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Pedestrian Environment Index (PEI) Documentation

The Pedestrian Environment Index (PEI) is a composite measure of walkability that combines four key subindices to evaluate pedestrian-friendly environments.

Core Subindices

  1. Population Density Index (PDI)
  2. Measures residential population density within defined areas
  3. Data sourced from Census block groups

  4. Implementation: PDI_generator.ipynb

  5. Commercial Density Index (CDI)

  6. Evaluates density of commercial establishments per Block Group
  7. Indicates availability of walkable destinations and services

  8. Implementation: CDI_generator.ipynb

  9. Intersection Density Index (IDI)

  10. Quantifies intersection density within an area
  11. Evaluates route options and pedestrian safety

  12. Implementation: IDI_generator.ipynb

  13. Land-use Diversity Index (LDI)

  14. Analyzes mix of land-use types (residential, commercial, industrial)
  15. Assesses environment walkability through land use diversity
  16. Implementation: LDI_generator.ipynb

Implementation Workflow

  1. Subindex Calculation
  2. Individual Jupyter notebooks (*_generator.ipynb) process Census block group shapefiles
  3. Each generator computes its respective subindex score

  4. Outputs saved as CSV or GeoJSON files

  5. PEI Compilation

  6. PEI_generator.ipynb combines subindex outputs

  7. Computes final PEI score for each block group

  8. Visualization

  9. Results displayed as geographic maps
  10. PEI scores visualized across census block groups

Note: Project is transitioning to standardize all output files to GeoJSON format.

Detailed Index Methodologies

Commercial Density Index (CDI)

Overview

Calculates amenity counts per block group, normalized against the region's maximum commercial density.

Input Data

  • Source: atl_bg.geojson
  • Contains Atlanta neighborhood data
  • Uses OSMNx for amenity quantification

Amenity Categories

  • Groceries: supermarket, convenience, grocery, food, organic
  • Restaurants: restaurant, cafe, food_court, bistro, fast_food
  • Banks: bank, atm
  • Schools: school, college, university, kindergarten, music_school, language_school, driving_school
  • Entertainment: cinema, theatre, nightclub, casino, arts_centre, sports_centre, stadium, amusement_arcade, dance, bowling_alley, attraction, theme_park, zoo
  • Parks: recreation_ground, grass, greenfield

Output

Normalized commercial density values relative to regional maximum

Intersection Density Index (IDI)

Overview

Analyzes intersection patterns within block groups to evaluate street connectivity.

Input Requirements

  • GeoJSON file containing:
  • Block group geometries
  • State and county FIPS codes

Output Data (CSV)

  • Polygon: Block group geometry
  • Area: Block group area
  • Intersection: Sum of intersection-connected roads
  • IDI: Normalized intersection density value

Processing Steps

  1. Read GeoJSON geometry data
  2. Extract intersection data via OSMNx
  3. Calculate equivalency factors
  4. Compute population density
  5. Generate visualization-ready output

Land Diversity Index (LDI)

Overview

Evaluates land use diversity within block groups.

Input Requirements

  • GeoJSON file containing:
  • Block group geometries
  • State and county FIPS codes

Output Data (CSV)

  • Polygon: Block group geometry
  • Land_use_dict: Land use type areas
  • Entropy: Block entropy value
  • LDI: Normalized land diversity value

Processing Steps

  1. Extract GeoJSON geometry
  2. Gather land use data via OSMNx
  3. Calculate block entropy
  4. Compute land diversity
  5. Prepare visualization data

Population Density Index (PDI)

Overview

Processes Census Bureau population data to calculate density metrics.

Input Requirements

  • GeoDataFrame with:
  • Block group geometries
  • State/county FIPS codes
  • Census API key (from parameter or census_api_key.txt)

Output Data (GeoDataFrame)

  • POP: Block group population
  • POPDENSITY: Persons per square kilometer
  • NORMPOPDENSITY: Normalized population density

Processing Steps

  1. Validate API credentials
  2. Extract FIPS codes
  3. Retrieve Census data
  4. Integrate population data
  5. Calculate density metrics
  6. Clean and format output

Error Handling

Raises ValueError if Census API key is unavailable