Emergency management plan priorities: How data analytics are changing rescue work
When disaster strikes, every moment can mean the difference between life and death. For first responders, a 21st century emergency management plan is one that makes maximum use of the best information available and delivers it to the key stakeholders on the ground. That's where data analytics comes into play. By addressing the complications of gathering and integrating information from many sources about an emergency's myriad variables, and by analyzing follow-up dangers and how survivors reacted early on, big data is changing the way first responders prioritize their efforts.
On the scene: What responders face
Emergency management plans already focus on getting first responders to the scene and assigning roles to stakeholders. Now, big data in all its varied formats can play a key part in outlining the best response plan possible in real-time.
A recent Computing Research Association report pointed out that it's not just the potential chaos of an emergency that affects response, but also "the need for maintaining the privacy and security of data; the politics, sociology, psychology and native language issues," plus whatever damage has been done to the basic infrastructure needed to assist survivors. Because data related to complex crises can get complicated quickly, a strong response needs powerful ways of leveraging information and cutting through confusion.
Data analytics: Leveraging the power of priorities for the emergency management plan
How do data analytics help deliver the best information to first responders during a disaster? In a nutshell, it's about processing vast amounts of incoming information, and then using it to illuminate patterns and relief opportunities. This can happen in the following key ways:
- Prioritize urgent sites and situations. Responders want up-to-date intelligence about survivors' locations and available resources. Data analytics shows these kinds of details. According to an iRevolutions blog post, by gathering information from social media, responders mapped the geography of Oklahoma's tornado damage in 2013. Actual mapping applications are emerging that can prioritize which zones need attention first, all based on what users post during a crisis.
- Track resources. Data analytics can help map the locations of critical resources like ambulances and medical facilities. First responders can then illustrate the key connections that support these resources to prioritize efforts. For example, if a substation is out, knowing which hospitals are serviced by that utility and how long their generators will last will help first responders decide whether to focus on the substation immediately, or if it can wait another two hours.
- Advise on new dangers. First responders need to know if new emergencies are about to become a priority event during a response. A forest fire might be approaching a chemical plant, for example. Analytics can help responders see these details, and they can highlight the convergence of dangerous factors in real time.
- Assess and inform about the best evacuation routes. As conditions shift, prioritizing the best ways out of danger is another advantage that data analytics bring to an emergency management plan. Additionally, satellite and population data can show where people are likely to go first in a disaster. Wired explains how this was used this in a recent report about Typhoon Haiyan in the Philippines. In a recent interview, Michael Li, formerly of Foursquare, said that app connection data fueled response during Hurricane Sandy in New York. "You could visualize the blackouts south of 34th Street and see new pockets of activity," he said. "People tended to congregate near the few Wi-Fi and electricity points." That's valuable data when prioritizing where to put responders.
Next steps: Horizons for big data and the emergency management plan
Scientists will continue to harness the volume and velocity of incoming data during disasters. Patrick Meier, director of social innovation at the Qatar Computing Research Institute, recently worked with both variables following the 2010 earthquake in Haiti. The quantity and speed of incoming social media communications threatened to outstrip his team's ability to process the data, according to his account in Forbes.
More recently, Meier says that the ability to take in that kind of data and classify it for responders is improving. In some cases, his algorithms are performing at 70 to 90 percent accuracy. "The beauty of this technology is that it continues to learn and improve over time, as users 'teach' the classifier not to make the same mistakes," he wrote.
The future of data analytics and the emergency management plan is being written, one line of code at a time. It's a promising future, one that ensures that even more lives will be saved thanks to powerful tools for prioritizing key aspects of disaster response.
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