Geography Reference
In-Depth Information
title visit rate was higher with cartographers. There was no difference of the dwell time
statistically proved with other observed AOI.
4.5. Space-Time-Cube
Data sets created as a result of the eye-tracking are often very large, which limits their
visualization possibilities by means of methods mentioned above. When displaying larger
data sets with the ScanPath visualization, overlapping parts are created and the follow-up
interpretation of results is not possible. The cause of this problem is displaying of three-
dimensional data (X, Y, time) into two-dimensional space (X, Y).
With relatively smaller data sets, it is possible to use colours to differentiate fixations
according to their order. During the visualization, transparency can be used to identify
overlapping fixations.
Another possible solution is to neglect the time. Two dimensional data (X, Y) can be then
displayed by means of HeatMap method which displays only the number of fixations,
without their order. However, the loss of the information can make the analysis of results
impossible in many cases.
Thanks to Space-Time-Cube (STC) which is the most important element of the Hägerstrand
time-space model, the data can be effectively visualized without neglecting any of the data
files [45].
In its basic appearance, the cube has on its base a representation of the geography (X, Y), while
the cube's height represents time (Z) [46]. As it is evident from figure 7, if the location of the
observed object or phenomenon does not change in time, the line is always perpendicular to
the base of the cube. The steeper the line between two vertices, the slower the change in the
position of observed object/phenomenon. Today there are softwares that automatically creates
a Space-Time-Cube from the data in the database. It is also important that it is possible to
interactively rotate the cube and select the best perspective for data analysis.
By means of Space-Time-Cube, it is possible to portray any space-time data. These might be
for example data recorded by a GPS device, statistic data containing information about
location and time, or data detected with eye-tracking.
In this case, coordinates X and Y describe the distribution of fixations in space, and time is
described by the axis Z. Thanks to Space-Time-Cube it is possible to reveal different
behaviour of particular users. On the other hand, ScanPath cannot identify in which
direction the user moved when reading the picture. Up to now, application of Space-Time-
Cube with analysis of eye-tracking data was examined only by [9, 47, 48].
Space-Time-Cube visualization is presented in this chapter on testing the user's perception
of the map legend. Respondents were given the task to mark flax growing areas on the map.
The aim of the test was to find out the proportion of respondents (%), which use the map
legend to fulfil the task. Trajectories of eye movements of two respondents are displayed in
figure 8. It is evident that during the first two second of solving the task, the respondents
