This repository contains the code and documentation for a project exploring the relationship between climate change, land-use practices, and natural disasters. The study emphasizes Brazil while leveraging global datasets to provide insights into disaster trends and environmental factors.
- Objective: Analyze trends and patterns of natural disasters in relation to environmental factors.
- Datasets:
- FAO Land Cover and Forest Area (1992-2020)
- EM-DAT Disaster Database (2023)
- Techniques:
- Data cleaning and normalization
- Exploratory data analysis (descriptive statistics and visualizations)
- Predictive modeling using machine learning
- Regional analysis focusing on Brazil
├── data/
│ ├── raw/ # Raw datasets
│ ├── interim/ # Intermediate datasets
│ └── processed/ # Final processed datasets
├── main.py # Main script for data processing and analysis
├── ANALISE_EXPLORATORIA.ipynb # Jupyter Notebook for analysis and visualizations
├── Exploratory Data Analysis of Climate and Land-Use Data.pdf # Final report
└── README.md # Project documentation
- Python 3.9+
- Libraries: Dask, Pandas, NumPy, Matplotlib, Seaborn
-
Clone this repository:
git clone https://github.com/your-username/your-repository.git cd your-repository -
Install the dependencies:
pip install -r requirements.txt
-
Run the main script:
python main.py
-
Explore the results in the output files:
- Normalized data:
data/interim/dataConcat_silver.csv - Processed data:
data/processed/dataConcat_gold.csv
- Normalized data:
Open the ANALISE_EXPLORATORIA.ipynb file to explore the analysis and visualizations interactively.
- Increasing Disaster Frequency: A clear trend of increasing natural disasters was observed, with a rate of 4.09 events/year (R²=0.37, p=0.0003).
- Brazil Focus: The analysis identified deforestation rates and forest area as critical predictors for temperature changes.
- Best Predictive Model: Random Forest achieved the best R² score with minimal prediction error.
The findings emphasize the importance of regional environmental policies and climate resilience strategies. Data science plays a crucial role in deriving actionable insights for sustainable decision-making.
- @Vigrel - Vinicius Grando Eller
- @FabricioNL - Fabricio Neri Lima
- @isabellemm - Isabelle Moschini Murollo
Based on the tech stack, the following workflows are recommended:
- Django: Build and test a Django project.
- Python Package using Anaconda: Create and test a Python package with Anaconda.
- Python Package: Create and test a Python package on multiple versions.