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Natural disaster management: Analytics give first responders a data-driven advantage

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Business and Technology Writer

Data analytics has put the power of more effective natural disaster management in the hands of first responders.

Using records about geography, population, mobile-device usage and more, algorithms that discern underlying patterns and associations across numerous industries are also allowing federal and local agencies to quickly react to floods, fires and other deadly scenarios.

The following examples showcase some of the ways natural disaster management is improved by data and continuously evolving analytical tools.

Identifying population-based 'hot spots'

Details about past road, utility and environmental vulnerabilities allow first responders to plot tomorrow's emergency strategies today. This approach applies to people as well. By gathering data about important population subsets, such as elderly communities, infant and youth concentrations and areas where individuals need specific mobility support, responders can apply resources to those locations. They can also send information and real-time messaging to those residents as a storm approaches.

Considering the high potential for privacy issues, as Noah Reiter notes in Emergency Management, it's crucial for agencies build bridges between their information technology teams and the public health and safety organizations that keep data on the area's most vulnerable populations.

Forecasting on-the-ground activity

Call records stemming from previous disaster locations give data analysts a key tool to plan future reactions. As local mobile-network operators capture details about where users connect to functioning towers, anonymized sets of data can show scientists how populations move in response to emergency situations such as flooding, as shown in this UN Global Pulse report.

Cross-referencing evidence of phone-user behavior to emergency messages sent during an event provides further insight as well, showing emergency planners what messaging works best and what elements of alert communications can actually make a scenario more complicated.

Revealing new approaches to rescue and recovery

Maps are a key starting point for modern, effective natural disaster management. However, to put mapping data to use, responders need to be able to combine geographical records with real-time images, in-the-moment evidence of new circumstances and knowledge of what agencies and individuals control access to a disaster site.

In 2001, for example, integrating thermal data with real-time visuals and municipal records allowed 9/11 responders to better assess response and safety concerns within the debris and substructures of the World Trade Center. This multi-layered approach to mapping a dangerous area is applicable to natural disasters, such as the recent large-scale wildfires in the western U.S., where responders needed warnings about how heat and smoke would affect their ability to enter and exit segments of burning land.

Sharing data to benefit all parties

There is an ongoing consideration, however, when it comes to the ways technology can make natural disaster management more effective. For data to best serve agencies and civilians, it must be shareable.

As Mohammad Javad Valadan Zoej and his co-authors point out on ResearchGate in their paper about how data is exchanged by disaster respondents and the agencies that tend to collect this information, one key message is that a foundation of policies, protocols and ways to exchange information must be an ongoing priority. They call this system a 'spatial data infrastructure.'

Because tools continue to evolve, data analytics promise new and focused ways to mitigate risk and provide relief to emergency-stricken areas. Likewise, the effectiveness that analytics brings to next-generation planning will continue to depend on teams identifying and linking together the information that advances disaster response and creates new best-case scenarios for responders and survivors alike.

Improve disaster management time with advanced analytics.