Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convexity rule for shape decomposition based on discrete contour evolution
Computer Vision and Image Understanding
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Representation with Aspect Maps
Spatial Cognition, An Interdisciplinary Approach to Representing and Processing Spatial Knowledge
Constraint-Based Spring-Model Algorithm for Graph Layout
GD '95 Proceedings of the Symposium on Graph Drawing
GD '96 Proceedings of the Symposium on Graph Drawing
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Enhancing the Accessibility of Maps with Personal Frames of Reference
Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction
Automatic generation of destination maps
ACM SIGGRAPH Asia 2010 papers
A mixed-integer program for drawing high-quality metro maps
GD'05 Proceedings of the 13th international conference on Graph Drawing
VISUAL'05 Proceedings of the 8th international conference on Visual Information and Information Systems
Explorative data analysis based on self-organizing maps and automatic map analysis
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Uncovering locally characterizing regions within geotagged data
Proceedings of the 22nd international conference on World Wide Web
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Shape simplification in map-like representations is used for two reasons: either to abstract from irrelevant detail to reduce a map user's cognitive load, or to simplify information when a map of a smaller scale is derived from a detailed reference map. We present a method for abstracting simplified cartographic representations from more accurate spatial data. First, the employed method of discrete curve evolution developed for simplifying perceptual shape characteristics is explained. Specific problems of applying the method to cartographic data are elaborated. An algorithm is presented, which on the one hand simplifies spatial data up to a degree of abstraction intended by the user; and which on the other hand does not violate local spatial ordering between (elements of) cartographic entities, since local arrangement of entities is assumed to be an important spatial knowledge characteristic. The operation of the implemented method is demonstrated using two different examples of cartographic data.