Using Data to Improve Resiliency Before a Disaster Strikes


Using Data to Improve Resiliency Before a Disaster Strikes

The ability to share and analyze data is crucial in any disaster response process. But because no one knows exactly when or where a disaster will strike, individuals charged with spearheading disaster response efforts are often content to fill budget line items with necessities: trucks, manpower, food, salt for snow emergencies, and a host of other items for hurricane or tornado recovery efforts, rather than invest in IT systems that can improve response time and decrease costs.

On some level, it makes sense. In April, no one wants to think about a potentially catastrophic hurricane that may or may not occur six months later. It is hard to think about improving snow emergency response when its 90 degrees outside. In the off season, when emergency responders go back to their day jobs, preparedness is not top of mind, and data sharing to ease the pain of the next unpredictable emergency is unnecessarily low on the list of priorities.

But down time is the ideal time to begin having conversations with officials and responders at all levels of government. That discussion should include analyzing data from the last emergency response to identify areas of improvement and drawing up memoranda of understanding (MOUs) and establishing real-time data sharing capabilities across departments, agencies, and organizations. Clear-headed thinking and ample preparation happen at times of lowest stress. It is much more effective to plan for the worst when representatives from state and local agencies can come together, create common goals, and make plans to share data and resources before, during, and after the next event.

Why Share Data

Despite the best-laid plans, decisions about deploying life-saving resources are not usually centralized. First responders often lead the way in emergency situations. They often decide on the fly which neighborhoods to clear of snow or which families they can safely rescue.

In the middle of an emergency, few people are thinking about the cost of resources, personnel, deployment, or even the impact of a disaster on the bottom line. Agencies are much more concerned with securing the safety of citizens and cleaning up the mess.

It’s time to flip that script and start focusing on how to improve services and realize cost savings in the face of disaster response. Making important decisions shouldn’t necessarily happen individually or in silos created by different levels of government. Instead, local, county, city, state and even federal government agencies should collaborate and share data to increase efficiency and develop better response efforts.

How to Share Data

Data sharing is the cornerstone of interagency collaboration. Levels of government often have access to differing data streams. Sometimes these streams are redundant—such as how many people reside in a certain neighborhood. Other times, they are disparate. Can any single agency report which homes use a specific waste collection service? Do state-level agencies know which homes are powered by which utility companies? Are localities equipped with geographic knowledge, such as which streams have or are likely to flood in a severe storm?

A data-driven preparedness program begins with a robust geographic information system (GIS) that maps the physical location of streets, homes, businesses, and key landforms in the area. GIS is the backbone of any data-based emergency response program; additional data, such as location of specific snowplows during a snow storm or trucks deployed after a hurricane, can be called up and laid over the existing GIS data to give additional insight to where assistance is most needed and how long it will be before help arrives.

When Hurricane Harvey hit Houston in 2017, it caused more than $125 billion in damage, in part due to the path it cut across the state. Houston’s metro area, home to 6.6 million people, is the fourth-largest in the U.S. The hurricane wiped out power, infrastructure, and thousands of homes. People in the area are still working to rebuild after the wrath of Harvey.

Although city managers will never know when a storm like Harvey will strike, they can plan for the worst by upping their disaster-preparedness programs. The best data-driven disaster recovery programs live in the cloud. Agencies only have to set up data streams once. The programs can be “shut off” when not in use, updated when parameters to data streams change, and scaled when disaster strikes, and people need to respond quickly and effectively.

Best-Case Scenario

The City of Boston provides an example of what can happen when departments and agencies come together and share data. The City hired Qlarion to build a data sharing and visualization platform, called Snow Cop. The program allows city managers to keep eyes on 850 miles of roads and the 750 ice and snow removal vehicles charged with clearing them in real time during one of the worst winters in the city’s recent history. The tool permits public works managers to pinpoint the location of citizen calls for service and easily direct trucks to those locations from a centralized platform.

The platform integrates with existing technology, such as mobile phones and passive barcodes, to track locations. The platform costs less to scale than other solutions because of the easy integration with existing technology.

With the cloud, these platforms can scale easily in other situations that call for disaster response. Sadly, the 2013 Boston Marathon bombing brought the city another catastrophic emergency. But the integrated data streams and operations agreements among government branches allowed Boston to quickly determine which parts of the city could safely resume operations in the face of such an event.

Governments at all levels should consider building cloud-based, data-driven platforms to centralize operations in the face of other natural disasters such as floods and hurricanes. The use of the cloud now allows them to “shut down” these systems and “spin” them up in 10-miuntes when needed, drastically reducing the operational cost of limited-use IT systems for disaster response.

But most of all, governments should build the systems and data sharing relationships in the off-season, to make it easier and less costly for emergency responders to do their jobs when the next emergency strikes.