Post on 11-Apr-2017
Deemphasizing Dead-EndsNavigating Today’s Dendritic Cities
Nate WesselPhD student at the University of Toronto
nate.wessel@mail.utoronto.ca
What’s going on here?
Map #1
- All streets are the same
Map #2
- Dead-ends are 50% transparent- Indirect streets are up to 50%
transparent
1
2
What’s going on here?
Developed technique for a bike map in a hilly suburban city ->
Problem:
- Noisy map in the absence of road hierarchy
Solution
- Clarify topology- Reduce noise- Free up visual channels
Research Questions
1. How many dead-ends are there?2. Where are they?3. How dependant are they on mode?4. Is this even remotely helpful??
Methods
23 urban regions selected for variety and data quality
(whole built-up region used)
OpenStreetMap -> osm2po -> PostGIS -> R
Constructed graphs for: car, bike, foot
Code on Github at https://github.com/Nate-Wessel/dead-ends
Results: Where are they?
● Edge centroids -> KDE surface weighted by edge length
● Ratio of dead-ending segments to total
● = % dead-ends, locally
Results: Dependence on mode?
Anecdotal musings:- European cities and New Urbanism
- Dead-ends for cars, connections for people
Next Step: Does this actually work?
Need to test whether this technique actually helps people read maps
● Developing platform to test○ A->B route-finding speed○ A->B route-finding accuracy○ Does it matter where A & B are? What if they are on dead-ends?
Discussion
- Technique seems relevant to many cities- Mode specificity is an issue - Data quality issues a concern ( OSM )- Algorithm could be way smarter- Needs empirical testing