User study for representing the spatial data
uncertainty in land cover maps with use of
intrinsic and extrinsic methods

J...
Question about quality of outputs
•
•
•
•

Are the map true?
How about the quality of presented data?
What about the subje...
Sources of Uncertainty – data quality elements
• Lineage (description of the source material from which the data were
deri...
Is uncertainty visualisation necessary?
• Isn’t better to provide geoinformation with some kind of
uncertainty?
• Isn‘t ma...
Approach in uncertainty visualizations

Examples

Usability testing

www.geoinformatics.upol.cz
www.geoinformatics.upol.cz
www.geoinformatics.upol.cz
Representation of Uncertainty
• Presenting the data sets with different associated uncertainty

• positional accuracy
• at...
Laboratory setup
• SMI RED 250 eye-tracker with 120 Hz sampling rate
• SMI Experiment Center - design of experiment
• SMI ...
Study and experiments
• the aim of our study was to evaluate the effect of uncertainty
visualisations on eye movements and...
Testing details
• user perception of uncertainty visualizations derived from
photointerpretation of land cover classes
• m...
Delineation of uncertainty – entropy approach
• Land Facet Corridor Tools for ArcGIS

• can be used for
each map layer
• c...
Entropy calculation
• concept of entropy was applied to landcover classes

www.geoinformatics.upol.cz
Methods examples
hillshade - positive
hillshade - negative
glyphs
transparent dots - size
transparent grid
grid - width of...
Stimuli setup

www.geoinformatics.upol.cz
Stimuli setup

www.geoinformatics.upol.cz
Results
wrong answer

right answer

2
5

6

5
7

8

6

10

12
9

8

9
7

6

8

4

hillshade positive

hillshade negative

...
Results
Fixation Lenght
350

300

250

200
Mean

150

Median

100

50

0
hillshade positive

hillshade negative

glyphs

w...
Results
Number of Fixations
70

60

50

40

30

20

10

0
hillshade - positive

hillshade negative

glyphs

www.geoinforma...
Results

www.geoinformatics.upol.cz
Results
• As a top rated when compared to all methods and metrics
have been examined methods:
• transparent grid
• transpa...
Problems
•
•
•
•
•
•

small amount of respondents
respondents not domain experts
very specific task – can be domain depend...
Conclusion and future
• in our study we try to capture the uncertainty visualisation
connected with land cover classes
• s...
Conclusion and future
• same method for different studies
• more respondents
• combination of different spatial quality co...
Thank you for
your attention…

jan.brus@upol.cz

www.geoinformatics.upol.cz

The presentation has been completed within th...
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Jan Brus - User study for representing the spatial data uncertainty in land cover maps with use of intrinsic and extrinsic methods

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Jan Brus - User study for representing the spatial data uncertainty in land cover maps with use of intrinsic and extrinsic methods

  1. 1. User study for representing the spatial data uncertainty in land cover maps with use of intrinsic and extrinsic methods Jan BRUS www.geoinformatics.upol.cz
  2. 2. Question about quality of outputs • • • • Are the map true? How about the quality of presented data? What about the subjectivity? Will reader know about the positional and other errors caused by data manipulation? • Study focused on uncertainty visualisations intuitiveness www.geoinformatics.upol.cz
  3. 3. Sources of Uncertainty – data quality elements • Lineage (description of the source material from which the data were derived and the methods of derivation) • Positional accuracy (resolution of the measurement) • Attribute accuracy (both measurement accuracy and class assignment accuracy) • Logical consistency (describing the fidelity of relationships inside data structure) • Completeness (relationship between the objects represented and the abstract universe) • Currency (time currency, time relevance) • Credibility (reliability of information source, experiences) • Subjectivity (amount of human judgments in the information) • Interrelatedness (source independence) www.geoinformatics.upol.cz
  4. 4. Is uncertainty visualisation necessary? • Isn’t better to provide geoinformation with some kind of uncertainty? • Isn‘t maps (geovisualizations) with information about data uncertainty confusing? • What‘s the right/good way of uncertainty visualization? • What‘s better in a real decision process? www.geoinformatics.upol.cz
  5. 5. Approach in uncertainty visualizations Examples Usability testing www.geoinformatics.upol.cz
  6. 6. www.geoinformatics.upol.cz
  7. 7. www.geoinformatics.upol.cz
  8. 8. Representation of Uncertainty • Presenting the data sets with different associated uncertainty • positional accuracy • attribute accuracy • subjectivity • How best to represent the data? • How to best reflect reflect the uncertainty? www.geoinformatics.upol.cz
  9. 9. Laboratory setup • SMI RED 250 eye-tracker with 120 Hz sampling rate • SMI Experiment Center - design of experiment • SMI BeGaze, OGAMA, R software - data analyses • remote eye tracker most practical method of ET • illuminator/eye camera module placed below line of sight • all participants were recorded and have to speak during testing • evaluation of right and wrong answers was based on post processing of recorded video www.geoinformatics.upol.cz
  10. 10. Study and experiments • the aim of our study was to evaluate the effect of uncertainty visualisations on eye movements and performance in maprelated tasks • the study involved decision making questions where the participants were presented with several uncertainty visualisation methods based on intrinsic and extrinsic methods • finding areas with the least or most uncertainty of selected land cover class – based on intuitiveness • additive factor of the study also compared user performance with and without the use of the legend www.geoinformatics.upol.cz
  11. 11. Testing details • user perception of uncertainty visualizations derived from photointerpretation of land cover classes • maps without legend – intuitiveness of uncertainty methods • 14 participants – 8 uncertainty methods as stimuli • dependent variables were represented by following metrics derived from the analysis of eye-tracking data: – fixation duration – number of fixation – fixation count, saccade count – and more www.geoinformatics.upol.cz
  12. 12. Delineation of uncertainty – entropy approach • Land Facet Corridor Tools for ArcGIS • can be used for each map layer • combination of entropies • showing most uncertain • map algebra (Wellmann and Regenauer-Lieb, 2012 www.geoinformatics.upol.cz
  13. 13. Entropy calculation • concept of entropy was applied to landcover classes www.geoinformatics.upol.cz
  14. 14. Methods examples hillshade - positive hillshade - negative glyphs transparent dots - size transparent grid grid - width of line transparency quadtree www.geoinformatics.upol.cz
  15. 15. Stimuli setup www.geoinformatics.upol.cz
  16. 16. Stimuli setup www.geoinformatics.upol.cz
  17. 17. Results wrong answer right answer 2 5 6 5 7 8 6 10 12 9 8 9 7 6 8 4 hillshade positive hillshade negative glyphs www.geoinformatics.upol.cz transparent dots transparent grid grid - width of line transparency quadtree
  18. 18. Results Fixation Lenght 350 300 250 200 Mean 150 Median 100 50 0 hillshade positive hillshade negative glyphs www.geoinformatics.upol.cz transparent dots transparent grid grid - width of line transparency quadtree
  19. 19. Results Number of Fixations 70 60 50 40 30 20 10 0 hillshade - positive hillshade negative glyphs www.geoinformatics.upol.cz transparent dots transparent grid grid - width of line transparency quadtree
  20. 20. Results www.geoinformatics.upol.cz
  21. 21. Results • As a top rated when compared to all methods and metrics have been examined methods: • transparent grid • transparent circles • problem with implementation these methods • quantification of uncertainty based on blur or transparency • Semantic Depth of Field (Kosara, 2011) • partly method grid - width of line • and quadtree method www.geoinformatics.upol.cz
  22. 22. Problems • • • • • • small amount of respondents respondents not domain experts very specific task – can be domain depended difficult visualisation methods same area (rotated and fliped) target group mostly cartographers and geoinformatics professionals • not statistically proved • long interpretation of results from recorded video www.geoinformatics.upol.cz
  23. 23. Conclusion and future • in our study we try to capture the uncertainty visualisation connected with land cover classes • study focused more on uncertainty visualisations methods • this should bring more adequate results to uncertainty visualisation community • it is clear that uncertainty visualizations will have great importance in optimization of cartographic products and presenting geographic data in the future • comparison of different uncertainty visualization methods • proofing and confirming results from the past research www.geoinformatics.upol.cz
  24. 24. Conclusion and future • same method for different studies • more respondents • combination of different spatial quality components in one visualisation www.geoinformatics.upol.cz
  25. 25. Thank you for your attention… jan.brus@upol.cz www.geoinformatics.upol.cz The presentation has been completed within the project CZ.1.07/2.2.00/28.0078 “InDOG” which is co-financed from European Social Fund and State financial resources of the Czech Republic.

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