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. 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. We believe that near real-time collection, analysis, mapping, and reporting on social media data during the rapid onset disasters can give emergency managers and others more relevant and possibly better decision support information.
GIBD has been conducting research on leveraging social sensing data (geo-tweets) to enhance situational awareness from several aspects including rapid flood mapping, evacuation analysis, and on-topic disaster-related information(tweets) automatic extraction.
To call for more efforts on this research field, we have organized a series of paper sessions and panel sessions in the American Association of Geographers Annual Meeting since 2017, as well as a book published from a special issue in IJDE.
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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. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. |
