California Wildfire Analysis Project

A geospatial data analysis project for exploring and visualizing California fire perimeters and prescribed burns using GeoPandas, with interactive visualizations via ipywidgets.

Project Overview

This project analyzes wildfire and prescribed burn perimeter data from California's fire database (2010-2024). It provides tools to:

• Load and explore fire perimeter geodata (GDB format)
• Visualize fire locations on interactive base maps
• Filter and analyze fires by year
• Compare prescribed burns vs. active wildfire perimeters

Data

• Source: data/raw/fire24_1.gdb – ESRI Geodatabase containing:
  • firep24_1: California wildfire perimeters
  • rxburn24_1: Prescribed burn perimeters
• Coordinate System: EPSG 3310 (California Albers projection)
• CAL FIRE Fire Perimeters: https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program/fire-perimeters
• US Census California Map Data https://www2.census.gov/geo/tiger/TIGER2023/STATE/

Project Structure

ca-wildfire-project/
├── README.md                          # This file
├── requirements.txt                   # Python dependencies
├── data/
│   ├── raw/                          # Original fire perimeter GDB
│   │   └── fire24_1.gdb/
│   └── processed/                    # Processed/derived datasets
└── notebooks/
    └── 01_explore_fire_perimeters.ipynb  # Main analysis notebook

Installation

Prerequisites

• Python 3.11+ (recommended 3.14)
• pip or conda package manager

Setup (Recommended: Conda)

Using conda-forge ensures proper installation of geospatial dependencies (especially GDAL/Fiona on Windows):

conda create -n ca-wildfire python=3.11 geopandas -c conda-forge
conda activate ca-wildfire
pip install -r requirements.txt

Setup (Alternative: pip)

python -m venv venv
source venv/Scripts/activate  # On Windows
pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

Note: On Windows, if pip fails to build geopandas/fiona, use the conda approach above.

Dependencies

• geopandas – Geospatial vector data manipulation
• pandas – Data manipulation and analysis
• shapely – Geometric operations
• pyogrio – Vector I/O backend
• matplotlib – Visualization
• contextily – Basemap tiles for interactive maps
• ipywidgets – Interactive controls in Jupyter notebooks
• jupyter & ipykernel – Notebook environment
• requests – HTTP library for data fetching

See requirements.txt for pinned versions.

Usage

Launch Jupyter Notebook

jupyter notebook notebooks/01_explore_fire_perimeters.ipynb

Notebook Walkthrough

The main notebook (01_explore_fire_perimeters.ipynb) contains:

1. Import Libraries – Load geospatial and visualization packages
2. Load Data – Read fire perimeter GDB layer into a GeoDataFrame
3. Exploratory Analysis – Inspect shape, CRS, and columns
4. Static Visualization – Plot fire perimeters on base map (2010–2024)
5. Interactive Analysis – Use ipywidgets slider to filter and plot fires by year

Example: Load and Explore Fire Data

import geopandas as gpd

# Load wildfire perimeters
gdf = gpd.read_file("data/raw/fire24_1.gdb", layer="firep24_1")
print(gdf.shape)          # (n_fires, n_columns)
print(gdf.crs)            # EPSG:3310
print(gdf.columns.tolist())

# Filter 2024 fires
gdf_2024 = gdf[gdf['YEAR_'] == 2024]
gdf_2024.plot()

Example: Plot Recent Fires on Basemap

import contextily as ctx
import matplotlib.pyplot as plt

# Filter and reproject to Web Mercator
gdf_recent = gdf[gdf['YEAR_'] >= 2010].to_crs(epsg=3857)

# Plot
fig, ax = plt.subplots(figsize=(10, 10))
gdf_recent.plot(ax=ax, alpha=0.5, color="red")
ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik)
plt.title("California Wildfires (2010-2024)")
plt.show()

Key Columns in Fire Perimeter Data

Column	Description
YEAR_	Year of fire
geometry	Fire perimeter polygon (EPSG:3310)
(others)	Incident name, acreage, discovery date, etc.

Refer to the GDB metadata for complete column definitions.

Troubleshooting

ImportError: No module named 'fiona'

Issue: Fiona (GDAL I/O backend) failed to install.

Solution: Use conda instead of pip:

conda create -n ca-wildfire python=3.11 geopandas -c conda-forge

Conda provides pre-compiled binaries for GDAL/Fiona, avoiding build failures on Windows.

CRS or Layer Name Errors

• Verify GDB path: data/raw/fire24_1.gdb
• Check layer names: firep24_1 (wildfires) or rxburn24_1 (prescribed burns)
• Confirm EPSG 3310 is recognized: gdf.crs should show EPSG:3310

References

• GeoPandas Documentation
• Contextily (Basemaps)
• ipywidgets (Interactive Controls)
• EPSG:3310 (California Albers)