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Full Description
Social Sensing and Big Data Computing for Disaster Management captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically, analysed within this book are some of the promises and pitfalls of social sensing data for disaster relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems.
Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all citizens to become part of a large sensor network which is low cost, more comprehensive, and always broadcasting situational awareness information. However, data collected with social sensing is often massive, heterogeneous, noisy, and unreliable in some aspects. It comes in continuous streams, and often lacks geospatial reference information. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. Meanwhile, big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion.
This book was originally published as a special issue of the International Journal of Digital Earth.
Contents
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
