Correlating Twitter Use with Disaster Resilience at Two Spatial Scales: A Case Study of Hurricane Sandy. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Wang, Kejin; Lam, Nina S. N.; Mihunov, Volodymyr
• Is Part Of: Annals of GIS. Volume 29:Issue 1(2023)
• Page Start: 1
• Page End: 20
• Publication Date: 2023-01-02
• Subjects: Disaster resilience -- Hurricane Sandy -- social media -- sentiment analysis -- spatial scale; Geographic information systems -- Periodicals; Periodicals; 910.285
• DOI: 10.1080/19475683.2023.2165545
• Languages: English
• ISSNs: 1947-5683

Abstract

ABSTRACT: Disaster resilience describes the ability of a community to bounce back from disaster impacts by resilience building activities. Social media provides an innovative way to observe human attitudes and responses, especially during disasters. However, most previous social media and disasters studies were conducted at a coarse spatial scale such as by county. This study analyzes Twitter activities during Hurricane Sandy in 2012, at the county and the zip code area levels in the five affected states. The study examines two questions: (1) will the relationships between disparities in social media use and disparities in disaster resilience found at the county level in previous studies still hold at the zip code area level? And (2) what new information or patterns can be revealed with the zip code area level analysis? Results show that correlations between Twitter use indices and social-environmental variables representing community resilience found at the county level in previous studies still hold, but they are weaker at the zip code area level. The study also shows that zip code areas that have major transportation hubs and commercial activities or low night-time population are major factors affecting Twitter use indices and hence the correlations. Future research should consider adding data on land use types and population dynamics to help improve social media use for disaster resilience analysis. Furthermore, employing a multiscale analysis approach can reduce uncertainties involved in analysis and obtain a more thorough understanding of the relationships between Twitter use and geographical and socioeconomic characteristics of the affected communities.