README.md

Pedestrian Environment Index (PEI) Implementation

This project implements the Pedestrian Environment Index (PEI) methodology as developed at the University of Illinois Chicago (see the research paper: https://www.sciencedirect.com/science/article/pii/S0966692314001343). The PEI provides a composite measure of the walkability of an environment, incorporating the following subindices:

• Population Density Index (PDI)
• Commercial Density Index (CDI)
• Intersection Density Index (IDI)
• Land-use Diversity Index (LDI)

Motivation

Understanding the walkability of an environment is important for urban planning, public health initiatives, and promoting active transportation. This implementation of the PEI can be used by researchers to:

• Assess the current walkability of neighborhoods or regions
• Compare walkability across different areas
• Identify areas with potential for improvement

Getting Started

• Prerequisites:
  • Python 3.x
  • Libraries:
    • osmnx
    • pandas
    • numpy
    • matplotlib.pyplot
    • csv
    • census
  • Census API Key: Can be found at: https://api.census.gov/data/key_signup.html. Must paste key in text file titled census_api_key.txt in the same directory as PDI_generator.ipynb to access population data.

Installation

You can install the required libraries using pip:

pip install osmnx pandas numpy matplotlib csv census

• Usage:

Contributing

License

Idk how licensing works... Just don't sell it I guess.