Subscribe To Our Newsletter

Get tips and tools to tell your data story better.

No, thanks

 In Case Studies, Current Events, Experts

Fire season has begun on North America’s west coast and already, news feeds are with stories of wildfires raging as emergency responders struggle to contain and extinguish them. Unless you’ve been hiding under a rock, you probably knew all that already. But did you know that collaborative data use is also being used to improve fire safety?

Using shared data (which you probably also know is an agenda I continue to push for social sector organizations of all sizes) isn’t just about getting data on populations. Data analysis can provide valuable insight and answers into all kinds of problems.

Because we at Datassist love to highlight collaborative data use wherever it’s being used to make a difference, I’d like to share the story of how DataKind and the American Red Cross are partnering to improve fire safety across the US.

Identifying Risk

As DataKind explains on their blog, around 25,000 Americans are injured or killed in fires each year. Experts estimate that up to 60% of these could be prevented with basic smoke alarm installation. Local fire stations and the American Red Cross cooperate to help ensure more homes had working smoke alarms, but often struggle to identify where those alarms would make the biggest difference.

Enter DataKind — and collaborative data use.

Using open data from the American Community Survey, the American Housing Survey, the National Fire Incident Reporting System, and Red Cross’s own internal home fire preparedness data, DataKind and Red Cross team worked together to identify the areas most at risk of house fires in cities and towns across the US.

The conclusion?

Residents living in the south or west sides of a city — any city — are at a significantly higher risk of being injured or killed in a house fire than their counterparts in other neighbourhoods. The difference is attributable to a number of factors:

  • Higher poverty rates in these neighbourhoods, which means
  • Homes are often underheated, poorly heated, old and poorly wired, and
  • Crime rates are higher – leading residents to block windows for security, trapping them inside in case of fire

Firefighting professionals corroborated the findings:

“A fire house on the north side can go for weeks without having a real fire. A fire house on the south side might end up with two or three or four fires a week. It’s quite an imbalance.”

Larry Langford, Chicago Fire Department

Making Safety a Priority

Working together with the Enigma smoke signals project, the team developed an interactive map that identified neighbourhoods that were at highest risk of house fire injuries and fatalities.

Collaborative data use allowed the team to create a map of high-risk neighbourhoods.This allowed the Red Cross and local governments and first responders to target their efforts where they were most needed, providing:

  • Fire safety education programming
  • Free smoke alarms
  • Better resource allocation

By mid-2016, just one year after DataKind and the American Red Cross initially partnered on this collaborative data use project, volunteers had installed over 400,000 fire alarms in over 175,000 homes. The Red Cross has pledged to install 2.5 million smoke alarms in communities where they are most urgently needed.

Can Collaborative Data Use Help You?

Has DataKind’s work with the American Red Cross inspired you? Do you have an idea for a collaborative data use project that your organization could use to make a difference?

If you have questions or need help getting started, the Datassist team is here to help. We specialize in helping nonprofits, governments, and social sector organizations make connections between available data and the problems at hand. We’ll help you find, collect, analyze, and communicate data in a way that engages and educates. Get in touch with our team today.

Recommended Posts

Start typing and press Enter to search

Learn how to make a difference when you use social identity data.Weighting helps improve survey results without the need for costly analysis.