Popelka, S: Space-Time-Cube for Visualization of Eye-tracking data

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Popelka, S: Space-Time-Cube for Visualization of Eye-tracking data

  1. 1. Space-Time-Cube for Visualization of Eye-tracking data Stanislav POPELKAThis presentation is co-financed by theEuropean Social Fund and the statebudget of the Czech Republic
  2. 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
  3. 3. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  4. 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. 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. 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. 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. 8. Space-Time-Cube First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  9. 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. 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. 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. 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
  13. 13. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  14. 14. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  15. 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
  16. 16. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  17. 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
  18. 18. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  19. 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
  20. 20. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  21. 21. Videa First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  22. 22. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  23. 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. 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

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