Skip to main content

Calculate environmental risk metrics for a given polygon

Project description

Environmental Risk Metrics

License Python Version

Calculate environmental risk metrics for a given polygon using advanced geospatial and data processing tools.

Table of Contents

Features

  • NDVI Calculation: Compute Normalized Difference Vegetation Index (NDVI) values for specified polygons.
  • Sentinel-2 Integration: Load and process Sentinel-2 satellite data for various spectral bands.
  • Interactive Notebooks: Utilize Jupyter notebooks for data analysis and visualization.
  • Comprehensive Soil Data: Incorporates detailed soil type information for accurate risk assessment.
  • Protected Areas: Get nearest Ramsar protected sites for a given geometry
  • Social Indices: Get Global Witness data for the countries containing or intersecting the given geometry
  • Endangered Species: Get endangered species data for the countries containing or intersecting the given geometry
  • Climate Data: Get climate data for the countries containing or intersecting the given geometry

Getting Started

Prerequisites

  • Python 3.12+
  • Git

Installation

  1. Clone the Repository

    pip install environmental-risk-metrics
    

Examples

Using Jupyter Notebooks

Interactive analysis can be performed using the provided Jupyter notebooks.

  1. Navigate to the Notebooks Directory

    cd notebooks
    
  2. Launch Jupyter Notebook

    jupyter notebook
    
  3. Open and Run the Desired Notebook

    For example, open 01 - all_metrics.ipynb to explore environmental risk metrics calculations.

Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the Repository

  2. Create a Feature Branch

    git checkout -b feature/YourFeature
    
  3. Commit Your Changes

    git commit -m "Add some feature"
    
  4. Push to the Branch

    git push origin feature/YourFeature
    
  5. Open a Pull Request

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

Developed by Thimm. For any inquiries or feedback, please reach out via email.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

environmental_risk_metrics-0.1.18.tar.gz (5.7 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

environmental_risk_metrics-0.1.18-py3-none-any.whl (5.5 MB view details)

Uploaded Python 3

File details

Details for the file environmental_risk_metrics-0.1.18.tar.gz.

File metadata

File hashes

Hashes for environmental_risk_metrics-0.1.18.tar.gz
Algorithm Hash digest
SHA256 b654faa1276325393316aaf0ccc04e6e23a9c2f784247a3a6990b0eb047a0497
MD5 f8a25090628b44b075f4be8eae0e5ff6
BLAKE2b-256 e00a30bce81cb123f5f88b0091d623da92bfcc9db02c757f519831628a8500c8

See more details on using hashes here.

File details

Details for the file environmental_risk_metrics-0.1.18-py3-none-any.whl.

File metadata

File hashes

Hashes for environmental_risk_metrics-0.1.18-py3-none-any.whl
Algorithm Hash digest
SHA256 09617dcf57e8eabaf874875c2080eb41d727c37f05788442475d7efd9ad9a145
MD5 9127ee68dafc9a8a10a2ff55b51aa824
BLAKE2b-256 9fb5a52c400093ba46ae264fe73ec3bb5e41e9ca00eaffa81201e3176ceadcaf

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page