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The Bicycle Network Analysis (BNA) is data analysis software that measures how well the bike network in a given area connects people to the places they want to go, automating the otherwise labor-intensive task of network analysis. The BNA produces network analysis results for each census block in a designated area, as well as an overall score for the area. The software is freely available for anyone to run from source code via the Brokenspoke Analyzer.


The BNA relies on the concept of a low-stress bike network as developed by Maaza Mekuria, Peter Furth, and Hilary Nixon at the Mineta Transportation Institute and approximately aligns with NACTO street and bikeway design standards. The BNA's low-stress determination generally translates to a Level of Traffic Stress 1 or 2 rating on the Mineta Transportation Institute's original scale. In practical terms, this corresponds with the comfort level of a typical adult with an interest in riding a bicycle but who is concerned about interactions with motor vehicles.

The BNA scores cities in four steps:

  1. Data collection
  2. Traffic stress analysis
  3. Destination access analysis
  4. Score aggregation

Each step is described below. Click through the tabs to learn more about our process.

The types and sources of data used in the BNA are listed in Table 1.

Table 1. Data Types and Sources in the Bicycle Network Analysis

Data TypeCountry
United StatesAustraliaCanadaAll other international
Bike + street infrastructureOpenStreetMap via Geofabrik
City boundaryU.S. Census TIGER/Line Files or individual submissionsAustralian Bureau of StatisticsStatistics CanadaOpenStreetMap via Overpass Turbo or individual submissions
Default residential speed limitBased on state law or individual submissions25 mph (40 km/h) or individual submissions
Unit of analysisCensus blockStatistical area level 1Dissemination area1 km square
Population2010 Decennial Census2021 Census2021 Census of Population2020 WorldPop (unconstrained, UN adjusted)
JobsLEHD Origin-Destination Employment Statistics (except U.S. territories, where jobs are excluded)Excluded

Although most places in the BNA database are cities, the BNA can measure any area within its computational limits. For the purposes of this methodology, we will refer to the measured area as a city. Take caution when comparing BNA results between cities with different population data sources, as smaller geographic units increase the stringency of the analysis.

The BNA obtains information on bike infrastructure, street, and intersection characteristics as well as the location of common destinations from OpenStreetMap. OpenStreetMap employs a system of key-value pairs called 'tags' to represent metadata about map features. For a comprehensive review of the tags recognized in the BNA, please refer to our tagging guidelines. When any needed information is missing from OpenStreetMap, the BNA makes assumptions about street characteristics based on the functional class of the street in OpenStreetMap as described in Tables 2, 3, and 4.

Table 2. Street Segment Assumptions

» For two-way streets, each side is evaluated separately

Functional classSpeedNumber of lanesParkingParking lane widthRoadway width
Primary40 mph2Yes8 ftN/A
Secondary40 mph2Yes8 ftN/A
Tertiary30 mph1Yes8 ftN/A
Unclassified25 mph1YesN/A27 ft
Residential25 mph1YesN/A27 ft

Table 3. Bicycle Facility Minimum Width Assumptions

» Gutter does not count towards bike lane width

Bike InfrastructureMulti-Use PathBuffered Bike LaneBike Lane (with parking)Bike Lane (no parking)
Width8 ft6 ft5 ft4 ft

Table 4. Intersection Signal Assumptions

» Uncontrolled intersections assume a low-stress crossing for travel along the higher order roadway

Functional ClassPrimarySecondaryTertiaryResidential

The BNA downloads OpenStreetMap data and census data then clips it to match the city's boundary plus a buffer distance around the boundary equivalent to the default trip distance, which is 2,680 meters or 1.67 miles, a distance roughly equivalent to a person biking 10 minutes at 10 mph.