All visualization involves transformation Raw Data Data Models Queries Arrays Visual Arrangements
The “final” transformation The visual product encodes a series of transformations from raw data to visual design A key element of this design is the use of space Space is complex—it involves the concepts of dimension, location, distance, and shape Each visualization uses these elements differently
Patterns of Transformation (i) Image Grids (aka Image Graphs) Purpose: Creates 2D qualitative space Space is uniform, Cartesian “Points” are actually not atomic, but contain content Designed to show “hot spots” Method: Identify X and Y in which to plot objects of type A Create query to generate A, X and Y columns Convert query data into 3D array $DATA[$X][$Y] = $A Convert array into HTML
Patterns of Transformation (ii) Network Graphs Purpose: Creates a network of relationships Space not uniform—distance and location of nodes require interpretation Method: Identify nodes and principle of relationship (e.g. container) Create query to generate nodes and principle Convert query into NODE and EDGE arrays Convert arrays data into Cartesian Product for each principle Convert array into PNG, SVG, etc.
Patterns of Transformation (iii) Adjacency Matrix Purpose: Creates a 2D space But X and Y are “self similar” Method: Identify X and Y Create query to generate X and Y columns Convert query data into 2D array Convert array into HTML
Patterns of Transformation (iv) Arcs and Circles Purpose: Creates a 2D dimensions, with 1 dimension metric, the other not Only an X axis with connections in qualitative space Method: Same as network graphs Visualize using Protovis library