Jason Kronemeyer

OSMnx Urban Network Analysis for Digital Equity

Integrating street network topology and urban morphology analysis with broadband infrastructure planning and digital equity research.

Research Type: Planning Document
Status: Research Plan • 2025
Research Area: Spatial Data for Community Development

Planning Status

This document outlines future research directions integrating OSMnx (OpenStreetMap NetworkX) capabilities with existing digital equity and broadband infrastructure analysis. Projects described are in the planning and design phase.

Introduction

OSMnx is a Python package that downloads street networks, buildings, bike lanes, rail, and walkable paths from OpenStreetMap and converts them into clean, routable NetworkX graphs. It provides powerful capabilities for:

Relevance to Digital Equity Research

Digital accessibility is fundamentally tied to physical accessibility. Communities isolated by sparse road networks, long travel distances, or limited transportation options face compounded barriers to digital inclusion. Understanding the relationship between physical network topology and digital infrastructure deployment enables more effective and equitable broadband planning.

Connection to Existing Repository Work

This research builds directly on established work in this repository:

By integrating OSMnx network analysis with existing broadband infrastructure data, we can develop more nuanced understanding of access patterns, deployment constraints, and equity implications in rural and underserved communities.

Research Project Ideas

🗺️ Project 1: Digital Access Accessibility Study

Overview

Use OSMnx to calculate drive-time and walk-time isochrones from public WiFi locations, libraries, and community centers in Michigan’s Eastern Upper Peninsula (EUP), identifying populations beyond reasonable travel distance to digital access points.

Research Questions

Methodology

  1. Network extraction: Download EUP road networks using OSMnx
  2. Access point mapping: Geocode libraries, community centers, schools with public WiFi
  3. Isochrone generation: Calculate multi-modal travel time polygons (drive, walk, bike)
  4. Population analysis: Overlay with Census block data to quantify affected populations
  5. Broadband integration: Combine with BEAD/RDOF coverage data for comprehensive view

Deliverables

Technical Stack

🛣️ Project 2: Infrastructure Routing Optimization

Overview

Model optimal fiber deployment paths using road network topology, analyzing existing infrastructure corridors (roads, utilities) for broadband buildout cost-distance analysis.

Research Questions

Methodology

  1. Network analysis: Extract road networks with attributes (width, surface type, traffic)
  2. Corridor identification: Map existing utility easements and rights-of-way
  3. Cost modeling: Assign deployment costs based on terrain, distance, infrastructure access
  4. Optimization algorithms: Use NetworkX shortest path and minimum spanning tree algorithms
  5. Scenario planning: Model multiple deployment strategies and compare outcomes

Deliverables

Integration with Existing Work

📊 Project 3: Digital Equity Composite Scoring

Overview

Create a multi-factor digital equity index combining broadband availability with network accessibility metrics, walkability/drivability scores, and proximity to digital literacy resources.

Research Questions

Methodology

  1. Factor development:
    • Broadband availability: Speed, provider competition, affordability (from FCC data)
    • Network accessibility: Road network connectivity, centrality measures
    • Physical access: Distance to digital resources, public transportation availability
    • Walkability scores: Pedestrian network analysis, sidewalk connectivity
    • Demographics: ACS data on income, education, age, disability status
  2. Index construction:
    • Normalize individual metrics to 0-100 scales
    • Apply weights based on research priorities and community input
    • Calculate composite scores by census tract and block group
  3. Validation:
    • Compare with existing digital divide research
    • Community feedback on score relevance and accuracy
    • Statistical validation of factor relationships

Deliverables

Policy Applications

🌲 Project 4: Rural Isolation Network Metrics

Overview

Quantify how road network topology affects digital inclusion by analyzing network centrality, connectivity, and resilience in sparse rural areas, comparing EUP characteristics to state and national benchmarks.

Research Questions

Methodology

  1. Graph theory metrics:
    • Betweenness centrality: Identify critical network nodes and bottlenecks
    • Closeness centrality: Measure average distance to all other locations
    • Clustering coefficient: Analyze local connectivity density
    • Network diameter: Calculate maximum shortest path lengths
  2. Comparative analysis:
    • Extract networks for EUP communities and comparable regions
    • Calculate metrics for different geographic scales (township, county, region)
    • Compare to urban and suburban network characteristics
    • Benchmark against state and national averages
  3. Resilience assessment:
    • Simulate network disruptions and measure impact
    • Identify single points of failure
    • Analyze redundancy and alternative routing options

Deliverables

Research Contribution

This project advances theoretical understanding of the relationship between physical and digital connectivity, providing empirical evidence for policy discussions about rural infrastructure investment priorities.

Technical Implementation

Required Python Packages

# Core spatial and network analysis
osmnx>=1.9.0          # Urban network analysis
networkx>=3.2         # Graph algorithms and analysis
geopandas>=0.14.0     # Spatial data manipulation
shapely>=2.0.0        # Geometric operations

# Visualization
folium>=0.15.0        # Interactive web maps
matplotlib>=3.8.0     # Static plotting
contextily>=1.5.0     # Basemap tiles

# Data processing
pandas>=2.1.0         # Tabular data
numpy>=1.26.0         # Numerical operations

# Integration with existing stack
duckdb>=0.10.0        # Fast spatial queries
pyarrow>=14.0.0       # Apache Arrow integration

Data Sources

Primary Data

Supplementary Data

Integration with Existing Technical Stack

DuckDB Integration

# Efficient spatial queries on large datasets
import duckdb

con = duckdb.connect()
con.execute("INSTALL spatial; LOAD spatial;")

# Join OSMnx network data with BEAD coverage
query = """
SELECT n.*, b.provider, b.max_speed
FROM network_nodes n
JOIN bead_coverage b
ON ST_Contains(b.geometry, ST_Point(n.x, n.y))
"""

Apache Sedona Integration

Neo4j Graph Database

Suggested Directory Structure

research/osmnx-projects/
├── notebooks/
│   ├── 01-network-extraction.ipynb
│   ├── 02-accessibility-analysis.ipynb
│   ├── 03-routing-optimization.ipynb
│   └── 04-equity-index.ipynb
├── data/
│   ├── raw/              # Downloaded OSM data
│   ├── processed/        # Cleaned networks
│   └── outputs/          # Analysis results
├── scripts/
│   ├── extract_networks.py
│   ├── calculate_isochrones.py
│   └── generate_metrics.py
├── visualizations/
│   ├── maps/             # Static map outputs
│   └── interactive/      # Folium HTML maps
└── docs/
    ├── methodology.md
    └── findings.md

Phased Development Roadmap

🔧 Phase 1: Environment Setup & Exploration

Timeline: 2-3 weeks
Objectives:

Key Milestones:

🎯 Phase 2: Accessibility Analysis Pilot

Timeline: 4-6 weeks
Objectives:

Key Milestones:

🌐 Phase 3: Integration with Broadband Maps

Timeline: 6-8 weeks
Objectives:

Key Milestones:

📈 Phase 4: Full Regional Analysis

Timeline: 8-12 weeks
Objectives:

Key Milestones:

📝 Phase 5: Documentation & Publication

Timeline: 4-6 weeks
Objectives:

Key Milestones:

Connection to Existing Work

Integration with Maps Directory

This research will enhance and extend existing map visualizations:

Digital Navigation & Maslow’s Hierarchy

Domain-Specific Performance

Finding the Digital Divide

Alignment with EUPConnect Collaborative Goals

The EUPConnect Collaborative focuses on expanding broadband access and digital equity across Michigan’s Eastern Upper Peninsula. This research directly supports those goals by:

  1. Evidence-based planning: Quantify accessibility barriers to inform infrastructure priorities
  2. Resource optimization: Identify cost-effective deployment strategies through network analysis
  3. Equity focus: Ensure investment prioritizes most isolated and underserved communities
  4. Community engagement: Provide accessible visualizations and scorecards for local stakeholders
  5. Policy advocacy: Generate empirical evidence for funding applications and policy discussions

Resources & References

OSMnx Documentation & Papers

Primary Resources:

Key Academic Papers:

Accessibility Analysis Research

Transportation & Access:

Digital Equity & Accessibility:

Broadband Infrastructure & Policy

Federal Resources:

Michigan-Specific:

Network Analysis & Graph Theory

Foundational Texts:

Software Documentation:

Urban Morphology & Spatial Analysis

Key Research:

Next Steps

Getting Started

This research plan provides a comprehensive framework for integrating OSMnx capabilities with digital equity analysis. The phased approach allows for iterative development, community feedback, and adaptive refinement based on initial findings.

Immediate Actions (Phase 1)

  1. Environment Setup: Install Python packages and configure development environment
  2. Data Inventory: Catalog available broadband coverage data and digital resource locations
  3. Pilot Selection: Choose 2-3 EUP communities for initial analysis
  4. Stakeholder Engagement: Connect with EUPConnect partners to refine research questions

Future Opportunities

Collaboration Opportunities

This research benefits from partnerships with:

Contact for Research Collaboration

Research Inquiries: jason@jasonkronemeyer.com
Areas of Interest: Urban network analysis, digital equity, rural connectivity, spatial data science

By integrating street network topology with broadband infrastructure analysis, we can develop more nuanced understanding of digital equity challenges and create more effective, community-centered solutions for expanding access in rural and underserved areas.