COGS219 Final Project

Part I: Frequency Analysis

It analyzes the frequency and trends of wildfires in California using data from January 2000 through March 2022. It provides insights into wildfire occurrences at yearly, monthly, and detailed year-month levels, visually highlighting patterns and trends over the analyzed period.

Part II: Question 1: Which factors have the greatest influence on fire frequency?

In addition to frequency analysis, this project investigates the relationship between environmental conditions and wildfire frequency. Using meteorological data, it identifies key environmental factors contributing to fire outbreaks and evaluates their significance using statistical analysis and machine learning models.

Part III: Question 2: Can environmental factors help predict the frequency of fires in a given month?

We first analyzed wildfire frequency across different months and found that environmental factors significantly influence fire frequency. However, are environmental factors the only thing we should consider? While environmental conditions like temperature, humidity, and wind speed have a strong correlation with fire frequency, other external factors may also contribute.

Package Required

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

Steps ro Run

1. Download the Git repository.

2. Unzip the frequency_data and environmental_data file.

3. Set up the Python environment.

4. Run the script to generate the visualizations.