Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Jan Brus - User study for representing the spatial data uncertainty in land cover maps with use of intrinsic and extrinsic methods
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. Question about quality of outputs
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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. 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. 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
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. 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. 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. 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. 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
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. Problems
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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. 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. Conclusion and future
• same method for different studies
• more respondents
• combination of different spatial quality components in one
visualisation
www.geoinformatics.upol.cz
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.
