Bike Infrastructure Data Analysis: Transforming Cycling Networks with Data

Why Bike Infrastructure Data Matters

Cycling infrastructure is no longer a niche concern—it's central to urban sustainability, public health, and transportation equity. Yet many cities lack rigorous data to guide investment decisions. They don't know where cyclists actually ride, which routes are underutilized, or how network improvements affect ridership patterns.

This is where bike infrastructure data analysis becomes essential. By systematically collecting and analyzing cycling data, cities and transportation agencies can:

• Identify high-demand corridors and priority expansion areas
• Evaluate the effectiveness of completed infrastructure investments
• Forecast ridership growth and network adoption
• Allocate scarce budgets to projects with the highest community impact
• Track progress toward active transportation and climate goals

We've worked on bike count data projects across the Pacific Northwest and beyond. These projects demonstrate that rigorous data analysis drives smarter, more equitable infrastructure planning.

Data Sources for Cycling Infrastructure

Building a comprehensive bike infrastructure data analysis starts with understanding your data sources. The most valuable cycling datasets combine multiple approaches:

Bike Count Sensors

Permanent and portable bike count sensors are the foundation of infrastructure analysis. These devices use infrared, radar, or pressure sensors to automatically record cyclist movements. When deployed on key corridors and at regular intervals, they provide:

• Temporal trends (hourly, daily, seasonal ridership patterns)
• Before/after analysis of infrastructure improvements
• Directional flow and peak usage times
• Long-term growth tracking for planning forecasts

Strava Metro & Crowdsourced Data

Strava Metro provides anonymized data from hundreds of thousands of cyclists using the Strava mobile app. This volunteer data layer shows actual cycling patterns across neighborhoods—revealing informal routes, desired corridors, and underserved populations. We use Strava data to complement sensor counts and identify network gaps that traditional surveys might miss.

OpenStreetMap & Network Data

OpenStreetMap (OSM) is an invaluable free resource for bike infrastructure mapping. It tags bike lanes, paths, and shared routes across the world. When combined with bike count and Strava data, OSM enables bicycle network analysis—measuring connectivity, identifying missing links, and computing accessibility metrics for equity analysis.

Survey & Community Data

Quantitative bike counts tell part of the story. Community surveys reveal why people do or don't cycle, what barriers exist, and which populations are underrepresented in cycling. Harospec Data helps design, deploy, and analyze surveys that center equity in infrastructure planning.

Tools & Methods We Use

Bike infrastructure data analysis requires a blend of GIS, statistical modeling, and visualization expertise. Our team leverages industry-standard tools:

Python & Data Processing

Python, with libraries like pandas and scikit-learn, is our primary tool for cleaning, transforming, and analyzing bike count time series. We build ETL pipelines to ingest sensor data, handle missing values, and standardize datasets across multiple jurisdictions—essential for comparative analysis.

QGIS for Spatial Analysis

QGIS enables us to visualize bike infrastructure networks, calculate network metrics, and perform spatial analysis. With QGIS, we assess bike lane coverage by neighborhood, compute shortest paths for equity analysis, and create publication-quality maps that tell the story of your cycling network.

Statistical Modeling & Forecasting

Bike ridership follows predictable seasonal and growth patterns. We build forecasting models—from simple trend analysis to advanced machine learning—to project ridership under different infrastructure scenarios. These models inform capital planning and help justify investment to funders and elected officials.

Real-World Application: Network Optimization

Here's how bike infrastructure data analysis works in practice: A mid-sized city has installed bike counters on five key corridors. They've collected two years of count data, plus Strava activity from local riders. Our analysis reveals:

• Corridor A shows 40% growth year-over-year; investment here pays dividends
• Corridor B has low counts but high Strava activity—suggesting infrastructure gaps deterring riders
• Neighborhoods with no bike infrastructure show near-zero cycling; targeted investment could unlock demand
• Peak hours shift seasonally; maintenance and safety messaging should align with actual usage patterns

With these insights, the city can reallocate its $2M annual budget toward Corridor B improvements and equity-focused neighborhood projects—maximizing impact per dollar spent. That's the power of data-driven infrastructure planning.

Building Your Bike Data Strategy

Whether you're a city planner, transportation agency, or advocacy group, a robust bike infrastructure data strategy should include:

• Data collection: Install sensors on strategic corridors; leverage Strava and OSM
• Standardization: Build pipelines to clean and align data across sources
• Analysis: Compute ridership trends, network metrics, and forecasts
• Visualization: Create dashboards and maps for stakeholders and decision-makers
• Reporting: Document findings, limitations, and recommendations for actionable insights

At Harospec Data, we specialize in helping organizations build exactly this kind of data infrastructure. Our experience spans transportation planning, GIS analysis, and interactive dashboards—all focused on turning complex cycling data into clarity and action.

Our Transportation Expertise

Bike infrastructure is just one corner of the transportation data landscape. Our team has deep expertise in active transportation planning, decision support tools, and geospatial analysis. We've built systems for state transportation agencies and city planning departments across the West. If you're looking to transform your cycling data into strategic decisions, we're here to help.