Paper 29

Modeling and Forecasting Wildfire Trends in Colorado, Montana, Utah, and Wyoming: A Spatio-Temporal Approach with R

September 24, 2025: 1:00 PM - 1:30 PM

Careers, Training, and Education (CTE), Breakout Room #7

Authors	Abstract
Princess Tagoe	Wildfires pose serious threats to ecosystems, communities, and economies across the United States most especially the Western part. This research investigates historical wildfire trends and forecasts future events in Colorado, Montana, Utah, and Wyoming using spatio-temporal analysis. Leveraging data from the U.S. Forest Service Fire Occurrence Database (1992–2020), we used R for data preparation, pattern detection, and predictive modeling. The study examines how wildfire occurrences evolve over time and space to identify key trends and potential hotspots. These insights support disaster preparedness, resource allocation, and risk mitigation efforts. By demonstrating the practical application of R in environmental analytics, this project highlights how data-driven approaches can inform effective wildfire management strategies.

Princess Tagoe

Wildfires pose serious threats to ecosystems, communities, and economies across the United States most especially the Western part. This research investigates historical wildfire trends and forecasts future events in Colorado, Montana, Utah, and Wyoming using spatio-temporal analysis. Leveraging data from the U.S. Forest Service Fire Occurrence Database (1992–2020), we used R for data preparation, pattern detection, and predictive modeling. The study examines how wildfire occurrences evolve over time and space to identify key trends and potential hotspots. These insights support disaster preparedness, resource allocation, and risk mitigation efforts. By demonstrating the practical application of R in environmental analytics, this project highlights how data-driven approaches can inform effective wildfire management strategies.