Predictive ignition models built on transportation-network proximity, urban-fringe density gradients, and seasonal fire history — turning anthropogenic geometry into actionable wildfire risk maps.
Overview
This project focused on developing predictive wildfire ignition models using spatial interaction frameworks that integrate transportation networks, urban density gradients, and historical fire occurrence data. The work shifted attention from purely climatic drivers (which dominate the literature) toward anthropogenic ignition signals — the places where people, roads, and rural-urban transition zones intersect with combustible fuel loads.
Seasonal Wildfire Trend Analysis
Historical wildfire data was analyzed across seasonal periods to identify long-term ignition patterns and spatial clustering behavior. The analysis was partitioned into two seasonal windows:
- Summer: April through September
- Winter: October through March
This separation surfaces ignition behavior that gets washed out in annual averages — winter fires cluster very differently from summer fires, and a single model trained on both seasons loses both signals.
Spatial Wildfire Risk Distribution
- Winter fires concentrated around cities and road corridors
- High-risk and low-risk zones showed strong spatial separation
- Summer wildfire events were more evenly distributed
- Historic wildfire distributions validated seasonal prediction models
Road Proximity & Anthropogenic Ignition Modeling
Wildfire occurrence showed a strong inverse relationship with distance from roads. Historic wildfire events across road buffers were used to fit an exponential decay function — ignition probability falls off sharply as distance from the nearest road increases. This is the clearest single anthropogenic predictor in the dataset and quantifies how road-network access concentrates ignition risk.
Urban Influence Analysis
- Lower wildfire frequency near dense city centers
- Higher wildfire occurrence in urban fringe zones — the wildland-urban interface
- Reduced wildfire activity farther from populated areas
The non-monotonic relationship with urban density is the key finding: it isn't more people that drives ignition, it's the transition between built and unbuilt land.
Key Outcomes
- Development of wildfire ignition probability models grounded in road and urban geometry
- Identification of wildfire-prone transportation corridors for targeted prevention spending
- Seasonal wildfire risk characterization that separates winter vs summer dynamics
- Improved understanding of anthropogenic wildfire drivers in mixed rural-urban landscapes
- Scalable framework for wildfire risk assessment — same code path, new region, new map