirectly estimate the post-emergency recovery status (downtime) of small businesses in urban areas looking at their online posting activity on social media. Analysing the time series of posts before and after an event, we quantify the downtime of small businesses for three natural hazard events occurred in Nepal, Puerto Rico and Mexico. A convenient and reliable method for nowcasting the post-emergency recovery status of economic activities could help local governments and decision makers to better target their interventions and distribute the available resources more effectively.
Identifiants
pubmed: 32242023
doi: 10.1038/s41467-020-15405-7
pii: 10.1038/s41467-020-15405-7
pmc: PMC7118130
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1629Références
Neal, D. M. Reconsidering the phases of disasters. Int. J. Mass Emerg. Disasters 15, 239–264 (1997).
Alexander, D. E. Principles of Emergency Planning and Management (Oxford University Press, 2002).
Olshansky, R. B., Hopkins, L. D. & Johnson, L. A. Disaster and recovery: processes compressed in time. Nat. Hazards Rev. 13, 173–178 (2012).
doi: 10.1061/(ASCE)NH.1527-6996.0000077
Horney, J., Dwyer, C., Aminto, M., Berke, P. & Smith, G. Developing indicators to measure post-disaster community recovery in the united states. Disasters 41, 124–149 (2016).
doi: 10.1111/disa.12190
Comerio, M. C. Estimating downtime in loss modeling. Earthq. Spectra 22, 349–365 (2006).
doi: 10.1193/1.2191017
Chang, S.E. Urban disaster recovery: a measurement framework and its application to the 1995 kobe earthquake. Disasters 34, 303–327 (2010).
Harding, A. & Powell, F. Variations in pedestrian traffic count in christchurch due to the september 2010 darfield (canterbury) earthquake. In Proceedings of the Ninth Pacific Conference on Earthquake Engineering: Building an Earthquake-Resilient Society, 14–16 (2011).
Lam, N. S., Pace, K., Campanella, R., LeSage, J. & Arenas, H. Business return in new orleans: decision making amid post-katrina uncertainty. PloS ONE 4, 6765 (2009).
Hino, M., Belanger, S.T., Field, C.B., Davies, A.R. & Mach, K.J. High-tide flooding disrupts local economic activity. Sci. Adv. 5, eaau2736 (2019).
Booth, E., Saito, K., Spence, R., Madabhushi, G. & Eguchi, R. T. Validating assessments of seismic damage made from remote sensing. Earthq. Spectra 27, S157–S177 (2011).
doi: 10.1193/1.3632109
Shermeyer, J. Assessment of electrical & infrastructure recovery in puerto rico following hurricane maria using a multisource time series of satellite imagery. In Earth Resources and Environmental Remote Sensing/GIS Applications IX (eds. Michel, U. & Schulz, K.) (SPIE, 2018) https://doi.org/10.1117/12.2325585 .
De Luca, F. et al. Traffic data as proxy of business downtime after natural disasters: the case of kathmandu. In 11th National Conference on Earthquake Engineering (2018).
Murthy, D. & Gross, A. J. Social media processes in disasters: Implications of emergent technology use. Soc. Sci. Res. 63, 356–370 (2017).
doi: 10.1016/j.ssresearch.2016.09.015
Preis, T., Moat, H. S., Bishop, S. R., Treleaven, P. & Stanley, H. E. Quantifying the digital traces of hurricane sandy on flickr. Sci. Rep. 3, 3141 https://doi.org/10.1038/srep03141 (2013).
Tkachenko, N., Jarvis, S. & Procter, R. Predicting floods with flickr tags. PLoS ONE 12, e0172870 (2017).
doi: 10.1371/journal.pone.0172870
Zou, L., Lam, N. S. N., Cai, H. & Qiang, Y. Mining twitter data for improved understanding of disaster resilience. Ann. Am. Assoc. Geogr. 108, 1422–1441 (2018).
Earle, P. S., Bowden, D. C. & Guy, M. Twitter earthquake detection: earthquake monitoring in a social world. Ann. Geophys. 54, 708–715 (2012).
Vieweg, S., Hughes, A. L., Starbird, K. & Palen, L. Microblogging during two natural hazards events. Proceedings of the 28th International Conference on Human factors in Computing Systems - CHI ’10 (2010).
Sutton, J. et al. Warning tweets: serial transmission of messages during the warning phase of a disaster event. Inf. Commun. Soc. 17, 765–787 (2014).
doi: 10.1080/1369118X.2013.862561
Kryvasheyeu, Y. et al. Rapid assessment of disaster damage using social media activity. Sci. Adv. 2, e1500779 (2016).
doi: 10.1126/sciadv.1500779
Maas, P. et al. Facebook disaster maps: aggregate insights for crisis response & recovery. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3173–3173 (ACM, 2019).
Bagrow, J. P., Wang, D. & Barabási, A.-L. Collective response of human populations to large-scale emergencies. PLoS ONE 6, e17680 (2011).
doi: 10.1371/journal.pone.0017680
Deville, P. et al. Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. USA 11, 15888–15893 (2014).
Lu, X., Bengtsson, L. & Holme, P. Predictability of population displacement after the 2010 haiti earthquake. Proc. Natl. Acad. Sci. USA 109, 11576–11581 (2012).
Wilson, R. et al. Rapid and near Realtime Assessments of Population Displacement Using Mobile Phone Data Following Disasters: the 2015 Nepal Earthquake. PLoS Curr. 1, https://doi.org/10.1371/currents.dis.d073fbece328e4c39087bc086d694b5c (2016).
Facebook. Helping small businesses succeed in a mobile world. https://www.facebook.com/business/news/helping-small-businesses-succeed-in-a-mobile-world (accessed 24 July 2018).
Angus, J. E. The probability integral transform and related results. SIAM Rev. 36, 652–654 (1994).
doi: 10.1137/1036146
Office of the Resident Coordinator. Nepal : Earthquake 5, 1–9 (2015).
Goda, K. et al. The 2015 gorkha nepal earthquake: insights from earthquake damage survey. Front. Built Environ. 1, 1–15 (2015).
United States Department of Energy, Infrastructure Security and Energy Restoration. Hurricanes Maria, Irma, and Harvey September 20 Event Summary (Report #39). Tech. Rep. https://www.energy.gov/sites/prod/files/2017/10/f37/Hurricanes (2017).
FEMA. 2017 Hurricane Season FEMA After-Action Report, https://www.fema.gov/media-library-data/1531438753896-273f27679ba04c93301af90546abae18/2017FEMAHurricaneAAR.PDF (2018).
Shermeyer, J. Assessment of electrical and infrastructure recovery in puerto rico following hurricane maria using a multisource time series of satellite imagery. In Earth Resources and Environmental Remote Sensing/GIS Applications IX, vol. 10790, 1079010 (International Society for Optics and Photonics, 2018).
Quick facts: Hurricane maria’s effect on puerto rico january 19, 2018, https://reliefweb.int/report/puerto-rico-united-states-america/quick-facts-hurricane-marias-effect-puerto-rico (2018).
ACAPS. MEXICO AND GUATEMALA Earthquake Epicentre: Pacific Ocean, 8km southwest of Pijijiapan, Chiapas state in Mexico Magnitude in Mexico: 8.1 Magnitude in Guatemala: 7.6 Crisis overview, 1–6, https://reliefweb.int/sites/reliefweb.int/files/resources/20170911_acaps_start_briefing_note_mexico_and_guatemala_earthquake.pdf (2017).
UNITAR-UNOSAT. Pijijiapan earthquake M w 8 . 1, Mexico Population Exposure in Mexico - Population Exposure Analysis, 4020 (2017).
Palen, L. & Anderson, K. M. Crisis informatics–new data for extraordinary times. Science 353, 224–225 (2016).
doi: 10.1126/science.aag2579
Shermeyer, J. Comet time series visualizer: Cometts. J. Open Source Softw. 4, 1047 (2019).
doi: 10.21105/joss.01047
Estadísticas.PR: Instituto de estadísticas de pr. https://www.estadisticas.pr (accessed 11 Feb 2019).
OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org , https://www.openstreetmap.org (2018).
Boeing, G. Osmnx: new methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput. Environ. Urban Syst. 65, 126–139 (2017).
doi: 10.1016/j.compenvurbsys.2017.05.004
Commission, N. P. et al. Post disaster needs assessment. Vol. A and B. Kathmandu: Government of Nepal (2015).