Location-dependent generalization of road networks based on equivalent destinations

Suppose a user located at a certain vertex in a road network wants to plan a route using a wayfinding map. The user's exact destination may be irrelevant for planning most of the route, because many destinations will be equivalent in the sense that they allow the user to choose almost the same...

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Bibliographic Details
Published in:Computer graphics forum Vol. 35; no. 3; pp. 451 - 460
Main Authors: van Dijk, Thomas C., Haunert, Jan-Henrik, Oehrlein, Johannes
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.06.2016
Subjects:
Algorithms
Analysis
Categories and Subject Descriptors (according to ACM CCS)
Clustering
Clusters
Computer graphics
Computer networks
I.2.1 [ARTIFICIAL INTELLIGENCE]: Applications and Expert Systems-Cartography
I.5.3 [PATTERN RECOGNITION]: Clustering-Algorithms
map generalization
Networks
Roads
Roads & highways
Routing
Routing (telecommunications)
simplification
Studies
Visualization
Wayfinding
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Suppose a user located at a certain vertex in a road network wants to plan a route using a wayfinding map. The user's exact destination may be irrelevant for planning most of the route, because many destinations will be equivalent in the sense that they allow the user to choose almost the same paths. We propose a method to find such groups of destinations automatically and to contract the resulting clusters in a detailed map to achieve a simplified visualization. We model the problem as a clustering problem in rooted, edge‐weighted trees. Two vertices are allowed to be in the same cluster if and only if they share at least a given fraction of their path to the root. We analyze some properties of these clusterings and give a linear‐time algorithm to compute the minimum‐cardinality clustering. This algorithm may have various other applications in network visualization and graph drawing, but in this paper we apply it specifically to focus‐and‐context map generalization. When contracting shortest‐path trees in a geographic network, the computed clustering additionally provides a constant‐factor bound on the detour that results from routing using the generalized network instead of the full network. This is a desirable property for wayfinding maps.
Bibliography:ark:/67375/WNG-ZPNGXZRH-B
istex:0F7841AD4C3B8BED389A4ED0F8760BD51740142B
ArticleID:CGF12921
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12921