Social media inundation data: urban flood impacts and response assessment of 2020 Chengdu rainstorm

Abstract

During and shortly after a flooding, traditional flood data sources such as remote sensing data are scarce and not readily available. As the amount of volunteered geographic information created by social media grows, it is a valuable and rapidly available information source to inform flood response and recovery. Based on the popular platform, Weibo (static data) and Tiktok (dynamic data), this paper examines the spatio-temporal patterns of public responses towards urban flooding in Chengdu city during August 17-25, 2020. Temporal evolution of social media activities is investigated to track the flood process and further compared with observed precipitation data. Moreover, major flood impacts were assessed through frequency analysis of impact-related keywords. Finally, spatial information is extracted and the typical hotspots are selected for detailed analysis. This study can demonstrate the value of social media data for flood assessment and rapid damage estimation information from social media data could help decision makers in resource allocation and management under emergencies.