Attention map is a summary map of spatial distribution of user attention, which is based on discrete spatial and spatio-temporal aggregation of eye trajectories.
Attention maps represent one or more aggregate attributes (count of fixations, count of different users, average number of fixations per user, total fixation time, average fixation time per user, maximum number of repeated visits, etc.) by area shading, proportionally sized symbols, or diagrams. Attention map can be combined with flow map.
Interactions: zooming and panning; filtering of areas by attribute values; filtering of trajectories resulting in dynamic re-computing of the aggregate attributes and subsequent map update to reflect the changes (dynamic aggregation).
A display consisting of multiple attention maps can be used to represent the distribution of users' attention over the visual stimulus in different time intervals or clusters of time intervals, or for different users or user groups.
For example, the display below represents a temporal sequence of attention maps. Before aggregating the data, the trajectories have been aligned to the same start and end times; hence, we consider relative time intervals with respect to the whole duration of the task fulfillment (i.e., trajectory duration). We have divided the trajectory duration into 10 equal intervals; hence, each of the small maps represents a time interval of 10% of the task completion time. The sizes (areas) of the circle symbols represent the count of visits of the areas in each of the time intervals.
The example display below represents the attention distribution for four user groups defined according to the performance: (1) 9 fastest users; (2) 9 users from places 10-18 according to the performance; (3) 9 users from places 19-27; (4) 10 slowest users.
On an attention map or a display with multiple attention maps, it is possible to transform the values of the currently represented aggregate attribute to differences from values of another attribute. For each place, the value of the second attribute is subtracted from the value of the first attribute and the resulting difference is represented in the same way as the original attribute has been represented before. Two different colors are used to represent positive and negative differences. The sizes of the symbols or diagrams (or the darkness of the area shading) are proportional to the absolute values of the differences.
In the example below, we apply this transformation to the multiple attention map display representing summarized attention distributions of the four user groups according to the performance. The counts of the second fastest group (group 2) have been subtracted from the counts of all groups. The positive differences in move counts are shown in purple and negative in green. No symbols are visible on the map for group 2 since all the differences are zeros.
Juxtaposed attention maps (JAM)
Juxtaposed attention maps show summarized distributions of users' attention on different displays.
For example, two attention maps below represent the counts of place visits for two different trees.
Gennady Andrienko, Natalia Andrienko
A General Framework for Using Aggregation in Visual Exploration of Movement Data
The Cartographic Journal, 2010, v.47 (1), pp. 22-40
Natalia Andrienko, Gennady Andrienko, Hendrik Stange, Thomas Liebig, Dirk Hecker
Visual Analytics for Understanding Spatial Situations from Episodic Movement Data
Künstliche Intelligenz, 2012
pre-print published version: http://dx.doi.org/10.1007/s13218-012-0177-4