Forest Fires Data Visualization & Web Application (Project 3)

Description of Project

This project analyzes wildfire incidents using structured datasets, SQL queries, and visualizations. It includes a cleaned dataset of fire records, SQL queries for data extraction, a Jupyter Notebook for data analysis and visualization, and an interactive web application map displaying fire data.

Data Source

Department of Agriculture, United States Forest Service (USFS)

Links to Source Data

https://data-usfs.hub.arcgis.com/datasets/usfs::national-usfs-fire-occurrence-point-feature-layer/about?form=MG0AV3

Link to Fire Occurrences Metadata

https://www.fs.usda.gov/r3/gis/gisdata/FireOccurrence.html#2

Features

Dataset Analysis: Structured fire data with over 15,000 records.

SQL Queries: Predefined queries to analyze fire trends and causes.

Visualizations: Interactive graphs generated using Python.

Web Application: Flask API serving wildfire data.

Interactive Map: Leaflet.js map visualizing fire data by decade.

Google Slides

Installation

To install and set up our project, these steps must be completed prior to running:

Clone the repository

git clone https://github.com/tohassan21/forest-fires

Navigate to the project directory

cd .../forest-fires

Install dependencies

pip install -r requirements.txt # For Python dependencies

Usage

Troubleshooting: Due to size of the clean_fire.csv file, may nee to download it to the desktop directly from the repository first. If downloading on a Mac, the file may download as a .txt file. To reformat to a .csv file, enter the following line in a terminal window in the repository folder:

mv clean_fire.txt to clean_fire.csv

Now you should be able to continue to the following step.

Run all of the cells in the clean_fire_visuals.ipynb file in jupyter notebook.

This file pulls the data from the clean_fire.csv file and exports 3 visualizations:

1. fires_per_year_by_sizeclass.png
2. total_fires_by_sizeclass.png
3. total_fires_per_year.png

Running SQL Queries

Use the fire_queries.sql file to run predefined queries on fire data. We used Postgres (pgAdmin 4).

Running Jupyter Notebook

To analyze and visualize data:

jupyter notebook clean_fire_visuals.ipynb

Running Flask API

To start the Flask web application:

python app.py

The API will be available at http://127.0.0.1:5000/data.

Running the Interactive Map

The map is powered by Leaflet.js and fetches data from the Flask API. Open the relevant HTML file in a browser to view fire incidents.

Dataset Details (clean_fire.csv)

15,322 wildfire records

20 columns, including:

LONGITUDE, LATITUDE: Fire location

FIREYEAR: Year of occurrence

TOTALACRES: Total acres burned

STATCAUSE: Cause of the fire

Some missing values in GLOBALID, DISCOVERYDATETIME, and OWNERAGENCY.

SQL Queries Overview (fire_queries.sql)

• Fetches fires by a specific year.

• Counts fires per decade.

• Summarizes total acres burned by decade.

• Investigates fire causes.

Database Schema (schema.sql)

Defines the fires_data table structure.

Includes:

FIRENAME, FIREYEAR, STATCAUSE, TOTALACRES.

DISCOVERYDATETIME, FIREOUTDATETIME for tracking fire duration.

• Uses DOUBLE PRECISION for latitude/longitude.

• Visualization Notebook (clean_fire_visuals.ipynb)

• Uses pandas, numpy, matplotlib for data processing and visualization.

• Loads clean_fire.csv and generates insights.

Web Application (app.py)

• Flask API that serves wildfire data.

• Uses SQLAlchemy to connect to a PostgreSQL database (forest_fires_db).

• Defines a fires_data model for querying fire records.

• Provides an endpoint (/data) to return fire data as JSON.

Interactive Map (fire.js)

• Uses Leaflet.js to display fire locations on an interactive map.

• Allows filtering by decade using a dropdown.

• Fetches fire data from the Flask API.

• Uses a color-coded legend based on acres burned.

Styling (style.css)

• Styles map layout, dropdown menu, and legend.

• Ensures responsive design for interactive elements.

• Positions UI components effectively for better usability.

Contributors

Lannon Grady

Toka Hassan

Toni Makakoa

Daniel Simonson

Chad Hillman