Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

@article{Andrienko2010SpaceinTimeAT,
  title={Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns},
  author={G. Andrienko and N. Andrienko and S. Bremm and T. Schreck and T. V. Landesberger and Peter Bak and D. Keim},
  journal={Computer Graphics Forum},
  year={2010},
  volume={29}
}
Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation… Expand
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References

SHOWING 1-10 OF 59 REFERENCES
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
Visual cluster analysis of trajectory data with interactive Kohonen Maps
Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach
Visual exploration of the spatial distribution of temporal behaviors
Representations of Space and Time
...
1
2
3
4
5
...