1. Space-Time-Cube
for Visualization of Eye-tracking data
Stanislav POPELKA
This presentation is co-financed by the
European Social Fund and the state
budget of the Czech Republic

2. Introduction
 Eye-tracking is one of the methods of usability studies and is
considered as an objective
 The modern eye-trackers use contactless measurements of the
visible parts of the eye and corneal reflection of direct beam
of infrared light
 The reflected light is recorded by camera
 From analysis of the changes of corneal reflection, the point of
regard is calculated
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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4.  The human eye performs several types of movement - the
most important are fixations and saccades
 Qualitative information about eye movements describes the
way in which the user explores the stimulus
 It can reveal areas of greatest interest, disruptive elements or search
tactics during answering the question
 Quantitative information can be derived from eye-tracking
data through metrics of fixation and saccades
 For example - the fixation length, saccade amplitude, fixation/saccade
ratio or AOI (Area of Interest) dwell time
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

5. Visualization of eye-tracking data
 HeatMaps and ScanPaths are common visualization methods
of eye-tracking data
 They cannot effectively express the change of time
 The cause of this problem is displaying of the three-
dimensional data (X, Y, time) in two-dimensional space (X, Y)
 It is necessary to use spatio-temporal visualization
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

6. Spatio-temporal visualization
 Kraak and Ormeling (1996) describes three approaches to space-
time visualization
 Simple static map (ScanPath)
 If the static map displays complex time phenomena, it is likely that
the phenomena will overlap in the map, which can lead to loss of
information.
 Series of static maps (ScanPaths)
 It allows viewing changes in a phenomenon in several time periods
 For huge series of static maps, the interpretation should be difficult
 Animation (of ScanPaths)
 Animation captures the dynamics of the space-time phenomenon
appropriately
 Displaying of the development of the phenomenon comprehensively
for the entire study period is not possible
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

7. Space-Time-Cube
 Space-Time-Cube displays spatial and temporal
component at the same time
 Space–Time–Cube is the most important element in
the Hägerstrand’s spatio-temporal model
 Space-Time-Cube displays the map at the base of the
cube (axes X and Y) while Z axis is used to represent
time
 Spatial and temporal components are shown
together, and relationship between space and time
can be revealed
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

8. Space-Time-Cube
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

9. Software for STC visualization
 Visual Analytics Toolbox (CommonGIS)
 Developed by Fraunhofer Institute IAIS
 Gennady and Natalia Andrienko
 GeoTime 5
 Commercial application for Space-Time-Cube
 Designed for geographical data
 Direct import from ArcGIS
 Space-Time-Cube extension for open-source GIS uDig
 ITC in Eschende, Netherlands
 Team around professor Jan-Menno Kraak
 Unavailable at the moment
 Extended Time-Geographic Framework Tools
 Extension for ArcGIS 9.3
 compared to the above mentioned applications, this extension has less
functionality
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

10. Case study
 Research of map reading during solving the geographic problems
 Study was focused to the use of map legend
 Students project – students of Masters program
 Total of 16 respondents
 8 cartographers (KGI students after cartography course)
 8 non-cartographers (zoologists, lawyers..)
 Total of 19 stimuli
 Maps from school atlases
 Unlimited time to read the question
 45 seconds to answer
 Short questionaire after the test
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

11. Case study
 During the research, SMI RED 250 eye-tracker developed by
SensoMotoric Instrument was used
 The device allows data acquisition with frequency of 120 Hz
 Point of regard of the eye, expressed with Y and Y coordinates
are recorded and stored with a regular interval of 8
miliseconds
(X, Y, t)
X – location
Y – location
t – time
…
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

12. Results of case study
 Respondents automatically looks into the legend first
 Regardless belonging to the Cartographer/NonCartographer
group
 If the legend is barely legible, they spend much more
time in it
 One of the stimuli was a gimmick – respondents were
asked to find the coal mine in the map, but the symbol
was missing in the legend
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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15. Space-Time-Cube example from the case study
 Space-Time-Cube was used for visual analysis of users
interaction with maps from school atlases
 Respondents were asked to quickly find areas where
the flax is grown and identify it by clicking the mouse
in the map
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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17. Space-Time-Cube in Common GIS
 Data modification is necessary before importing into
CommonGIS
 Two types of visualization of measured data in a Space-
Time-Cube have been tested
 Visualization of trajectory made directly from raw data
 Visualization of fixations connected with lines (representing
saccades)
 CommonGIS has not an ability to differ fixations based on
their size
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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19. Space-Time-Cube in GeoTime
 Occulus GeoTime 5 is the software for visual analysis of
time series data
 Possibility of connecting GeoTime with ArcMap or
Microsoft Excel
 Fixations can differ based on their lenght
 Geotime has a possibility to analyze data – find
patterns, clusters, gaps, intersections in space and time…
 Problems with coordinate system, units
 Geotime is designed for geographical data (WGS 84)
 Eye-tracker produce data in Cartesian coordinate system
(1680*1050 px)
 GeoTime is unable to load data in miliseconds
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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21. Videa
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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23. Future plans
 Solve the problems with GeoTime
 Use its functions for analysis
 Use Space-Time-Cube for visual analysis of
results from next eye-tracking tests
 Comparison of 2D vs. 3D visualization
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

24. Thank you for your attention
Stanislav Popelka
standa.popelka@gmail.com
www.geoinformatics.upol.cz/ET
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc