Assessing wild fire risk in the United States using social media data

Yaojie Yue, Kecui Dong, Xiangwei Zhao and Xinyue Ye

Journal of Risk Research, 2021, vol. 24, issue 8, 972-986

Abstract: Massive Geo-tagged social media data provide new opportunities for disaster risk assessment, prevention, and management. This article presents a proof of concept for assessing wildfire risk using Geo-tagged social media data, by taking wildfire risk as a function of wildfire hazard and social–ecological vulnerability. The case study of the United States shows that the regions with the highest wildfire hazard are concentrated in the Western, while the most vulnerable areas are mainly distributed in the Eastern, the Western Coast, and the Southern parts of the nation. Areas with high wildfire risk are mainly located in the Northwestern and Southeastern United States. It shows that the wildfire risk level has significant linear relationship with population density. Massive and vulnerable population might result in significant increase in wildfire risk perception. We conclude that Geo-tagged social media data have great potential in disaster risk studies.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/13669877.2019.1569098 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jriskr:v:24:y:2021:i:8:p:972-986

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RJRR20

DOI: 10.1080/13669877.2019.1569098

Access Statistics for this article

Journal of Risk Research is currently edited by Bryan MacGregor

More articles in Journal of Risk Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().