Nonphotorealistic Visualisation of Multidimensional Datasets
Author(s): Laura Tateosian.
Master Thesis: Graduate Faculty of North Carolina State University,
2002.
[BibTeX] 
Abstract:
The huge quantities of data that are being recorded annually need to be organized and analyzed.
The datasets often consist of a large number of elements, each associated with multiple
attributes. Our objective is to create effective, aesthetically appealing multidimensional visualizations.
By mapping element attributes to carefully chosen visual features, such visualizations
support exploration, encourage prolonged inspection, and facilitate discovery of unexpected
data characteristics and relationships.
We present a new visualization technique that uses “painted” brush strokes to represent
data elements of large multidimensional datasets. Each element’s attributes controls the visual
features of one or more brushstrokes. To pursue aesthetic appeal, we draw inspiration from the
Impressionist style of painting and apply rendering techniques from nonphotorealistic graphics.
We construct our mappings to harness the strengths of the human visual system. The resulting
displays are nonphotorealistic visualizations of the information in the datasets.
Studies confirm that existing guidelines based on human visual perception apply to our
painterly styles. Additional studies investigate the artistic appeal of our visualizations, along
with the emotional and visual features that influence aesthetic judgments. Finally, we use the
results of these studies to combine painterly styles to build a tool which creates visualizations
that are both effective and aesthetic and we apply our method to a real-world dataset.