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https://github.com/invictusaman/analyzing-wildfire-activities-in--australia

This project analyzes wildfire data using Python and libraries like Pandas, Matplotlib, Folium and Seaborn to visualize trends, regional differences, and fire characteristics from 2005 onwards.
https://github.com/invictusaman/analyzing-wildfire-activities-in--australia

folium jupyter-notebook matplotlib python quarto report seaborn

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This project analyzes wildfire data using Python and libraries like Pandas, Matplotlib, Folium and Seaborn to visualize trends, regional differences, and fire characteristics from 2005 onwards.

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README 

        
# Analysis of Wildfire Activities in Australia



## Overview



This project presents an in-depth analysis of historical wildfire data in Australia. The aim is to uncover patterns and trends in fire activities across different regions, utilizing various data visualization techniques to explore the temporal and spatial distribution of these events.



## Table of Contents



- [Introduction](#introduction)

- [Methodology](#methodology)

- [Data Preparation](#data-pre)

- [Analysis and Findings](#analysis-and-findings)

  - [Temporal Trends in Fire Activity](#temporal-trends-in-fire-activity)

  - [Regional Analysis](#regional-analysis)

  - [Fire Characteristics Analysis](#fireistics-analysis)

  - [Geographical Visualization](#ographical-visualization)

- [Conclusion](#conclusion)

- [References](#)

- [Appendix](#appendix)

- [Visit my Portfolio](#visit-my-portfolio-portfolio-link)



## Introduction



Wildfires are a significant environmental concern in Australia, widespread, economic, and social impacts. This study uses a comprehensive dataset of fire activities from 2005 onwards to provide insights into the nature and distribution of these events across the continent.



## Methodology



### Data Source



The analysis is based on the Historical Wildfires dataset, which contains detailed information on fire activities in Australia, including variables such as estimated fire area, fire brightness, and confidence levels.



### Analytical Approach



The analysis employs a combination of statistical methods and data visualization techniques using Python libraries such as Pandas, Matplotlib, Seaborn, and Folium for data manipulation and visualization.



## Data Preparation



The data is loaded and preprocessed to derive essential insights. The `Date` is converted to a datetime format, and additional `Year` `Month` are for further analysis.



## Analysis and Findings



### Temporal Trends in Fire Activity



The analysis reveals significant trends in fire activity over the years, with notable increases observed during specific periods.



### Regional Analysis



Regional differences in fire brightness and incident occurrences are explored, highlighting areas the highest fire activity.



### Fire Characteristics Analysis



The correlation between fire radiative power and confidence levels is examined, providing insights into fire detection accuracy.



### Geographical Visualization



A map visualization is created to display the geographical distribution of wildfire incidents across various regions in Australia.



## Conclusion



This comprehensive analysis highlights significant temporal and spatial patterns in wildfire activities in Australia. Key findings include:



- An increase in fire activity between 2010 and 2012.

- Seasonal patterns in fire occurrences.

- Regional variations in fire intensity and frequency.

- A positive correlation between fire detection confidence and estimated fire radiative power.



These insights are valuable for fire management strategies and importance of continued monitoring and analysis of wildfire in Australia.



## References



1. NASA FIRMS. (n.d.). [MODIS/VIIRS Active Fire and Hotspot Data](https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/c6-mcd14dl).



## Appendix



Additional visualizations and detailed statistical analyses are available upon request.



## Visit my Portfolio [Portfolio Link](https://amanbhattarai.com.np)