Understanding Descriptive Analytics in Bushfire Mitigation
FiSci’s Approach to Fire Risk Insight
When it comes to bushfire risk, the first and most foundational step in building an effective mitigation plan is understanding the landscape as it is today — and how it has behaved in the past.
At FiSci, we call this layer Descriptive Analytics.
What Are Descriptive Analytics?
Descriptive analytics are the data layers that help land managers, infrastructure owners, and emergency planners interpret the conditions of their landscape. This includes both static and historical datasets, displayed through spatial visualisations that bring complex information to life.
In FiSci’s Mitigate platform, our descriptive analytics include:
Vegetation communities – Different types of vegetation carry different fuel loads, burn behaviours, and recovery times. Mapping these communities is a first step in understanding risk.
Fire history – Where have fires occurred in the past? How frequently do they return? Understanding ignition density and burn patterns helps identify high-risk zones.
Fuel load – Built-up ground fuels and ladder fuels are a key driver of fire intensity and spread. Tracking this helps identify areas that require active treatment.
Topography and geography – Elevation, slope, and aspect all influence how a fire will behave on the landscape. Understanding these patterns provides a spatial context for fire planning.
Why It Matters
Descriptive analytics serve as the baseline for everything that follows. They help users:
Identify high-risk areas and asset exposure
Prioritise areas for fuel treatment
Understand changes over time in landscape condition
Communicate risk clearly with stakeholders
But there’s a caveat.
The Risk of Human Bias
Descriptive data is powerful — but when used in isolation, it carries the greatest risk of subjective interpretation. Without predictive or prescriptive modelling, users are left to make assumptions or lean on past experience when assessing how dangerous a fuel load is, or how close a fire might get to key assets.
This can lead to overreliance on certain datasets, or even missed signals in others.
That’s why FiSci’s approach doesn’t stop at descriptive analytics. It forms just one layer of a more complete decision-making framework.
Laying the Groundwork for Deeper Insight
By providing clean, well-structured, and transparent descriptive data, we enable our users to confidently move into the next stages: predictive and prescriptive analytics.
Those deeper layers allow us to simulate fire behaviour, model risk scenarios, and stress-test treatment strategies — which we’ll explore in the next parts of this series.
But for now, the message is simple: if you want to make smarter fire management decisions, start by understanding what’s already there.
Want to learn more about how Mitigate visualises and manages landscape risk? Contact our team or book a demo.
