Building a Real-Time Geo-Targeted Event Observation (Geo) Viewer for Disaster Management and Situation Awareness

Tsou, Ming‐Hsiang; Jung, Chin‐Te; Allen, Christopher; Yang, Jiue-An; Han, SangUk; Spitzberg, Brian H.; Dozier, J.

doi:10.1007/978-3-319-57336-6_7

Cited by 28 publications

(17 citation statements)

References 17 publications

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“…Previous studies researched crossreferencing tweets with prioritised locations for mapping flooding (Middleton et al, 2014), and location-based queries during floods, using georeferencing tweets (Laylavi et al, 2016). A recent application, GeoViewer, queried a location (as defined by a user), via the Twitter Streaming API, to output maps of tweets in real-time (Tsou et al, 2017).…”

Section: ) How To Programmatically Identify At a National-scale (Acrmentioning

confidence: 99%

Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

Barker

Macleod

2019

Environmental Modelling & Software

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Social media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20-29th June 2016. Highlights • Prototype real-time social geodata pipeline for flood events and demonstration dataset • National-scale flood warnings/river levels set 'at-risk areas' in Twitter API queries • Monitoring multiple locations (without keywords) retrieved current, geotagged tweets • Novel application of word embeddings in flooding context identified relevant tweets • Pipeline extracts tweets to visualise using open-source libraries (SciKit Learn/Gensim)

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Section: ) How To Programmatically Identify At a National-scale (Acrmentioning

confidence: 99%

Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

Barker

Macleod

2019

Environmental Modelling & Software

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“…Social media analytics is the term that is commonly used for the analysis of social media data by combining and adopting different computational methods (Stieglitz et al 2018c(Stieglitz et al , 2018b. Several studies highlight the benefits of social media analytics, which builds upon social media platforms such as Twitter and Facebook, for curating, analysing and summarising crisis-related information to help decision-makers and relief efforts (Vieweg et al 2010;Terpstra et al 2012;Imran et al 2014Nazer et al 2017;Tsou et al 2017;Reuter and Kaufhold 2018). The related literature in this domain can be divided into two categories, namely (i) 'situational awareness', which corresponds to research work that focuses on understanding the big picture during a disaster event and (ii) 'actionable', which corresponds to works that focus on identifying and extracting actionable information that enables first responders to help victims.…”

Section: Social Media-driven Crisis Informaticsmentioning

confidence: 99%

Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria

Alam

Ofli

Imran

2019

Behaviour & Information Technology

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People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyse high-volume and highvelocity data streams, dealing with information overload, among others. To eliminate such limitations, in this work, we first show that textual and imagery content on social media provide complementary information useful to improve situational awareness. We then explore ways in which various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields can exploit such complementary information generated during disaster events. Finally, we propose a methodological approach that combines several computational techniques effectively in a unified framework to help humanitarian organisations in their relief efforts. We conduct extensive experiments using textual and imagery content from millions of tweets posted during the three major disaster events in the 2017 Atlantic Hurricane season. Our study reveals that the distributions of various types of useful information can inform crisis managers and responders and facilitate the development of future automated systems for disaster management.

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“…In recent years, global demand for near real-time (NRT) information on natural disasters has increased considerably [18][19][20]. Time is vital during a disaster event [21] in order to evacuate vulnerable people at risk, minimise the socio-economic, ecologic and cultural impact of the event and restore society to normal as soon as possible [21].…”

Section: Flood and Flood Impact Detectionmentioning

confidence: 99%

A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

Ackere

Verbeurgt

Sloover

et al. 2019

Water

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Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future.

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Building a Real-Time Geo-Targeted Event Observation (Geo) Viewer for Disaster Management and Situation Awareness

Cited by 28 publications

References 17 publications

Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria

A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

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