/fp-stop_forest_fires_at_any_cost

fp-stop_forest_fires_at_any_cost created by GitHub Classroom

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

CMU Interactive Data Science Final Project

Stop Forest Fires at any Cost

  • Select the URL link to navigate to our project!

Application Overview

The application we built is organized into three main sections. The first section contains an introduction designed to captivate the user. This section consists of an animation of human-caused and nature-caused fires over 37 years with illuminating pie charts that depict the proportion of acres-burned and burn-days caused by humans and nature. Below the animation, the user can also choose to manually progress through the time-series at their own pace with a slider. The introduction concludes with an acres-burned clock and reforestation clock for 2020, demonstrating the estimated acres-burned and reforestation efforts so far this year, as well as the rate of both of these processes each second. The user is able to attempt to “catch-up” to wild-fire deforestation with an input that calculates the reforestation efforts increased by that user input (in millions of acres). The exploration section leads the user through an exploratory data analysis of the environmental and financial impact of wildfires, as well as the added analysis of fire types. The final section displays a predictor model for future acres-burned and suppression cost for the next five years. The user is able to decrease the fires created by both humans and nature in an attempt to change the prediction outcomes.

Work distribution

  • Arpit Kumar: Arpit scraped the data from various pdfs and gathered url links for each of the significant fires to add interest to the project.
  • Sammy Hajomar: Sammy was primarily responsible for the "Exploration" tab on the application. He designed the environmental impact and financial impact visualizations for further wildfire analysis.
  • Joshua Vargas: Joshua was responsible for the Additional Analysis section of the "Exploration" tab. He provided added insight into fire type trends over time.
  • Taylor Sullivan: Taylor was responsible for the "Introduction" tab and captivating the user to progress through the application.
  • Joshua and Taylor: Joshua and Taylor collaborated on the predictor model, completing their code in R Studio. They developed the "Predictions" tab of the application which projects user-caused and nature-caused acres burned and costs of suppression for five years out. This page also offers users the opportunity to interact with the predictions with their own inputs.
  • All project members assisted in the production of project goals and direction, the narrative development, and the project report.

Application Screenshots