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-0.1.9.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-0.1.9-py3-none-any.whl (5.5 MB view details)

Uploaded Python 3

File details

Details for the file environmental_risk_metrics-0.1.9.tar.gz.

File metadata

File hashes

Hashes for environmental_risk_metrics-0.1.9.tar.gz
Algorithm Hash digest
SHA256 1722dbd1cce9bfd0622b7e90ec74cbc914e0c2e6a080a3ab060bbfef40a12d1f
MD5 d0b6f5027064c6983a6beacd541d90fc
BLAKE2b-256 989dea7b0a1e4cf241ee972e85835cc8efa865c516d0f87639c7d5b10cccdeab

See more details on using hashes here.

File details

Details for the file environmental_risk_metrics-0.1.9-py3-none-any.whl.

File metadata

File hashes

Hashes for environmental_risk_metrics-0.1.9-py3-none-any.whl
Algorithm Hash digest
SHA256 cd7af483dcb73c255e872520113570a5d11734df8e7bbfdb3999a66fa188db66
MD5 321977a864fcfd9921cad35998bc7cd4
BLAKE2b-256 ff2a218e2e3569921c011420b57eadb283a304449c997116232137607a372525

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