Knowledge to effective prevention: Power of open spatial data, models, methods
Abstract
Slides of the presentation held at the ICSC 2025 in Oslo. You find more detailed information on the methods used in the following scientific publications and preprint: gap detection / prioritization approach (preprint): https://doi.org/10.5281/zenodo.13349332 open source software for bikeability assessment „NetAScore“: https://doi.org/10.1177/23998083241293177 validation of the generic bikeability model of NetAScore: https://doi.org/10.1016/j.jcmr.2024.100040 spatial centrality (serves as foundation for the final approach used in gap detection): https://doi.org/10.4230/LIPIcs.GIScience.2023.83
Full text
Knowledge to effective prevention: Power of open spatial data, models, methods International Cycling Safety Conference, 2025-11-05, Oslo, Norway Dr. Christian Werner Mobility Lab, Department of Geoinformatics Paris Lodron University Salzburg
KEHN HERMANO
Fragmentation of the network
Fragmentation of the network
EFFECTIVE PREVENTION Knowledge Coherent, safe cycling network
DATA-DRIVEN METHODS
BIKEABILITY of road infrastructure for cycling #1 SUITABILITY
Bikeability of road segments 8 Datenbasiert, reproduzierbar, transparent ¥Validated model ¥Customizable ¥Indicators regarding: Publication (validation study): Werner, C., van der Meer, L., Kaziyeva, D., Stutz, P., Wendel, R., & Loidl, M. (2024). Bikeability of road segments: An open, adjustable and extendible model. Journal of Cycling and Micromobility Research, 2, 100040. https://doi.org/10.1016/j.jcmr.2024.100040
Bikeability: Data 9 Datenbasiert, reproduzierbar, transparent ¥OpenStreetMap
Segment importance: Centrality 16 Datenbasiert, reproduzierbar, transparent
Segment importance: Centrality 17 Datenbasiert, reproduzierbar, transparent
Segment importance: Centrality 18 Datenbasiert, reproduzierbar, transparent ¥Utilizes bikeable routes ¥Making use of safe, well-suited infrastructure where feasible
Segment importance: Centrality 19 a) standard metric b) spatially normalized
Segment importance: Centrality 20 Datenbasiert, reproduzierbar, transparent a) standard metric b) distance-restricted
Segment importance: Example for Oslo 21 Datenbasiert, reproduzierbar, transparent a) 4 km b) 7 km
Segment importance: Example for Oslo 22 Datenbasiert, reproduzierbar, transparent
PRIORITY SCORE of road segments to fill critical gaps #3 PRIORITY
Prioritization 24 Werner, C., & Loidl, M. (2024). Creating coherent cycling networks: A spatial network science perspective. Zenodo. https://doi.org/10.5281/zenodo.13349333 ¥Identify segments of high systemic importance but low bikeability ¥Input for detailed planning (local and planning experts)
Prioritization: Example for Oslo 25 Datenbasiert, reproduzierbar, transparent
Key aspects ¥Aim: create a coherent, safe cycling network ¥Data-driven method can improve efficiency and effectivity of planning processes ¥Experts can focus on implementation ¥Future work ¥Integrate intersections (data availability) ¥Specifically target vulnerable groups
Knowledge to effective prevention: Power of open spatial data, models, methods Dr. Christian Werner Mobility Lab, Dep. of Geoinformatics Paris Lodron University Salzburg [email protected] linkedin.com/in/ch-werner @[email protected] International Cycling Safety Conference, 2025-11-05, Oslo, Norway https://mobilitylab.zgis.at