How big data is changing the ways we fight fires
Big data is changing nearly every industry, so it is not surprising that it has begun to play a role in fighting fires. Here is how fire departments and other groups are using big data to help prevent, prepare for and put out flames.
One of the main ways we can use big data to fight fires is by using analytics to predict where they are most likely to occur. Officials can use this information to decide which buildings to inspect first.
In Atlanta, for example, the fire department worked with analysts from Data Science for Social Good to find commercial buildings that were missing from its inspection list. They then used an algorithm to determine which attributes of a structure were the best predictors of fire risk.
New Orleans has also used data to determine which homes to distribute free smoke detectors to. Data scientists developed a tool that could predict which city blocks had the highest fire risk and were the most likely not to have smoke detectors. The device, called Smoke Signals, is now also available to other cities.
New York City has become perhaps the most famous example of using big data to help prevent fires. The city’s fire department maintains a database that includes 60 factors related to buildings’ fire risk. An algorithm called FireCast ranks the structures according to fire risk, which helps determine which ones get inspected first. The tool helps ensure that the most at-risk buildings get inspected and eases the fire department’s workload.
Some factors that such a system might consider are the age of the buildings, how it is used and the results of fire and flammability testing. For example, the New York City Fire Department found that older buildings, those with active tax liens and those with ongoing foreclosure proceedings are at a higher risk.
Big data can similarly be used to help fight wildfires. The Los Angeles Fire Department uses a tool called WIFIRE to predict where wildfires will go next. The web-based platform uses signal processing, data assimilation, modeling and visualization to merge satellite imagery, footage from cameras and data from sensors to create a picture of a fire and the conditions that surround it. It can then use current and historical data to predict what will happen next.
During the Thomas Fire in California in 2017, the system updated its information every 15 minutes. Firefighting teams used the system to monitor the fire and prepare for what it may do next. The public also used the app to stay up to date and determine whether they may need to evacuate.
