A repository of resources for the CFD Challenge (Sponsor: City of Charlottesville Fire Department)
From the CID 2018 Challenge video:
The Charlottesville Fire Department regularly assesses fire risks to
life and property. With dozens of economic, building stock,
demographic, and lifestyle data sets relevant to assessing fire risk
available, creating a model to integrate myriad data into a
parcel-level assessment will allow dynamic and ongoing monitoring of
shifting fire risks in the community. How might we build a model
that integrates current and novel data sources into actionable fire
risk insights regularly available to local experts in the fire
service?
Two locations, Atlanta and
Pittsburg,
have successfully built and employed data-driven risk assessment
models in their daily operations. Other examples that use machine
learning to model fire risk are
here
and here.
The primary challenge can be divided into three parts, each
of which are described more fully below.
Predict which structures in Charlottesville are most at risk for a
fire event. This will involve joining public data from the
Charlottesville Open Data Portal with historical data on fire incidents
from the CFD.
CFD assigns each fire incidence a severity level, which is useful for
determining an appropriate response. Predict the likely severity level
of a fire event for structures around Charlottesville.
How might CFD make information about fire risk available and useful
for members of the Charlottesville community? One idea is an
interactive website or app that allows people to see the fire risk
assigned to their home or place of employment and offers suggestions
on what they can do to lower it. What would this look like? What else
might be useful?
If you’re interested in staying (or getting) involved with this
challenge beyond 6/2, please fill out this
form with your contact
information, level of interest, and area of interest.