🔥 Mapping the Firelines: A Data-Driven Exploration of U.S. Wildfires (2014–2024)

Introduction

Wildfires are no longer seasonal outliers — they’ve become structural threats. From record-breaking burns in California to steadily rising fire counts in Colorado and Texas, the U.S. wildfire crisis demands data-informed solutions.

For our Python team project, we built a comprehensive exploratory analysis of wildfire incidents across the U.S. from 2014 to 2024. Sourcing data from NOAA’s Storm Events Database, our team mapped patterns, corrected anomalies, and forecasted future risks using both spatial and time-series techniques.

This blog shares our approach, insights, and the analytical pipeline we developed to support more strategic wildfire preparedness.

🔍 Sourcing & Cleaning the Data

Data Origin

Our core dataset came from the National Oceanic and Atmospheric Administration (NOAA), capturing over a decade of wildfire events across the U.S., complete with:

• Event start/end times
• Location and zone identifiers
• Property damage
• Injuries, deaths, and duration

The Location Challenge

Each wildfire was tagged to a Public Forecast Zone (CZ_FIPS) — but these zones change over time. To map geographic centroids (lat/lon), we used Zone-County Correlation Files from 7 historical periods via the Wayback Machine, patching missing zone definitions that had evolved since the fire was reported.

Great for finding those pesky removed datasets!

SQL Preprocessing Pipeline

We joined multiple datasets in MySQL using:

• LEFT JOIN for location data (Step 1),
• COALESCE() to return the most recent non-null zone match (Step 2), and
• GROUP BY to merge duplicate entries for multi-month fires (Step 3).

Duration processing also posed a challenge — since MySQL’s TIME data type maxes out at ~839 hours, we stored duration in seconds since epoch, calculating fire length in days + remainder time.

🔥 Key Findings & Visualizations

1. Wildfires by Geography

• Wildfire density is highest in California, Colorado, Texas, and the Pacific Northwest
• Longest durations cluster in Western states, especially California and Oregon

2. Wildfire Seasonality

• Q3 (summer) consistently sees the most activity due to dry vegetation, heat, and wind
• Q1 (winter) is less active nationally — except in Texas, which sees early-season spikes

3. Trends Over Time

• A steady rise from 2014 to 2018, a surprising drop in 2019 (due to higher snowpack and moisture), then a spike again in 2020
• Wildfire frequency remains consistently high post-2020

🌲 State-by-State Analysis

• California: Dominates in fire count and duration. 2018’s Camp Fire remains one of the most devastating.
• Texas: Fewer long-duration fires, but frequent seasonal flare-ups.
• Washington/Oregon: Forest management practices (like planting dense, fire-prone tree species) amplify risk.
• Colorado: Fires are growing in scale and intensity — climate change and drought are key drivers.

💡 Strategic Takeaways

For Emergency Planners:

• Use Q2/Q1 forecasts to prepare equipment, allocate funding, and train responders ahead of peak fire seasons

For Policymakers:

• Support wildfire mitigation funding in high-risk areas (e.g. forest thinning, controlled burns)
• Consider using forecast data to influence zoning, development, and evacuation policies

For Insurers:

• Adjust pricing models to account for climate-driven frequency and intensity shifts

For the Public:

• Emphasize evacuation preparedness and community awareness, especially in Q3-prone regions

🧠 Lessons from the Project

We faced technical hurdles like database limitations (TIME type), evolving zone definitions, and multi-record events. But the most rewarding part was designing a flexible data pipeline that could accommodate historical changes and produce accurate spatial joins.

We also experimented with:

• Cartopy for geographic visualizations
• Seaborn and Matplotlib for temporal trends
• Sklearn for normalization in colormap displays

AI tools also helped us debug and tune these visualizations, especially with Cartopy’s documentation quirks.

📎 Citations & Acknowledgments

• NOAA Storm Events Database
• NOAA Zone-County Correlation Files
• Wayback Machine
• Western Fire Chiefs Association
• Colorado Department of Public Safety
• Washington State Department of Ecology

Special thanks to the MSBA Purple Team 2 (Huy Tang, Smita Kulkarni, Peggy Chen, Julia Nichols) — this project would not have come together without everyone’s collaboration and insights.

👋 Final Thoughts

Data can’t stop a wildfire. But it can shape where, when, and how we act before the next one starts.

If you’re working in emergency response, environmental planning, or even data journalism — this kind of analysis is a powerful foundation for decision-making.

Let’s connect if you’d like to talk about spatial data, forecasting, or building full-stack analytical pipelines for real-world problems!

Further links are as follows:

• See the full presentation here.
• See the full report here.
• See the Tableau visualization here.