Prioritizing Inspections Based on Fire Risk
The mission of the Chattanooga Fire Department (CFD) is to protect life, property, and community resources through prevention, preparation, response, and mitigation. As part of that mission, CFD conducts regular building inspections to determine if applicable fire code regulations are being followed and to ensure safety in the event of a fire.
Currently, it is not possible to inspect all commercial properties on an annual basis, so choosing which properties to inspect is left to the fire prevention bureau. To determine which properties to inspect, the first considerations are 1) looking at what is required by city code and 2) weighing CFD's priorities. Many other variables also factor into where inspections are performed after the first two criteria are considered.
Using a data-driven variable, such as a fire risk score, would help determine where to apply resources towards additional inspections. A fire risk score:
collects data about an address, including things like the number of non-fire emergency calls to that
looks at where fire events have occurred in the past
attempts to determine if there is any correlation between the address data and fire events
assigns a fire risk score to properties based on the relevant address data
More about our fire risk model
The model is based on the assumption that fires do not occur in random locations, and that certain information about each property can tell us something about the likelihood of future fires.
We first determine where fires have occurred between 2015 and 2018. For this model a fire was a call with a generic incident code of 100. Some fires that were not building or parcel related were removed based on their incident code (code 131 'passenger vehicle fire,' code 136 'self-propelled motor home or recreational vehicle fire,' code 137 'camper or recreational vehicle (RV) fire,' and code 138 'off-road vehicle or heavy equipment fire').
To determine what properties looked like before a fire event, we first look at what non-fire calls were made to an address in the two years before a fire, based on the code of those calls. For addresses without any fires, the model looks at what occurred in the previous two years (2017 and 2018). The model currently uses the following non-fire codes:
745 Alarm system activation no fire unintentional
743 Smoke detector activation no fire
651 Smoke scare odor of smoke
522 Water or steam leak
531 Smoke or odor removal
412 Gas leak (natural gas or LPG)
We also use parcel information like the parcel size and value to look for information that might correlate with fires.
Once the dataset is ready, we use a machine learning algorithm to look for correlations between address data and the prevalence of fires. We can use that same machine learning algorithm to assign a fire risk score to parcels in 2019 based on what happened at that parcel in 2017 and 2018. Finally, the fire risk score generated for 2019 can be compared to what actually occurred in 2019 to see how well the algorithm performed.