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Disaster Relief and Big Data Featured

Disaster Relief and Big Data Kelly Sikkema

Big data analysis is proving to be highly beneficial to businesses. The patterns are beneficial to business practice and can improve the efficiency and effectiveness of managing disasters and emergencies in organizations. Thanks to the availability of mobile devices such as smartphones, disasters can be measured in real-time and information relayed to people for rapid, efficient and accurate response. The use of big data to predict and help with disaster relief comes after centuries of struggle. Therefore, agencies can respond to disasters quickly and effectively by adopting and analyzing big data. Here are some ways that big data is helping in disaster relief.

Predicting, preparing and preventing

Every disaster provides an enormous amount of data regardless of its size. Gathering information from every disaster, officials, and the first responder helps gain insights into how the disaster occurred, thus helping forecast future incidences. With today’s advanced sensor data collection, surveillance and satellite imagery, it becomes easy to survey and assess critical areas. For instance, knowing a particular area that has been flooded and how much floods are useful for mapping out areas that are prone to floods. This allows for planning regarding where to store supplies and other rescue resources.

Furthermore, the information can help understand the future reach of floods in such areas so that people can be warned earlier. With artificial intelligence, Google can predict flood patterns in a particular country and enhance the accuracy of response efforts. Similarly, AI-based drones can collect data for mapping to enable accuracy in tackling wildfires. All these are things that were difficult to achieve using conventional methods.

Gathering real-time data from survivors

 Wearables and personal technology are proving instrumental in helping responders manage emergencies. For instance, information transferred from mobile phones, smart watches or other connected medical devices can be analyzed to help responders respond by rescuing individuals. Emergency calls and actions of the dispatchers can be used to identify callers and make relevant decisions based on relevant data.

Data can be obtained on social media because survivors can turn to social media. Geotagging or timestamps left by survivors can be used to create a real-time picture of events and gather insights from users in the affected areas regarding power outages, road closure and infrastructure damages. Furthermore, survivors can use social media to mark themselves as safe, which is helpful for emergency responders, response teams and governments to identify areas to focus their resources. Government agencies can use social media to reach targeted individuals and provide large-scale warnings of disasters.

Pinpointing disaster

Location technology has advanced to the extent that it can be used to identify the location of a disaster easily. With technologies such as those used by navigation apps, it becomes easy for limited disaster, and its effects can easily be identified. A recent example of such use is the Carr Fire in Redding, California, where navigational apps and technologies were used to track the disaster. The real-time maps were used to warn citizens of the impending danger by showing the exact locations of the spreading fires. During the COVID-19 pandemic, real-time data from apps were also used to track the number of infections on any given day in particular regions. From these examples, it is clear that location technology is becoming advanced in smartphones and IoT devices and are helping responders determine things such as caller location. This is important considering the number of people getting saved yearly due to the adoption of precise location technologies. Data mapping technology can show the location of the responders and whether the location can be accessed by road, foot, car or by helicopter. 

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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