Using data before, during and after natural disasters

By Yue Yu

What data sets can reporters get ahead of natural disasters? How can reporters cover disasters as they happen? What kind of follow-up leads should they chase?

Matt Dempsey from the Houston Chronicle, Omaya Sosa from Puerto Rico’s Center for Investigative Journalism and Lee Zurik from WVUE-TV in New Orleans broke down the chain of reporting at their CAR Conference panel.

Preparing for disasters

Working in Houston, Dempsey knows his hurricanes. Disasters like hurricanes are a test of the knowledge a newspaper has of the community it serves, and it’s best to know the community’s vulnerable points, Dempsey said.

He offered a list to tell reporters where to find information ahead of time to prepare for all kinds of disasters:

Flooding

• Floodplain shapefiles
• Dam conditions or ACOE ratings
• NFIP claims, policies by blockgroup/county/community
• Buyout data
• Rain data from NOAA
• Number of shelters and shelter plan

Wildfires

• Shapefiles of previous fires
• WUI shapefiles from the University of Wisconsin
• Raster maps to look at changes in vegetation
• Building codes (check to see what kind of development is allowed and compare those to Firewise guidelines)

Earthquakes

• Fault maps
• Hazard maps
• Landslide hazards
• Real time data feeds
• Historical earthquake data

Hurricanes

• Historical hurricane path data
• Data on past hurricane strength, rainfall and wind statistics, deaths, injuries
• Evacuation plans and procedures

Tornadoes

• Historical trends and location maps
• Siren locations, inspections, repair orders, test frequency, plan for usage
• Building codes

Chemical release or explosion

• Local emergency planning committee (LEPC)
• Tier II chemical inventories
• ECHO
• OSHA
• Rtk.net. RMP

Blizzards

• Number of plow trucks
• Amount of salt or de-icer available
• Paths for snow plows

General disaster preparedness

• Know who runs the Office of Emergency Management in your area
• Value of assessor data and home sales data
• Building codes and regulations
• Disaster plans
• Academic studies on your area’s disaster preparedness

During disasters — starting from scratch

Sosa had only a handful of volunteers to cover the local news when hurricanes Irma and Maria hit Puerto Rico last year. She didn’t even know whether her editor was alive. They’d lost WiFi, water, electricity and food.

They had no fancy databases, either. Sosa decided she would build her own.

It all started from making sense of the situation Puerto Rico was in. In the first 72 hours, official data suggested that the death toll was 16. Sosa interviewed two doctors, and they had already had nine deaths. That was when she noticed the official statistics didn’t match with reality.

She put her sneakers on, went out in the field and started interviewing sources on the ground, including doctors, police agents, rescue workers, funeral home directors, city officials and neighbors. Meanwhile, her team started questioning the official death toll data and pressed the government for information.

The team also collected missing persons reports and fliers, lost-and-found posts on social media, and more information from community leaders and radio. They even sent out Google forms for people to fill out when their family members died during the hurricanes.

These sources helped the team build a database that started out as an incomplete spreadsheet in Excel. After months of data collection, it became a list of all the uncounted deaths the government failed to include in the official data.

After sharing her own experience, Sosa offered a few more final tips:

• Get to know the community.
• Get to know all the details of the system.
• Don’t lose perspective: The data is not the story.
• Always “marry” data with reality. You’ll be surprised.
• Make sure your story is relevant. Why is this important?
• Humanize the data

Recovery spending — follow the money

Like Sosa, Zurik’s data story started out simple. He built a huge story about heavy government spending on parish school constructions after Hurricane Katrina, based on spreadsheets, pivot tables and invoices.

The bigger the disaster, the longer the money spending stretches, Zurik said, and the best way is to “stay on it.”

Following the money in Hurricane Katrina recovery, Zurik used a pivot table and found out that the most money was paid to contractors. In one school district, the contractor earned a rocket-high salary, with salaries of $185 per hour instead of the average of $55 to $60.

Following the money, Zurik eventually was able to reveal the discrepancy between earnings of HOV Services and of other construction companies. For tracing the money flow after a disaster, he suggested that the reporters should always keep some datasets in their laptops:

• Check registers — before and after
• Salary and overtime
• Business corporations
• Campaign finance

Data only tells part of the story, Zurik said, and letting data lead you to other documents, such as invoices in the case above, is also helpful.

Yue Yu is a journalism student at the University of Missouri.