Big data drives disaster preparedness, helping predict the unexpected

Business and Technology Writer

Planning for the unexpected is key to disaster preparedness. Running models, gathering data and utilizing analytics algorithms are essential to forecasting and being ready for what first responders may encounter in a given deployment. Big data allows scientists to project what could occur during a disaster in countless possible emergency scenarios.

"Each data set provides some sort of insight into the potential impact an event may have on a particular area during a particular time frame," says Joe Hillis, operations director at the Information Technology Disaster Resource Center. "Historical data for a region or given location is generally a good baseline, and an even better indicator of future events." Here are a few key ways that data analytics are preparing first responders for the unpredictable scenarios that can arise during an emergency.

Quake warnings: Big data drives disaster preparedness of the best ways to survive a disaster is to know it's coming. That's the thinking behind disaster preparedness projects that utilize data analytics, according to expert analyst Bernard Marr. "For many years earthquake prediction relied almost entirely on monitoring the frequency of quakes and using this to establish when they were likely to reoccur," he writes in a recent Forbes article. "Big data analysis has opened up the game to a new breed of earthquake forecasters using satellite and atmospheric data combined with statistical analysis."

As such, quake predictions are becoming more accurate. A recent earthquake in Sumatra measured 6.4 on the Richter scale, and data predicted it ahead of time. That advance information is available for free at sites such as Quake Hunters, and it stands to give a huge advantage to emergency officials.

Modeling weather

Natural disasters caused 7,700 deaths and $110 billion in damage globally in 2014, Reuters reported. Storms accounted for a significant percentage of that loss. Data analytics certainly has a role to play when it comes to predicting such events and helping responders get civilians out of harm's way. Responders are using the approach to identify regions that would be particularly vulnerable to unexpected hurricanes, winter storms and other meteorological events.

The National Oceanic & Atmospheric Administration (NOAA) announced that its High Impact Weather Prediction Project helped responders save lives and property during the January 2015 blizzard in the Northeast. "This is due to the recent improvements we've made to the [Global Forecast System], including higher resolution, improved physics and better access to new data," says Louis Uccellini, director of NOAA's National Weather Service, in a release.

Placing resources in advance

In the U.S., the Federal Emergency Management Agency (FEMA) is also working to give first responders tools to predict the unexpected in disasters. Key to the agency's approach is putting historical data in the hands of any key stakeholders. Because emergency officials are able to see the frequency and nature of state-of-emergency declarations that have occurred across their respective areas, they can start to see how relief and recovery money is spent and where it has the greatest impact. The data analysis can be used for proactive disaster preparedness.

"We need to have a better way of communicating risk and showing the vulnerability of communities," writes Craig Fugate on the FEMA blog. "It's looking at addressing risk from the future — from land-use planning to codes to ways we can build in our environment that doesn't grow our risk and hopefully buy down future risk."

That's a data-driven approach echoed by other experts in the field. "With a continuous supply of fresh data from external sources, trends may emerge," says Hillis. "The more data, the better the agencies can anticipate, plan and generally be better prepared to respond to an event."

Meeting the unexpected head-on is about being armed with information. Data analytics are opening avenues to powerful new disaster-forecasting tools, giving scientists and first responders more advance notice than ever before. When it comes to forewarning people in harm's way, it's safe to predict that big data will continue to save lives.

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