Suggested by: Martin Loidl
Short description: Connectivity is a fundamental spatial parameter. In road networks, connectivity decides on opportunities for moving through space in a comfortable way. Literature suggests that connectivity in networks has a direct effect on mobility behaviour.
Especially in urban environments, connectivity varies between different modes. Cities are planned and built for the predominant mode (from walkable cities to car-oriented cities). Based on OpenStreetMap (OSM), different subsets of urban road networks should be investigated for their connectivity. We hypothesize to find a significant correlation between the connectivity for different modes and the respecitve modal share.
In this research, OSM data should be acquired, processed and analyzed (semi-) automatically, in order to facilitate the calculation of connectivity indices for different modes and to compare these indices among various cities. Moreover, connectivity indices should be related to modal split statistics for the respective regions.
References, suggested reading:
- LOWRY, M. & LOH, T. H. 2017. Quantifying bicycle network connectivity. Preventive Medicine, 95, S134-S140.
- ABAD, L. & VAN DER MEER, L. 2018. Quantifying Bicycle Network Connectivity in Lisbon Using Open Data. Information, 9.
- PORTA, S., CRUCITTI, P. & LATORA, V. 2006. The network analysis of urban streets: A dual approach. Physica A: Statistical Mechanics and its Applications, 369, 853-866.
- HOCHMAIR, H. 2020. Von A nach B? – Erreichbarkeits- und Konnektivitätsanalysen in Verkehrsnetzwerken. In: ZAGEL, B. & LOIDL, M. (Eds.) Geo-IT in Mobilität und Verkehr. Berlin und Offenbach: Wichmann Verlag / VDE.
Prerequisites/qualifications: Profound skills in data modellingand processing. Scripting, database and spatial analytics (network analysis) skills.