Advanced visual analytics interfaces
Proceedings of the International Conference on Advanced Visual Interfaces
A conceptual framework and taxonomy of techniques for analyzing movement
Journal of Visual Languages and Computing
Exploring city structure from georeferenced photos using graph centrality measures
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Analysing the spatial dimension of eye movement data using a visual analytic approach
Expert Systems with Applications: An International Journal
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Inferring human mobility patterns from anonymized mobile communication usage
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
A visual analytics framework for spatio-temporal analysis and modelling
Data Mining and Knowledge Discovery
Opening up the "black box" of medical image segmentation with statistical shape models
The Visual Computer: International Journal of Computer Graphics
Tracing the German centennial flood in the stream of tweets: first lessons learned
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Vector field k-means: clustering trajectories by fitting multiple vector fields
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
Visualizing interchange patterns in massive movement data
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Movement data (trajectories of moving agents) are hard to visualize: numerous intersections and overlapping between trajectories make the display heavily cluttered and illegible. It is necessary to use appropriate data abstraction methods. We suggest a method for spatial generalization and aggregation of movement data, which transforms trajectories into aggregate flows between areas. It is assumed that no predefined areas are given. We have devised a special method for partitioning the underlying territory into appropriate areas. The method is based on extracting significant points from the trajectories. The resulting abstraction conveys essential characteristics of the movement. The degree of abstraction can be controlled through the parameters of the method. We introduce local and global numeric measures of the quality of the generalization, and suggest an approach to improve the quality in selected parts of the territory where this is deemed necessary. The suggested method can be used in interactive visual exploration of movement data and for creating legible flow maps for presentation purposes.