Abstract
Our research investigates the sensitivities and complexities of visualizing multivariate data over multiple scales with the consideration of local geography. We investigate this in the context of creating geodemographic classifications, where multivariate comparison for the variable selection process is an important, yet time-consuming and intensive process. We propose a visual interactive approach which allows skewed variables and those with strong correlations to be quickly identified and investigated and the geography of multi-scale correlation to be explored. Our objective is to present comprehensive documentation of the parameter space prior to the development of the visualization tools to help explore it.
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE Conference on Visual Analytics Science and Technology |
| Editors | Min Chen, David Ebert, Chris North |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 251-252 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781479962273 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | IEEE Conference on Visual Analytics Science and Technology 2014 - Paris, France Duration: 9 Oct 2014 → 14 Oct 2014 http://ieeevis.org/year/2014/info/call-participation/vast-papers |
Conference
| Conference | IEEE Conference on Visual Analytics Science and Technology 2014 |
|---|---|
| Abbreviated title | VAST 2014 |
| Country/Territory | France |
| City | Paris |
| Period | 9/10/14 → 14/10/14 |
| Internet address |
Keywords
- D.2.2 [Software Engineering]: Design Tools and Techniques
- I.5.2 [Pattern Recognition]: Design Methodology - Feature Evaluation and Selection