Wildfire Data Analysis and Visualization

This repository contains materials for a conference talk on wildfire data analysis and visualization using Python. The talk explores how to work with geospatial wildfire data, clean it, analyze it, and create compelling visualizations.

Key Terminology

Wildfire Terminology

Perimeter
The outer boundary of a wildfire.

Containment
Limiting the spread of a wildfire. The fire is still active. Usually represented as a percentage. 40% contained doesn't mean 40% of the fire is out. It means 40% of the fire's perimeter isn't expected to spread because of a man-made or natural physical barrier (firebreak).

Acreage
A measure of the land area affected by a wildfire.

IRWINID
Unique identifier assigned to each incident record in IRWIN (Integrated Reporting of Wildland-Fire Information). IRWIN is software that allows data to be shared between applications so that there's a single data source for every incident.

Geospatial Terminology

Geospatial Data
Data with a component that relates to a location on Earth. Examples include Google Maps, natural disaster tracking, and maps with locations of train stations.

Point
A single location on Earth.

Polygon
A closed shape on a map.

Coordinate Reference System
Describes where a geometry is located on the Earth's surface. A common reference system is 4326 - World Geodetic System 1984.

Data Source

The National Interagency Fire Center (NIFC) is our primary data source. Worth noting that fire data is updated "slowly". Update speed can depend on the size of the fire and where the fire is. A state like CA with regular wildfires and infrastructure to report on them might give a fire update 3x a day. It is not uncommon to not have an update on a fire for days or even weeks.

Data can be found at: https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-2025-interagency-fire-perimeters-to-date/about

Setup and Usage

1. Clone this repository
2. Install uv if you don't have it already
3. Set up a virtual environment and install dependencies:

   uv venv
   uv pip install -e .

4. Open and run the Jupyter notebook:

   jupyter notebook notebook.ipynb

Visualization Examples

The notebook demonstrates several visualization techniques:

• Basic wildfire perimeter visualization
• Coloring fires by acreage and cost
• Filtering fires by geographic location
• Aggregating fire counts by state