Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media

Yan, Zhiyu; Guo, Xiaogang; Zhao, Zilong; Tang, Luliang

doi:10.1016/j.scs.2023.105077

Cited by 20 publications

(3 citation statements)

References 58 publications

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“…Focuses on the dynamic fabric of civic life within the Municipality of Patras, Greece. Aligns with the existing literature [4,[6][7][8][9][10][11][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], which highlights the significance of temporal analysis of urban data in informing planning and decision-making.…”

Section: Discussionmentioning

confidence: 58%

“…Key findings include lower emissions during holidays, significant contributions from peak hours, and a positive correlation between bus and urban rail transit carbon footprints. In the research of [24], the authors introduce a novel framework for fine-grained information extraction and dynamic spatial-temporal awareness in disaster-stricken areas using social media data. Applied to a case study of urban floods in Anhui, China, the framework reveals significant spatial and temporal consistency between flooding hotspots and rainfall centers.…”

Section: Related Workmentioning

confidence: 99%

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Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

Gkontzis,

Kotsiantis,

Feretzakis

et al. 2024

BDCC

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In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes increasingly paramount. This study employs time series analysis to scrutinize citizen interactions with the coordinate-based problem mapping platform in the Municipality of Patras in Greece. The research explores the temporal dynamics of reported urban issues, with a specific focus on identifying recurring patterns through the lens of seasonality. The analysis, employing the seasonal decomposition technique, dissects time series data to expose trends in reported issues and areas of the city that might be obscured in raw big data. It accentuates a distinct seasonal pattern, with concentrations peaking during the summer months. The study extends its approach to forecasting, providing insights into the anticipated evolution of urban issues over time. Projections for the coming years show a consistent upward trend in both overall city issues and those reported in specific areas, with distinct seasonal variations. This comprehensive exploration of time series analysis and seasonality provides valuable insights for city stakeholders, enabling informed decision-making and predictions regarding future urban challenges.

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Section: Discussionmentioning

confidence: 58%

Section: Related Workmentioning

confidence: 99%

Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

Gkontzis,

Kotsiantis,

Feretzakis

et al. 2024

BDCC

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“…It can automatically extract place names from texts by analyzing the structure and grammar of sentences. Previous studies on disaster information extraction commonly adopted NER methods such as the bidirectional long short-term memory network (BiLSTM) combined with a Conditional Random Field (CRF) layer, which were trained with a large number of place name tags (Kundzewicz et al, 2019;Yan et al, 2024). The NER methods identify all place names in the text regardless of their contextual background, leading to the recognition of place names not related to the flood disaster.…”

Section: Introductionmentioning

confidence: 99%

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

Fu,

Schultz,

Lyu

et al. 2024

Preprint

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Abstract. Urban floods present a threat in China, demanding an understanding of their spatiotemporal distribution. Current flood datasets primarily offer provincial-scale insights and lack temporal continuity, which leads to a challenge in detailed analysis. To create a consistent national dataset of flood events, this study introduces a machine learning framework by applying online news media as a primary data source to construct a county-level dataset of urban flood events from 2000 to 2022. Using the Bidirectional Encoder Representations from Transformers (BERT) model, we achieved robust performance in information extraction, with an F1 score of 0.86 and an exact match score of 0.82. Further, a combined model of Bidirectional Long Short-term Memory (BiLSTM) networks with a Conditional Random Field (CRF) layer effectively identified flood locations. Our analysis reveals that the temporal trend of flooded cities in the news-based dataset is similar to the China Flood and Drought Bulletin. Furthermore, the consistency of flood events in the news with the typhoon trajectory in two cases, and the connection between flood occurrences and flood conditioning factors, confirm the accuracy of spatial distribution. The validated news-based dataset analyzes urban floods in China from both temporal and spatial perspectives. First, this dataset shows the seasonal characteristics of flood events, which are concentrated in the summer. From 2000 to 2022, the peak year for floods was 2010, and excluding the influence of peak year, the overall temporal trend of total flood occurrence shows an increase. Spatially, the distribution of floods decreases from southeast to northwest, with Guangxi Province having the highest number of floods. Additionally, the Yangtze and Pearl River basins are most frequently affected by urban floods. The subtropical climate zone is the most susceptible to flooding. This study provides an automated and effective method for constructing a national flood event dataset and reveals the spatiotemporal characteristics of urban flooding in China.

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Examining spatial and socioeconomic disparities in internet resilience during extreme weather events: a case study of Hurricane Harvey and Winter Storm Uri

Gupta,

Liu,

Mostafavi

2024

Urban Info

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The resilience of internet service is crucial for ensuring consistent communication, situational awareness, facilitating emergency response in our digitally-dependent society. However, due to empirical data constraints, there has been limited research on internet service disruptions during extreme weather events. To bridge this gap, this study utilizes observational datasets on internet performance to quantitatively assess the extent of internet disruption during two recent extreme weather events. Taking Harris County in the United States as the study region, we jointly analyzed the hazard severity and the associated internet disruptions in the context of two extreme weather events. The results show that the hazard events significantly impacted regional internet connectivity. There exists a pronounced temporal synchronicity between the magnitude of disruption and hazard severity: as the severity of hazards intensifies, internet disruptions correspondingly escalate, and eventually return to baseline levels post-event. The spatial analyses show that internet service disruptions can happen even in areas that are not directly impacted by hazards, demonstrating that the repercussions of hazards extend beyond the immediate area of impact. This interplay of temporal synchronization and spatial variance underscores the complex relationships between hazard severity and Internet disruption. Furthermore, the socio-demographic analysis suggests that vulnerable communities, already grappling with myriad challenges, face exacerbated service disruptions during these hazard events, emphasizing the need for prioritized disaster mitigation strategies and interventions for improving the resilience of internet services. To the best of our knowledge, this research is among the first studies to examine the Internet disruptions during hazardous events using a quantitative observational dataset. The insights obtained hold significant implications for city administrators, guiding them towards more resilient and equitable infrastructure planning.

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Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media

Cited by 20 publications

References 58 publications

Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

Examining spatial and socioeconomic disparities in internet resilience during extreme weather events: a case study of Hurricane Harvey and Winter Storm Uri

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