facebook pixel image Law enforcement agencies get analytics assist during hurricanes

Law enforcement agencies get analytics assist during hurricanes

Within two weeks of each other, two massive hurricanes struck the southern U.S. First it was Hurricane Harvey, which made landfall in Texas Aug. 25 and inundated much of Houston and the Texas coast with several feet of rain, causing unprecedented flooding and devastation in one of the nation’s largest population centers. Then came Hurricane Irma on Sept. 10, now the most powerful hurricane to ever make landfall in the U.S. when it battered the west coast of Florida and points north.

The one-two punch of these record-breaking storms put the resilience of countless communities to the test, not to mention the network of law enforcement agencies, military support and disaster relief organizations that each needed to coordinate a response of unforeseen scale. In several places across the country, those efforts were aided by advanced data analytics platforms already in use, helping officials conduct search-and-rescue operations, measure storm damage and much more.

Using data to serve and protect

Out of the destruction that these storms caused, several incredible stories of bravery and ingenuity emerged. Besides the damage from floodwaters and high winds, officials in Texas and Florida had to deal with massive power outages. In fact, Hurricane Irma may have caused the single largest power outage ever recorded in the U.S., as it left at least 16 million people throughout Florida and the southeastern U.S. without electricity for several days. That posed a serious threat to those who relied on home health equipment, powered by the electric grid, to stay alive.

In preparation for both storms, health officials and first-responders tapped into data that could help them pinpoint those who were most at risk during power outages. Stateline, an editorial wing of the Pew Charitable Trusts, reported on Ryan Pedigo, public health director of Hillsborough County which includes the city of Tampa Bay, Florida. Pedigo needed to figure out who in the area was most at risk based on their documented need for home health assistance. Instead of tapping into the federal database, Pedigo used his agency’s own directory that provided a more detailed, analytical view of those residents, their health needs and their location.

In the final days before Irma made landfall in Florida, Pedigo used this data to contact all 2,800 county residents who needed special medical assistance. At least 24 hours before the storm made its way to Tampa, Pedigo coordinated fleets of buses and ambulances to pick up residents who could not transport themselves to local shelters. Once the storm had moved on and it was safe to travel, first responders then returned high-risk patients to their homes as soon as they could verify they had power restored and their property was intact.

This response was made possible by the specialized database system Pedigo was using, which is designed to track people who depend on electric medical devices and allow emergency services to coordinate. The database includes more than 2.5 million people, accounting for around 90 percent of Americans who rely on electricity to power home health devices, according to Stateline. But officials estimate that this accounts for only a fraction of those who actually end up relocating to emergency medical shelters in advance of a storm. While that may sound troubling, it’s actually a testament to the effectiveness of the data-driven effort to ensure public health officials can help as many people as possible. Many high-risk and power-dependent residents are able to relocate to safety with family members, or can acquire the backup power supplies they need before a storm makes landfall.

New database use cases

After the storms hit, officials on the ground got even more creative with their use of state-level databases. As Hurricane Harvey crawled along the east coast of Texas, emergency responders were able to use a statewide database to pinpoint downed power lines and mobilize to that exact location. Other agencies crosschecked this state data with Medicare records to find homes of people who would need special medical attention in the immediate aftermath of the storm. Even electric utility companies tapped into this same data to understand where to concentrate efforts to restore power.

The incredible scope of devastation caused by Harvey and Irma means that we may not know the real success or failure of any of these efforts. Cleanup and damage assessment in the wake of these storms is expected to continue for years in certain areas, and some of the most tragic stories in the aftermath of both storms have already become widely publicized. But these novel examples of using data to reimagine the difficult task that is disaster relief response shine a hopeful light on the situation. We can only expect more creativity in data analytics from law enforcement and public health officials as they respond to new challenges in the future.