Motivation

Social media has played a key role in distributing information during disasters. People, both affected citizens and those outside the impact zone, and media outlets have used social media to collate and share disaster-related information during wildfires, earthquakes, floods, and tornados. This has created a well-established pattern: “a disaster strikes, and the crisis data collection begins”. For example, during the Calgary flood in 2013, people heavily used social media to post information, photos, and breaking news regarding the ongoing event. Besides citizens, Calgary’s official emergency responders, such as the Calgary Police Service and the City of Calgary, also used social media to broadcast safety-critical information and situation updates. So, both citizens and emergency response organizations have started to recognize the added value of information available via social media during disasters.

Challenges

However, there are some challenges when considering social media as an information source for disaster response. In particular, social media streams contain large amounts of irrelevant messages such as rumors, advertisements, or even misinformation. So, one major challenge to using social media messages like tweets is how to process them and deliver credible and relevant information to disaster responders and citizens. Another challenge relates to the amount of information that flows on social media and how to analyze them in real-time. Finally, social media messages are brief (e.g., 280 characters for tweets) and informal and, therefore, applying the methods that are used to process structured, long texts such as news articles to deal with them may lead to poor and misleading results.

Solution

Disaster Watch is a disaster mapping platform that collects data from twitter, extracts disaster-related information from tweets, and visualizes the results on a map. It enables users to quickly locate all the information in different geographic areas at a glance, and to find the physical constraints caused by the disaster, such as non-accessible river bridges, and take an informed action. Such information helps public and disaster responders (e.g., humanitarian organizations, disaster relief agencies, or local actors) answer the following questions:

  • When did the disaster happen?
  • Where are the affected areas?
  • What are the impacts of the disaster?

The answers to these questions provide spatial (where), temporal (when), and thematic (what) information about an event. The insights gained from analysis of such information can be of great value to decision-makers in different phases of a disaster (from preparedness to response and recovery).

Disaster Watch is built using free and open source software, open standards, and open data - TensorFlow 2.0, NodeJS and Express, VueJS, Vuetify, and Mapbox GL JS are used to create the system components. It collects tweets using Twitter’s streaming API, analyzes them using a deep learning model built by TensorFlow 2.0, and displays disaster-related tweets on a map. The application is hosted on Amazon’s AWS infrastructure.

See more details here: https://docs.google.com/document/d/15BGGFOvMZq2P_QHmKbe71MpBv1wMuFbJ15RlEkapnEs/edit?usp=sharing

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