publications

2024

1. How Good Is Open Bicycle Network Data? A Countrywide Case Study of Denmark

   Ane Rahbek Vierø, Anastassia Vybornova, and Michael Szell

   Geographical Analysis, Apr 2024

   Abs

   Cycling is a key ingredient for a sustainability shift of Denmark’s transportation system. To increase cycling rates, better bicycle infrastructure networks are required. Planning such networks requires high‐quality infrastructure data, yet the quality of bicycle infrastructure data is understudied. Here, we compare the two largest open data sets on dedicated bicycle infrastructure in Denmark, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether the data are good enough for network‐based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data conflation is necessary to obtain a more complete data set. Our analysis of the spatial variation of data quality suggests that rural areas are more prone to incomplete data. 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 is thus necessary to assess data completeness. Based on our data quality assessment, we recommend strategic mapping efforts toward data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high‐quality bicycle network data.

2023

1. BikeDNA: A tool for bicycle infrastructure data and network assessment

   Ane Rahbek Vierø, Anastassia Vybornova, and Michael Szell

   Environment and Planning B: Urban Analytics and City Science, Jun 2023

   Publisher: SAGE Publications Ltd STM

   Abs

   Building high-quality bicycle networks requires knowledge of existing bicycle infrastructure. However, bicycle network data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown, heterogeneous, or low quality, which hampers the green transition of human mobility. In particular, bicycle-specific data have peculiarities that require a tailor-made, reproducible quality assessment pipeline: For example, bicycle networks are much more fragmented than road networks, or are mapped with inconsistent data models. To fill this gap, 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.