title: Spatial generalization and aggregation of massive movement data.
abstract: 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.
authors.affiliation: Fraunhofer Institute IAIS, Sankt Augustin.
authors.forename: Natalia
authors.initials: N
authors.lastname: Andrienko
authors.forename: Gennady
authors.initials: G
authors.lastname: Andrienko
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008377:Maps as Topic; D009068:Movement