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-0.1.12.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-0.1.12-py3-none-any.whl (5.5 MB view details)

Uploaded Python 3

File details

Details for the file environmental_risk_metrics-0.1.12.tar.gz.

File metadata

File hashes

Hashes for environmental_risk_metrics-0.1.12.tar.gz
Algorithm Hash digest
SHA256 c21977bd8db426222da1c0cd2a8ab749cf38d18938fcc55431cb6171ba5e4469
MD5 77ebe152aa43d3e1c7ff8b5564a68679
BLAKE2b-256 09e2e3201291b79cd46db92de87e0cfbd4931fa1247706c36bb7eb5e765cd549

See more details on using hashes here.

File details

Details for the file environmental_risk_metrics-0.1.12-py3-none-any.whl.

File metadata

File hashes

Hashes for environmental_risk_metrics-0.1.12-py3-none-any.whl
Algorithm Hash digest
SHA256 e099aef3dec2578e32b0787436ce1c66736355ef28be5a45b3b7cf18b1632830
MD5 80485506b3ca3d1544e2b6d58f6a7cbd
BLAKE2b-256 d515f933fe4ec035dbb42a97bc5a5a95cabb509bcccbbe217ccef42d4580ffb7

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