Every year typhoons severely disrupt the normal rhythms of human activities and pose serious threats to China’s coast. Previous studies have shown that the impact extent and degree of a typhoon can be inferred from various geolocation datasets. However, it remains a challenge to unravel how dwellers respond to a typhoon disaster and what they concern most in the places with significant human activity changes. In this study, we integrated the geotagged microblogs with the Tencent’s location request data to advance our understanding of dweller’s collective response to typhoon Hato and the changes in their concerns over the typhoon process. Our results show that Hato induces both negative and positive anomalies in humans’ location request activities and such anomalies could be utilized to characterize the impacts of wind and rainfall brought by Hato to our study area, respectively. Topic analysis of Hato-related geotagged microblogs reveals that the negative location request anomalies are closely related to damage-related topics, whereas the positive anomalies to traffic-related topics. The negative anomalies are significantly correlated with economic loss and population affected at city level as suggested by an over 0.7 adjusted R². The changes in the anomalies can be used to portray the response and recovery processes of the cities impacted.

Understanding the public’s diverse linguistic expressions about rainfall and flood provides a basis for flood disaster studies and enhances linguistic and cultural awareness. However, existing research tends to overlook linguistic complexity, potentially leading to bias. In this study, we introduce a novel algorithm capturing rainfall and flood-related expressions, considering the relationship between precipitation observations and linguistics expressions. Analyzing 210 million social media microblogs from 2017, we identified 594 keywords, 20 times more than usual manually created bag-of-words. Utilizing Large Language Model, we categorized these keywords into rainfall, flood, and other related terms. Semantic features of these keywords were analyzed from the viewpoint of popularity, credibility, time delay, and part-of-speech, finding rainfall-related terms most common-used, flood-related keywords often more time delayed than precipitation, and notable differences in part-of-speech across categories. We also assessed spatial characteristics from keyword and city-centric perspectives, revealing that 49.5% of the keywords have significant spatial correlation with differing median centers, reflecting regional variations. Large and disaster-impacted cities show the richest expression diversity for rainfall and flood-related terms.

1. Population datasets play a crucial role in assessing and understanding human-environment relationships (Linard et al. 2012). Generating accurate, high-resolution, and dynamic population is chall...

How do urban emergency management systems evolve through policy learning, and what institutional mechanisms drive this evolution? This study addresses this question by examining three versions of megacity Changsha’s flood prevention emergency plans spanning nearly a decade (2012, 2017, 2020), supplemented by semi-structured interviews with 12 government officials and four official post-disaster assessment reports. We construct an integrated analytical framework combining large language model-based task segmentation (DeepSeek-V3), Chinese semantic embedding (bge-large-zh-v1.5) with BERTopic topic modeling, and bipartite social network analysis to systematically trace the structural evolution of flood emergency management. Qualitative evidence from thematic coding and documentary analysis is triangulated with computational findings to establish causal mechanisms underlying policy changes. Results reveal an evolutionary trajectory from single-department centralized management to multi-department division of labor and finally to cross-departmental coordinated response. Information Planning, Monitoring and Early Warning, and Material Support emerged as focal points by 2020, driven by institutional incentives favoring anticipatory governance, while Engineering Emergency Response and Logistics Support maintained core positions throughout. Publicity and Reporting, Social Security, and Material Support developed as critical bridging nodes for information flow and resource coordination. We identify three interacting policy learning pathways—crisis-driven learning mediated through hierarchical accountability, top-down diffusion of national directives with local adaptive implementation, and horizontal cross-jurisdictional policy borrowing—that jointly produce bounded experimentation under hierarchical constraints. These findings extend crisis-driven policy learning theory to authoritarian governance contexts and provide new methodological tools for understanding adaptive changes in urban emergency management systems.