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NACIS 2016 Presentation
Nate Wessel, University of Toronto
Algorithmic detection of dead-ends and highly indirect streets could help cartographers reduce visual noise in transport maps, without resort to generalization techniques that simplify data or remove it entirely. In this presentation, I'll discuss algorithms for detecting dead-ends and apply them to a sample of regions, using OpenStreetMap data. I'll attempt to show how the resulting classification can be used to reduce visual noise and make maps easier for the eye to navigate. Preliminary results show that dead-ends make up between 12% and 45% of all streets and/or paths in my broad sample of regions, and can depend in varying degrees on the chosen transport mode for which the network is constructed. The proposed technique then has special relevance for mode-specific transport maps or maps for users with unique access constraints.