interflow 1.0.4

An open-source Python package for calculating and organizing sectoral flows and interdependencies

interflow

interflow is an open-source Python package for collecting, calculating, organizing, and visualizing cross-sectoral resource interdependencies and flows.

Purpose

interflow provides a flexible, adaptable, and updatable method to evaluate the interdependencies between sectors and across regions for multiple resource types. The interdependencies and relationships between sectors offer multi-faceted and multi-scale opportunities as well as vulnerabilities. Whether it is water, energy, or otherwise, resources pass from source to discharge by flowing through sectors and are oftentimes a critical component of a sector's functionality.

The interflow package was created to:

• Provide a framework to aggregate and organize known resource flows through various sectors within a region
• Calculate and build additional flows between sectors in alternative units based on interdependency intensity values
• Provide data output and visualizations in a format that can be used to answer questions about cross-sectoral dependencies, how resources flow through sectors in an individual region, and how these dependencies compare across regions.

Though the package comes equipped with sample data for water and energy values across multiple sectors at the US county level, the interflow package can be used for any region, set of regions, set of sectors, and resource type so long as the appropriate data is provided by the user.

For more information on how the interflow package works, how to generalize it with your own data, see the documentation.

Installation

interflow can be installed via pip by running the following from a terminal window:

pip install interflow

Quickstarter

See interflow in action with our Quickstarter Jupyter Notebook hosted on binder!

User Guide

If you're interested in background information and explanations on how the model works visit out User Guide

Contributing to interflow

Spotted a bug or have a suggestion to improve the model? We'd love your input! See our guidelines for contributing.