How Good Is Open Bicycle Infrastructure Data? A Countrywide Case Study of Denmark

Abstract

Cycling is a key ingredient for a sustainability shift of Denmark's transportation system. To increase cycling rates, a better nationwide network of bicycle infrastructure is required. Planning such a network requires high-quality infrastructure data, however, the quality of bicycle infrastructure data is severely understudied. Here, we compare Denmark's two largest open data sets on dedicated bicycle infrastructure, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether data is good enough for network-based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data set conflation is necessary to obtain a complete dataset. Our analysis of the spatial variation of data quality suggests that rural areas are more likely to suffer from problems with data completeness. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features thus is necessary to assess data completeness. Based on our data quality assessment we recommend strategic mapping efforts towards data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high-quality bicycle network data.

Figures (13)

• Figure 1: Overview of the two input data sets. OSM bicycle infrastructure (left). GeoDanmark bicycle infrastructure (right). Copenhagen and surroundings in map insert.
• Figure 2: Types of bicycle infrastructure. The analysis includes dedicated bicycle infrastructure, which can be either protected bicycle tracks (left) or unprotected bicycle lanes (right).
• Figure 3: Common quality issues in bicycle infrastructure data. Left: Different levels of data completeness, with an error of commission resulting in a longer bike path in the GeoDanmark data (orange) than OSM (purple). Center: Different data models in OSM (purple) and GeoDanmark (orange), with OSM using a center line mapping and GeoDanmark mapping all infrastructure with separate geometries. Right: Example of an undershoot in OSM data.
• Figure 4: Example of hex grid aggregation. Hex grid cells used to compute the local infrastructure density of OSM data.
• Figure 5: Difference in infrastructure density between OSM and GeoDanmark data at the municipal level. The infrastructure density difference is computed as GeoDanmark km/km^2 - OSM km/km^2. Negative values (blue) indicate municipalities where OSM data have a higher density; positive values (red) indicate municipalities where GeoDanmark data have a higher density. Out of the 98 municipalities, only two have more infrastructure mapped in GeoDanmark than OSM.