Rapid onset disasters, often difficult to prepare for and respond to, make disaster management a challenging task worldwide. Disaster and emergency management effectiveness depends heavily on making good decisions in near-real time under extreme duress. These key, often life-saving, decisions are possible only with real-time data sources and the ability to timely collect, process, synthesize, and analyze these multi-sourced data. Traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or immediately following damaging events. For example, stream gauges are only useful for flood mapping while the stations are functioning properly and before they are overtopped by floodwaters and rendered inoperable.
Fortunately, sharing information such as texts, images, and videos through social media platforms enables all citizens to become part of a large sensor network and a homegrown disaster response team. Compared to traditional physical sensors, such a citizen-sensor network (social sensing) is low cost, more comprehensive, and always broadcasting situational awareness information. Social sensing enables wide-scale interaction where primary data becomes collectively resourceful, self-policing, and generates information that is otherwise hard to obtain in a useful timeframe for disaster areas. For example, with social sensing, massive amounts of micro-level disaster information (e.g. site specific damage) can be captured in real-time through social media platforms (e.g. Twitter, Facebook) and voluntarily reported via dedicated crowdsourcing applications (volunteered geographic information, VGI), enabling rapid assessment of evolving disaster situations.
On the other hand, data collected with social sensing is often massive, heterogeneous, noisy, unreliable, and comes in continuous streams. This is inherently “Big Data”, for example, millions of microblog posts from different social media platforms can be generated in a short time right after an impactful disaster. Hence, Big Data computing methods and technologies such as cloud computing, distributed geo-information processing, spatial statistics/modeling, data mining, spatial database, 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.
Along these lines, this special issue on “Social Sensing and Big Data Computing for Disaster Management” by the International Journal of Digital Earth aims to capture recent advancements in leveraging social sensing and big data computing for supporting disaster management in one or more disaster phases (mitigation, preparedness, response, and recovery). Specifically, we solicit original unpublished research articles that can shed light on the opportunities, challenges and solutions of leveraging social sensing and big data computing for supporting disaster management.
Potential topics include (but are not limited to) the following:
- Innovative approaches to synthesize multi-sourced social sensing data and/or traditional data (e.g. remote sensing) for disaster management
- Analyzing and visualizing human movement patterns before, during, and after disaster events
- Disaster event detection, early warning, and impact/damage assessment with social sensing
- Mining and extracting actionable information for rapid emergency response and relief coordination
- Geovisual analytics of social sensing data during a disaster
- Integrating data mining (machines) and crowdsourcing (human) to support decision-making
- New tools and solutions for real-time big social sensing data collecting, processing, analyzing, and visualizing
- Exploring public perception, sentiments, and understanding towards disaster events
- Novel social engagement approaches to effectively link the public in an organized way toward contributing to emergency response, recovery
- Data quality, reliability, and privacy issues of social sensing for disaster management
- Leveraging big social sensing data to enhance social resilience
- November 15, 2017, 800-word abstract submission to guest editors
- December 1, 2017, full paper submission invited
- March 1, 2018, full paper submission online
- May 1, 2018, revision/rejection notification
- August 1, 2018, paper acceptance notification
The International Journal of Digital Earth is an international peer-reviewed academic journal (SCI-E with a 2016 impact factor 2.292) focusing on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world.
Submissions must follow the instructions to authors outlined on the Taylor & Francis web page for the International Journal of Digital Earth found: here. Word templates are available on the web site and papers are typically 5000‐8000 words in length.
Papers should be submitted online at the International Journal of Digital Earth's Manuscript Central Site: here. New users should first create an account. Once a user is logged onto the site submissions should be made via the Author Centre. Please indicate the paper is submitted to Special Issue on “Social Sensing and Big Data Computing for Disaster Management” in the cover letter.
Each paper will receive comments from at least three reviewers. The special issue will include a maximum of 8 papers.
We look forward to your contributions. Please do not hesitate to contact the Guest Editors in case of questions.
- Special Issue Guest Editor: Zhenlong Li, Department of Geography, University of South Carolina, USA (firstname.lastname@example.org)
- Special Issue Guest Editor: Qunying Huang, Department of Geography, University of Wisconsin-Madison, USA (email@example.com)
- Special Issue Guest Editor: Christopher Emrich, School of Public Administration, University of Central Florida, USA (firstname.lastname@example.org)