Pattern-Driven Navigation in 2D Multiscale Visualizations with Scalable Insets

Scalable Insets is a novel technique for pattern-guided navigation in multiscale visualizations such as geographics maps, gigapixel images, or large matrices like genome interaction maps.

These slides are from my paper talk at IEEE VIS InfoVis 2019.

Paper: https://vcg.seas.harvard.edu/pubs/scalable-insets
Video: https://youtu.be/7Bn4mNLl3WQ
Code etc: https://scalable-insets.lekschas.de

Transcript

1. Fritz Lekschas @flekschas
 Harvard University, School of Engineering Michael Behrisch, Benjamin Bach, Peter Kerpedjiev,
 Nils Gehlenborg, and Hanspeter Pfister IEEE VIS
 Oct 23rd 2019 PATTERN-DRIVEN NAVIGATION
 IN 2D MULTISCALE VISUALIZATIONS
 WITH SCALABLE INSETS
2. Fritz Lekschas @flekschas
 Harvard University, School of Engineering Michael Behrisch, Benjamin Bach, Peter Kerpedjiev,
 Nils Gehlenborg, and Hanspeter Pfister IEEE VIS
 Oct 23rd 2019 PATTERN-DRIVEN NAVIGATION
 IN 2D MULTISCALE VISUALIZATIONS
 WITH SCALABLE INSETS SCALABLE INSETS
10. GOALS Identify promising regions
 for detailed exploration Find pattern instances
 within their context Compare patterns
 across scales
11. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion
12. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion
13. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion
14. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion See many insets
15. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion Close to origin
16. EFFECTIVENESS See many insets
 Close to origin Minimize occlusion Minimize occlusion
17. SCALABILITY Control inset size
 Group insets
19. GIGAPIXEL IMAGES Image tiles from The Rio—Hong Kong Connection
20. Zoom-invariant size: control details and context Dynamic leader line:
 avoid clutter
 preserve context INSET DESIGN
21. Zoom-invariant size: control details and context Dynamic leader line:
 avoid clutter
 preserve context INSET DESIGN
22. Simulated annealing Minimize:
 inset distance
 inset movement
 inset overlap
 leader-line crossing Evaluate on zoom INSET PLACEMENT
23. Clustering:
 projected distance and area in pixel space Stability:
 merge and split points differ
 avoid abrupt changes INSET GROUPING
24. Image data:
 gallery of representatives
 highlight diversity Matrices:
 pile of insets
 summary statistics INSET AGGREGATION
25. 1. User study: Performance comparison in
 frequency estimation, pattern search, and pattern comparison on gigapixel images Participants: 18 Techniques: Results: · faster pattern search
 · more accurate pattern comparison
 · little overhead for frequency estimation EVALUATION
26. 6 domain experts: free exploration + think aloud Easy-to-learn and useful: picked up in ~1min Inner placement:
 guided navigation Outer placement:
 pattern search
 & comparisons 2. USER STUDY
27. Explore sparsely-distributed local patterns in context Faster pattern search Improved navigation CONCLUSION
28. Thank You! PAPER vcg.seas.harvard.edu/pubs/scalable-insets CODE, SLIDES, ETC. scalable-insets.lekschas.de CONTACT @flekschas FUNDING CO-AUTHORS Michael Behrisch, Benjamin Bach, Peter Kerpedjiev Nils Gehlenborg, and Hanspeter Pfister is hiring!