Visual Perspectives iPLANT Visual Analytics Workshop November 5-6, 2009 ;lk Visual Analytics Bernice Rogowitz Greg Abram

Visual Perspectives Visual Representation of Data Rene Descartes (1596 – 1650) Values of X Values of Y Insight: Represent Magnitude as a Distance

Visual Perspectives Visualization: Mapping Data onto Visual Dimensions Many visual dimensions -Lines, glyphs, -Color, grayscale -Depth, texture -Motion, 3D Monte Carlo Risk Analysis Data

Visual Perspectives Four Visualizations of the Same Data Which is “correct” ? -- depends on the data, the task and the domain. Monte Carlo Risk Analysis Data

Visual Perspectives The Rainbow Color Map Data Value (Z)

Visual Perspectives Why Perception Matters In the standard, default “Rainbow” color map, equal steps in the magnitude of the data are not perceived as equal steps Rogowitz and Treinish, IEEE Spectrum 1998 “The End of the Rainbow”

Visual Perspectives Color Perception Experiments test the degree to which different trajectories in 3-D color space convey magnitude information

Visual Perspectives

Using Color to draw attention and mark semantic regions

Visual Perspectives Using Color to highlight semantics

Visual Perspectives … a closer look

Visual Perspectives Interactive Visual Exploration Using Color “Brushing” to help reveal linkages Year pop dji auto housing prime economy helps users explore features in high-dimensional data Diamond

Visual Perspectives Parametric Snake Plot Parallel Coordinates Animated 3-D Scatterplot Fractal Foam Dynamic linking (“brushing” between different data representations)

Visual Perspectives Many different types of data…. CCA CGAGTA CAA C CGA GTA CCCAA ATGAACACCCAA AAACCCATGATG CACAACAACACC CGA ATGAGACCC AACACCACCAAC CACCCATGA CGA AACACCGAGAAA AT GTACACCCAG time series numerical categorical field image sequence 3-D geometry text GIS graph

Visual Perspectives Visualizing Patterns across data types

Visual Perspectives Example: Finite Element Heart Excitation Model  3D computational model for investigating heart disease.  150,000 nodes.  Multiple simulation parameters at each node, 60 time steps. Gresh, Rogowitz, Winslow, et al, 2000 Winslow, et al, 2000 Collaboration with Johns Hopkins University

Visual Perspectives Interactive Data Exploration, linking numerical parameters and 3-D geometric representation LARGE PEAK AT ZERO

Visual Perspectives Interactive Data Exploration, linking numerical parameters and 3-D geometric representation COLOR ONLY DATA ABOVE THE PEAK

Visual Perspectives Interactive Data Exploration, linking numerical parameters and 3-D geometric representation SHOW ONLY COLORED POINTS

Visual Perspectives Interactive Data Exploration, linking numerical parameters and 3-D geometric representation COLOR PEAKS DIFFERENTLY

Visual Perspectives Visualization for Visual Analysis  Judgments (Tasks) Magnitude of a variable or set of variable Correlations, trends over timeTrends over time Interaction effects Patterns Connections and relationships Outliers  User Actions View a static representation Browse (pan, zoom, select, rotate) Filter Explore relationships within a data set; across different data sets Identify semantic regions of interest, and explore the behavior of that subset, across representations – “brushing” View over time Transform variables, create new variables Tag and annotate Integrated analysis and visualization of analysis

Visual Perspectives Visualization and Visual Analysis Framework Infrastructure Array3DImage Sequence TablesVideoText Operations, Functions, Tools (visualization, mathematical libraries, analytical methods) User Interactions Analytical Judgments Communication “Workflow” is a path through this hierarchy Different workflows for different users (personas) Flexible re-use and re-parameterization of functions for different use cases Extensibility (standard APIs, pre-established hooks, metadata)

Visual Perspectives Bernice’s Web page – Visualization in Plant Genetics  Please let me know if there are other sites or examples I should include  visualization visualization