Spatiotemporal Changes of Urban Rainstorm-Related Micro-Blogging Activities in Response to Rainstorms: A Case Study in Beijing, China

Abstract

Natural disasters cause significant casualties and losses in urban areas every year. Further, the frequency and intensity of natural disasters have increased significantly over the past couple of decades in the context of global climate change. Understanding how urban dwellers learn about and response to a natural hazard is of great significance as more and more people migrate to cities. Social media has become one of the most essential communication platforms in the virtual space for users to share their knowledge, information, and opinions about almost everything in the physical world. Geo-tagged posts published on different social media platforms contain a huge amount of information that can help us to better understand the dynamics of collective geo-tagged human activities. In this study, we investigated the spatiotemporal distribution patterns of the collective geo-tagged human activities in Beijing when it was afflicted by the “6-22” rainstorm. We used a variety of machine learning and statistical methods to examine the correlations between rainstorm-related microblogs and the rainstorm characteristics at a fine spatial and a fine temporal scale across Beijing. We also studied factors that could be used to explain the changes of the rainstorm-related blogging activities. Our results show that the human response to a disaster is very consistent, though with certain time lags, in the virtual and physical spaces at both the grid and city scales. Such a consistency varies significantly across our study area.

Recent Advances in Geographic Information System for Earth Sciences

“…Social media data collected through Twitter, Facebook, Flicker, and Weibo can be used to improve understanding of urban hydrology. Wang et al [10] examine rainstorm-related micro-blogging activities in response to rainstorms in an urban environment at fine spatial and temporal scales. The study collected hourly precipitation data and a total of 3.32 million Weibo blogs geotagged with Beijing, China from June to September 2017.…”

Quantifying Urban Linguistic Diversity Related to Rainfall and Flood across China with Social Media Data

“…The rise of big data from social media has significantly evolved the methods and focus of linguistic diversity research [24][25][26][27][28][29][30]. Blodgett, Green [31] used Twitter geolocation data to explore dialectal variations in African-American English.…”

Comparative Analysis of Different Heavy Rainstorm Periods Lasting for Two Consecutive Days in the Qinba Region under the Influence of the Southwest Vortex

“…As one of the most severe weather systems, a rainstorm triggered by the SWV is a complex and representative example of a typical rainstorm [3,4]. The SWV has played a crucial role in numerous significant precipitation events and floods, ranking as the second most important rainstorm system in China after typhoons [5,6]. Previous studies have found that there are significant differences in the precipitation distribution caused by the different movement paths of the SWV [7][8][9].…”