publications

2025

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

   Ane Rahbek Vierø, Anastassia Vybornova, and Michael Szell

   Geographical Analysis, 2025

   _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gean.12400

   Abs DOI

   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.

2. CoolWalks for active mobility in urban street networks

   Henrik Wolf, Ane Rahbek Vierø, and Michael Szell

   Scientific Reports, Apr 2025

   DOI
3. Network Analysis of the Danish Bicycle Infrastructure: Bikeability Across Urban–Rural Divides

   Ane Rahbek Vierø and Michael Szell

   Geographical Analysis, Apr 2025

   Abs DOI

   Research on cycling conditions focuses on cities, because cycling is commonly considered an urban phenomenon. People outside of cities should, however, also have access to the benefits of active mobility. To bridge the gap between urban and rural cycling research, we analyze the bicycle network of Denmark, covering around 43,000 km2 and nearly 6 million inhabitants. We divide the network into four levels of traffic stress and quantify the spatial patterns of bikeability based on network density, fragmentation, and reach. We find that the country has a high share of low-stress infrastructure, but with a very uneven distribution. The widespread fragmentation of low-stress infrastructure results in low mobility for cyclists who do not tolerate high traffic stress. Finally, we partition the network into bikeability clusters and conclude that both high and low bikeability are strongly spatially clustered. Our research confirms that in Denmark, bikeability tends to be high in urban areas. The latent potential for cycling in rural areas is mostly unmet, although some rural areas benefit from previous infrastructure investments. To mitigate the lack of low-stress cycling infrastructure outside urban centers, we suggest prioritizing investments in urban–rural cycling connections and encourage further research in improving rural cycling conditions.

4. BikeNodePlanner: A data-driven decision support tool for bicycle node network planning

   Anastassia Vybornova, Ane Rahbek Vierø, Kirsten Krogh Hansen, and 1 more author

   Environment and Planning B: Urban Analytics and City Science, Jun 2025

   Abs DOI

   A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a formalized methodology and data-driven tools for the planning of such networks is a hindrance to their adaptation on a larger scale. To address this need, we present the BikeNodePlanner: A fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner allows the user to evaluate and compare bicycle node network plans through a wide range of metrics, such as land use, proximity to points of interest, and elevation across the network. The BikeNodePlanner provides data-driven decision support for bicycle node network planning and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling.

2024

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, Feb 2024

   Abs DOI

   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.

2. Teaching spatial data science

   Ane Rahbek Vierø and Michael Szell

   Geoforum Perspektiv, Dec 2024

   Abs DOI

   Spatial data science is an emerging field building on geographic information science, geography, and data science. Here we first discuss the definition and history of the field, arguing that it indeed warrants a new label. Then, we present the design of our course Geospatial Data Science at IT University of Copenhagen and discuss the importance of teaching not just spatial data science tools but also spatial and critical thinking. We conclude with a perspective on the potential future for spatial data science, arguing that qualitative theory and methods will continue to play an important role despite new GeoAI-related advances. , ‘Spatial data science’ er et spirende felt, der udspringer af både geografisk informationsvidenskab, geografi og data science. I denne artikel diskuterer vi først begrebet spatial data science, introducerer dets historie og argumenterer for, at det repræsenterer et nybrud i forhold til tidligere tendenser indenfor GIS og geodata. Herefter præsenterer vi vores overvejelser om undervisning i spatial data science i forbindelse med udviklingen af kurset Geospatial Data Science på IT Universitet i København. Vi understreger vigtigheden i ikke bare at undervise i praktiske anvendelser af geodata, men også at lære de studerende geografisk og kritisk tænkning. Afslutningsvis diskuterer vi fremtiden for spatial data science, og argumenterer for, at geografisk teori og metoder vil forblive relevante trods af den stigende indflydelse fra GeoAI.