BikeDNA: A Tool for Bicycle Infrastructure Data & Network Assessment

TL;DR

BikeDNA addresses the lack of topology-aware data quality assessment for bicycle infrastructure by providing an open-source Python tool that can evaluate one or two data sets (e.g., OSM and a reference) at global and local scales. It combines intrinsic and extrinsic analysis, addresses network density, tag consistency, topology errors, and feature matching, and produces interactive maps and reports. The approach enables detection of spatial heterogeneity in data quality and supports use cases from urban planning to data improvement and network research. The results from a Greater Copenhagen showcase demonstrate substantial data quality issues in both OSM and admin data and highlight the need for routine, localized quality assessments to inform planning and data maintenance.

Abstract

High-quality data on existing bicycle infrastructure are a requirement for evidence-based bicycle network planning, which supports a green transition of human mobility. However, this requirement is rarely met: Data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown, heterogeneous, or low quality. Currently available tools for road network data quality assessment often fail to account for network topology, spatial heterogeneity, and bicycle-specific data characteristics. To fill these gaps, we introduce BikeDNA, an open-source tool for reproducible quality assessment tailored to bicycle infrastructure data with a focus on network structure and connectivity. BikeDNA performs either a standalone analysis of one data set or a comparative analysis between OpenStreetMap and a reference data set, including feature matching. Data quality metrics are considered both globally for the entire study area and locally on grid cell level, thus exposing spatial variation in data quality. Interactive maps and HTML/PDF reports are generated to facilitate the visual exploration and communication of results. BikeDNA supports quality assessments of bicycle infrastructure data for a wide range of applications -- from urban planning to OpenStreetMap data improvement or network research for sustainable mobility.

Figures (7)

• Figure 1: The pipeline of BikeDNA. The analysis (III) is divided into three parts: 1) Intrinsic analysis of OSM bicycle network data (purple), 2) intrinsic analysis of reference bicycle network data (orange), and 3) and comparison of OSM and reference data (black), including feature matching. Dotted parts are optional.
• Figure 2: Known quality issues in bicycle infrastructure data. a) Different aspects of data quality assessment: Accuracy (left) versus topology (right). Early research on spatial data quality focused on accuracy, while BikeDNA puts a special emphasis on topology. Adapted from haklay_how_2010 and neis_street_2012. b) Errors of omission (left) or commission (right) result in differing data completeness. c) Misclassification leads to different types of bicycle infrastructure in two corresponding data sets (red versus black). d) Edge undershoot (left) and edge overshoot (right). e) Mapping of bicycle infrastructure to the centerline of the road (red) or on the side (black). In this case, the centerline mapping creates routable network data at the cost of accuracy.
• Figure 3: Input data for test area from a) OSM and b) GeoDanmark. Maps created with BikeDNA v.1.0.0.
• Figure 4: Results from intrinsic analysis of OSM and GeoDanmark data: a) OSM edge density. b) Missing OSM tags. c) Disconnected components. d) Example of an undershoot. e) Edges from two disconnected components with less than the specified distance threshold between them. Maps created with BikeDNA v.1.0.0.
• Figure 5: Extrinsic analysis based on a comparison of intrinsic results: Zipf plots of a) OSM and b) GeoDanmark component length distribution. Percent of cells reachable through c) the OSM and d) the reference networks. Maps created with BikeDNA v.1.0.0.