Social Sensing and Big Data Computing for Disaster Management

ISBN-13: 9780367617677 / Miękka / 2023 / 192 str.

Opis

Social Sensing and Big Data Computing for Disaster Management captures recent advancements in leveraging social sensing and big data computing for supporting disaster management.

Spis treści

1. Introduction to social sensing and big data computing for disaster management

Zhenlong Li, Qunying Huang and Christopher T. Emrich

2. Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma

Muhammed Ali Sit, Caglar Koylu and Ibrahim Demir

3. Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies

Manzhu Yu, Qunying Huang, Han Qin, Chris Scheele and Chaowei Yang

4. A visual–textual fused approach to automated tagging of flood-related tweets during a flood event

Xiao Huang, Cuizhen Wang, Zhenlong Li and Huan Ning

5. Rapid estimation of an earthquake impact area using a spatial logistic growth model based on social media data

Yandong Wang, Shisi Ruan, Teng Wang and Mengling Qiao

6. Mapping near-real-time power outages from social media

Huina Mao, Gautam Thakur, Kevin Sparks, Jibonananda Sanyal and Budhendra Bhaduri

7. Social and geographical disparities in Twitter use during Hurricane Harvey

Lei Zou, Nina S. N. Lam, Shayan Shams, Heng Cai, Michelle A. Meyer, Seungwon Yang, Kisung Lee, Seung-Jong Park and Margaret A. Reams

8. Population distribution modelling at fine spatio-temporal scale based on mobile phone data

Petr Kubíček, Milan Konečný, Zdeněk Stachoň, Jie Shen, Lukáš Herman, Tomáš Řezník, Karel Staněk, Radim Štampach and Šimon Leitgeb

9. Discovering the relationship of disasters from big scholar and social media news datasets

Liang Zheng, Fei Wang, Xiaocui Zheng and Binbin Liu

10. A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

Zhe Zhang, Hao Hu, Dandong Yin, Shakil Kashem, Ruopu Li, Heng Cai, Dylan Perkins and Shaowen Wang

O autorze

Zhenlong Li is Associate Professor in the Department of Geography at the University of South Carolina, USA where he established and leads the Geoinformation and Big Data Research Laboratory. His primary research focuses on geospatial big data analytics, spatiotemporal analysis/modelling, and CyberGIS/GeoAI. By synthesizing advanced computing technologies, geospatial methods, and spatiotemporal principles, his research aims to advance knowledge discovery and decision making to support domain applications including disaster management, climate change, human mobilities, and public health.

Qunying Huang is Associate Professor in the Department of Geography at the University of Wisconsin–Madison, USA. Her fields of expertise include spatial computing, spatial data mining, and spatial data analytics. Dr. Huang’s research bridges the gap between computer and information science (CIScience) and GIScience by generating new computational algorithms and methods to make sense of complex big spatial datasets obtained from both the physical sensing (e.g. remote sensing) and social (e.g. social media) sensing networks. The problem domains of her research are related to natural hazards and human mobility.

Christopher T. Emrich is Endowed Associate Professor of Environmental Science and Public Administration within the School of Public Administration and a founding member of the newly formed National Center for Integrated Coastal Research at the University of Central Florida (UCF Coastal), USA. His research/practical service includes applying geospatial technologies to emergency management planning and practice, long-term disaster recovery, and the intersection of social vulnerability and community resilience in the face of catastrophe.