1. The document discusses several research projects using eye tracking to study perception in geographic and cartographic domains.
2. One study examined how wind turbines impact landscape visuals and found they attract more eye fixations, indicating a strong visual impact. Expertise had no influence on viewing patterns.
3. Another study investigated how photograph properties and landscape characteristics influence how people observe landscapes. It found panoramic, open and heterogeneous landscapes elicited stronger visual exploration.
4. A third study explored search strategies for depicting time intervals in linear and triangular representations, finding triangular diagrams resulted in quicker response times and were preferred.
Track 6 - Mobile Apps and computational systems as learning tools
Authors: Manuel Á. Gonzalez, Juarez Bento da Silva, Juan Carlos Cañedo, Félix Huete, Óscar Martínez, Diego Esteban, Javier Manso, Willian Rochadel and Miguel Á. Gonzalez
https://www.youtube.com/watch?v=SULHobpnvUk&index=2&list=PLboNOuyyzZ85H9KngzY-R31GbiqFcOQbH
In February 2012 Annika Naschitzki presented to both Wellington and Auckland audiences about Optimal Usability's new eye tracker, and what it can do. Here is the presentation, however if you would like Anni to come into your organisation to do the presentation please get in touch: anni@optimalusability.com
In February 2012 Annika Naschitzki presented to both Wellington and Auckland audiences about Optimal Usability's new eye tracker, and what it can do. Here is the presentation, however if you would like Anni to come into your organisation to do the presentation please get in touch: anni@optimalusability.com
Beyond Eye Tracking: Bringing Biometrics to Usability ResearchDan Berlin
User experience research has traditionally relied upon qualitative techniques that entail users telling us their feelings, wants, and needs. This creates an inherent cognitive bias – data is filtered through the participant’s cognition. That is, we may not necessarily be hearing the participants’ true feelings. They may be trying to please the moderator or may just be unable to articulate the cause of their emotions. But researchers and stakeholders alike are thirsty for quantitative data that complements the qualitative. Luckily, we live in exciting times – there are two particular technologies that are becoming more accessible that will help usability researchers break through cognitive bias and provide that ever tantalizing quantitative data: eye tracking and biometrics. Eye tracking equipment has only recently started to become affordable to most anyone who wants to use it. Researchers must now get up-to-speed on eye tracking methodology and analysis. When is it appropriate? How can we turn the data into actionable findings? What the heck do I do with all of this new data?! More importantly, we should find new research techniques that will break through cognitive bias.
This is where the second technology comes in: biometrics. Psychophysiology is the study of how emotions affect changes in the body. Changes in heart rate, breathing rate, heart rate variability, and galvanic skin response (GSR) have all been shown to be accurate indicators of a person’s emotions, among others. Just as with eye tracking, the equipment to measure these biometrics are just now starting to become accessible to usability researchers. Until very recently, the equipment to gather this data was rather obtrusive and invasive. This not only affected participant comfort, but also did not lend to conducting “discount” usability research. But new technology allows the collection of biometrics in non-invasive ways. For instance, Affectiva’s Q Sensor is worn on the wrist and wirelessly gathers a participant’s GSR. The problem with integrating psychophysiological data into usability research is that individual researchers will need to come up with not only the algorithms to interpret the biometrics but also the technology to temporally marry the biometrics to the eye tracking data. These are no small tasks. There are companies out there that will collect and interpret the data for you for a hefty fee. But this technique should be in every usability researcher’s toolkit. As such, we should come together as a research community to figure this out. We need an open dialogue. We need to share techniques and stories.
Track 6 - Mobile Apps and computational systems as learning tools
Authors: Manuel Á. Gonzalez, Juarez Bento da Silva, Juan Carlos Cañedo, Félix Huete, Óscar Martínez, Diego Esteban, Javier Manso, Willian Rochadel and Miguel Á. Gonzalez
https://www.youtube.com/watch?v=SULHobpnvUk&index=2&list=PLboNOuyyzZ85H9KngzY-R31GbiqFcOQbH
In February 2012 Annika Naschitzki presented to both Wellington and Auckland audiences about Optimal Usability's new eye tracker, and what it can do. Here is the presentation, however if you would like Anni to come into your organisation to do the presentation please get in touch: anni@optimalusability.com
In February 2012 Annika Naschitzki presented to both Wellington and Auckland audiences about Optimal Usability's new eye tracker, and what it can do. Here is the presentation, however if you would like Anni to come into your organisation to do the presentation please get in touch: anni@optimalusability.com
Beyond Eye Tracking: Bringing Biometrics to Usability ResearchDan Berlin
User experience research has traditionally relied upon qualitative techniques that entail users telling us their feelings, wants, and needs. This creates an inherent cognitive bias – data is filtered through the participant’s cognition. That is, we may not necessarily be hearing the participants’ true feelings. They may be trying to please the moderator or may just be unable to articulate the cause of their emotions. But researchers and stakeholders alike are thirsty for quantitative data that complements the qualitative. Luckily, we live in exciting times – there are two particular technologies that are becoming more accessible that will help usability researchers break through cognitive bias and provide that ever tantalizing quantitative data: eye tracking and biometrics. Eye tracking equipment has only recently started to become affordable to most anyone who wants to use it. Researchers must now get up-to-speed on eye tracking methodology and analysis. When is it appropriate? How can we turn the data into actionable findings? What the heck do I do with all of this new data?! More importantly, we should find new research techniques that will break through cognitive bias.
This is where the second technology comes in: biometrics. Psychophysiology is the study of how emotions affect changes in the body. Changes in heart rate, breathing rate, heart rate variability, and galvanic skin response (GSR) have all been shown to be accurate indicators of a person’s emotions, among others. Just as with eye tracking, the equipment to measure these biometrics are just now starting to become accessible to usability researchers. Until very recently, the equipment to gather this data was rather obtrusive and invasive. This not only affected participant comfort, but also did not lend to conducting “discount” usability research. But new technology allows the collection of biometrics in non-invasive ways. For instance, Affectiva’s Q Sensor is worn on the wrist and wirelessly gathers a participant’s GSR. The problem with integrating psychophysiological data into usability research is that individual researchers will need to come up with not only the algorithms to interpret the biometrics but also the technology to temporally marry the biometrics to the eye tracking data. These are no small tasks. There are companies out there that will collect and interpret the data for you for a hefty fee. But this technique should be in every usability researcher’s toolkit. As such, we should come together as a research community to figure this out. We need an open dialogue. We need to share techniques and stories.
PolyZoom: Multiscale and Multifocus Exploration in 2D Visual SpacesNiklas Elmqvist
Slides from ACM CHI 2012 presentation given by Sohaib Ghani.
Abstract: The most common techniques for navigating in multiscale visual spaces are pan, zoom, and bird’s eye views. However, these techniques are often tedious and cumbersome to use, especially when objects of interest are located far apart. We present the PolyZoom technique where users progressively build hierarchies of focus regions, stacked on each other such that each subsequent level shows a higher magnification. Correlation graphics show the relation between parent and child viewports in the hierarchy. To validate the new technique, we compare it to standard navigation techniques in two user studies, one on multiscale visual search and the other on multifocus interaction. Results show that PolyZoom performs better than current standard techniques.
Presentation of Antoni Pérez, Universitat Oberta de Catalunya, Spain, for the Open Education Week's fifth-day webinar on "Researching openness – evidence-based approach " - 8 March 2019
Recordings of the discussion are available: https://eden-online.adobeconnect.com/ptwsj0d95afy/
Application overlapping user profiles to foster reflective learning at work.Angela Fessl
Reflective learning is an important activity of knowledgeworkers in order to improve future working-behaviours. The insights gained by reflective learning are based on re-experiencing and re-evaluating past working situations. One time- and cost-eective way to support reflective learning is the employment of applications that collect data about working processes, store the data in user proles, and visualise it in order to provide timely feedback to the employees. However, a single application
can only capture part of the data that might be relevant for reflection and the parallel use of several applications leads to high demands on the user regarding the interpretation of relationships between several single visualizations. A combined visualisation of data captured by dierent apps should enhance the support for reflection about the working behaviour and experiences. This paper introduces an overlapping user prole application, which combines and aggregates data captured by various applications. The goal of this overlapping application is to provide higher-level reflection possibilities by combining visualisations of
dierent application data in order to better induce and support reflective learning at work. A rst proof-of-concept of such an approach indicates that a combined user prole application and especially it's visualisations can be benecial with regard to reflective learning and can enhance the awareness about the multiple aspects of a user's work life.
PolyZoom: Multiscale and Multifocus Exploration in 2D Visual SpacesNiklas Elmqvist
Slides from ACM CHI 2012 presentation given by Sohaib Ghani.
Abstract: The most common techniques for navigating in multiscale visual spaces are pan, zoom, and bird’s eye views. However, these techniques are often tedious and cumbersome to use, especially when objects of interest are located far apart. We present the PolyZoom technique where users progressively build hierarchies of focus regions, stacked on each other such that each subsequent level shows a higher magnification. Correlation graphics show the relation between parent and child viewports in the hierarchy. To validate the new technique, we compare it to standard navigation techniques in two user studies, one on multiscale visual search and the other on multifocus interaction. Results show that PolyZoom performs better than current standard techniques.
Presentation of Antoni Pérez, Universitat Oberta de Catalunya, Spain, for the Open Education Week's fifth-day webinar on "Researching openness – evidence-based approach " - 8 March 2019
Recordings of the discussion are available: https://eden-online.adobeconnect.com/ptwsj0d95afy/
Application overlapping user profiles to foster reflective learning at work.Angela Fessl
Reflective learning is an important activity of knowledgeworkers in order to improve future working-behaviours. The insights gained by reflective learning are based on re-experiencing and re-evaluating past working situations. One time- and cost-eective way to support reflective learning is the employment of applications that collect data about working processes, store the data in user proles, and visualise it in order to provide timely feedback to the employees. However, a single application
can only capture part of the data that might be relevant for reflection and the parallel use of several applications leads to high demands on the user regarding the interpretation of relationships between several single visualizations. A combined visualisation of data captured by dierent apps should enhance the support for reflection about the working behaviour and experiences. This paper introduces an overlapping user prole application, which combines and aggregates data captured by various applications. The goal of this overlapping application is to provide higher-level reflection possibilities by combining visualisations of
dierent application data in order to better induce and support reflective learning at work. A rst proof-of-concept of such an approach indicates that a combined user prole application and especially it's visualisations can be benecial with regard to reflective learning and can enhance the awareness about the multiple aspects of a user's work life.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
OGiC - Kristien Ooms - Eye tracking in the Geo-domain: a perception on cartography, navigation and landscape design
1. EYE TRACKING IN THE GEO-DOMAIN
A PERCEPTION ON CARTOGRAPHY,
NAVIGATION AND LANDSCAPE DESIGN
Research Conducted at the Landscape & CartoGIS Research
Unit, Department of Geography, Ghent University
Kristien Ooms Fanny Van den Haute
Lien Dupont Annelies Incoul
Pieter Laseure Pepijn Viaene
Philippe De Maeyer Nico Van de Weghe Veerle Van Eetvelde
InDOG – 13-16/10/2014
Palacký University – Olomouc
2. 2
Eye tracking in the Geo-Domain
1. Visual impact of wind turbines in the landscape
• Master Thesis Fanny Van den Haute
2. The use of eye tracking in landscape perception research
• PhD Research Lien Dupont
3. Search strategies on time intervals in 1D and 2d representations
• Master Thesis Pieter Laseure
4. Comparing paper and digital maps using eye tracking
• Master Thesis Annelies Incoul
5. Influence of toponyms’ colours on their readability
• PhD Research Rasha Deeb
6. Maps, how do users see them?
• PhD & PostDoc research Kristien Ooms
7. In search of indoor landmarks
• Master Thesis and PhD research Pepijn Viaene
InDOG – 13-16/10/2014
Palacký University – Olomouc
3. VISUAL IMPACT OF WIND
TURBINES IN THE LANDSCAPE
MASTER THESIS
FANNY VAN DEN HAUTE
InDOG – 13-16/10/2014
Palacký University – Olomouc
4. 4
Research Objective & Questions
▪ Sustainable energy >> wind turbines >> spatial planning
• Appropriate in the landscape?
• Visual impact?
▪ Research Questions
• How do people look at a landscape with wind turbines?
• Is there a difference before and after placement of the wind turbines?
• Is there a difference due to personal characteristics (expertise)?
• Does the type of landscape play any role in this?
InDOG – 13-16/10/2014
Palacký University – Olomouc
5. 5
Research Objective & Questions
▪ Stimuli
• Panoramic photos
• Simulations in photoshop
• 5 different landscape types
• 60 pictures in total
• 7 seconds free viewing
• Participants
• 15 experts
• 29 non-experts
InDOG – 13-16/10/2014
Palacký University – Olomouc
6. 6
Resultaten
▪ Wind turbine
• Viewed at after avg 1,5 s
• 86,8 % eye catchers
• 86,3% longest viewings
▪ Wind turbine vs. other vertical objects
• Faster
• More and longer fixations
• Shorter first fixation
• More returned movements
InDOG – 13-16/10/2014
Palacký University – Olomouc
1. How do people look at a landscape
with wind turbines?
2. Is there a difference before and after
placement of the wind turbines?
3. Is there a difference due to personal
characteristics (expertise)?
4. Does the type of landscape play any
role in this?
7. 7
Resultaten
Eye catchers
• Type changes > wind turbine
• Viewed at faster
Fixations
• More and longer fixations
• More returned movements
• Cause: presence wind turbines
WIND TURBINES HAVE
A VISUAL IMPACT
InDOG – 13-16/10/2014
Palacký University – Olomouc
1. How do people look at a landscape
with wind turbines?
2. Is there a difference before and after
placement of the wind turbines?
3. Is there a difference due to personal
characteristics (expertise)?
4. Does the type of landscape play any
role in this?
8. 8
Resultaten
Similarity
• Type eye catcher> wind turbine
• Type longest viewed object > wind turbine
• Timing of viewings
• Number of fixations
Difference
• Experts shorter fixations
EXPERTISE HAS NO INFLUENCE
ON VIEWING PATTERN
1. How do people look at a landscape
with wind turbines?
2. Is there a difference before and after
placement of the wind turbines?
3. Is there a difference due to personal
characteristics (expertise)?
4. Does the type of landscape play any
role in this?
InDOG – 13-16/10/2014
Palacký University – Olomouc
9. 9
Resultaten
Similarity
• Timing of perceiving wind turbine
Difference
• Type eye catcher and object viewed at longest
- industrial and infrastructural landscapes
wind turbines less dominant
• Timings of eye catcher
- Woody area > hill or open rural area
TYPE OF LANDSCAPE HAS INFLUENCE
ON VIEWING PATTERN
1. How do people look at a landscape
with wind turbines?
2. Is there a difference before and after
placement of the wind turbines?
3. Is there a difference due to personal
characteristics (expertise)?
4. Does the type of landscape play any
role in this?
InDOG – 13-16/10/2014
Palacký University – Olomouc
10. THE USE OF EYE-TRACKING IN
LANDSCAPE PERCEPTION
RESEARCH
PHD RESEARCH
LIEN DUPONT
InDOG – 13-16/10/2014
Palacký University – Olomouc
11. 11
Research Questions
Which elements in a landscape catch the attention and in
which context are they most eye-catching?
Important for the location of new
infrastructures
Observer
Representation
Observations of landscapes
are influenced by…
Landscape
InDOG – 13-16/10/2014
Palacký University – Olomouc
12. 12
Research Questions
How do people observe landscapes in general?
• Influence of the photograph properties?
‒ Focal length, horizontal and vertical view angles
• Influence of the landscape characteristics?
‒ Degree of openness
‒ Degree of heterogeneity
• Influence of the social/professional background of the observer?
‒ Landscape experts versus novices
• Influence of type of landscape?
‒ Degree of urbanisation
‒ Landscape experts versus novices
‒ Predict viewing pattern?
Experiment 3 Experiment 2 Experiment 1
InDOG – 13-16/10/2014
Palacký University – Olomouc
13. 13
Study design – Experiment 1
Photograph sampling
Focal
length
18 landscapes
Horizontal
view angle
90 photographs in total
Vertical
view
angle
a) Panoramic
photograph
50mm 70° 20,9°
b) Standard
photograph
50mm 31° 20,9°
c) Zoom 1 70mm 22,4° 15°
d) Zoom 2 100mm 15,8° 10,5°
e) Wide angle
photograph
18mm 75,1° 54,3°
23 participants (geographers)
InDOG – 13-16/10/2014
Palacký University – Olomouc
14. 14
Enclosed Semi-open Open
Homogeneous Heterogeneous
90 photographs in total
21 landscape expert participants
23 novice participants
InDOG – 13-16/10/2014
Palacký University – Olomouc
15. 15
Study design – Experiment 2&3
21 landscape expert participants
InDOG – 13-16/10/2014
Palacký University – Olomouc
74 photographs,
differing in degree of
urbanisation
21 novice participants
19. 19
1050 x 1680 matrices
Saliency map
Focus map
Correlation between focus maps and saliency maps?
InDOG – 13-16/10/2014
Palacký University – Olomouc
20. 20
Results experiment 3
▪ Significant effect of landscape type,
▪ No effect of expertstatus, no significant interaction
▪ Non-experts’ viewing pattern is a little more predictable
InDOG – 13-16/10/2014
Palacký University – Olomouc
21. SEARCH STRATEGIES ON TIME
INTERVALS IN 1D AND 2D
REPRESENTATIONS
MASTER THESIS
PIETER LASEURE
InDOG – 13-16/10/2014
Palacký University – Olomouc
22. 22
Research Objective
Evaluate added value of the
Triangular Model
to depict time intervals, compared to the ‘traditional’
Linear Model
InDOG – 13-16/10/2014
Palacký University – Olomouc
23. 23
Relevance and Research Questions
▪ Importance in education:
“How to depict temporal information most efficiently?”
▪ Research Questions:
• Is the TM a clearer / more efficient model than the LM?
• Do males and females search differently in these models?
• Do students and experts search differently in these models?
• Can we distinguish differences in the users search strategies; TM vs. LM?
InDOG – 13-16/10/2014
Palacký University – Olomouc
24. 24
Study Design
LM TM
25 novice participant; some removed
3 expert participants
8 stimuli & questions for LM
8 stimuli & questions for TM
Similar questions
Mixed
Alternate
Quantitative analyses
Response time
Score
Fixation duration
Saccadic length
Qualitative analyses
InDOG – 13-16/10/2014
Palacký University – Olomouc
25. 25
Results: Quantitative
Students’ response time
Students’ nr of fixations per second
InDOG – 13-16/10/2014
Palacký University – Olomouc
Participants’ preference and score attributed to the models
GROUP nr
AVG. SCORE
LM
AVG. SCORE
TM
PREFERENCE
Students 25 5,48/10 8,3/10 TM (25/25)
Experts 3 4,75/10 8/10 TM (3/3)
Students’ fixation duration
Students’ saccadic length
Students’ score
29. COMPARING MAP READING ON
PAPER AND DIGITAL MAPS
MASTER THESIS
ANNELIES INCOUL
InDOG – 13-16/10/2014
Palacký University – Olomouc
30. 30
Introduction
▪ Paper versus digital maps
▪ Drawbacks of digital maps:
• Resolution
• Colour ranges
• Dimensions
▪ Same information displayed differently
▪ Eye tracking
• Register the users’ eye movements (Point of Regards, POR)
• Users’ cognitive process
compare the users’ attentive behaviour
InDOG – 13-16/10/2014
Palacký University – Olomouc
31. 31
Study Design
▪ Participants
• 32 Master students or researchers
• Department of Geography, Ghent University
• Similar domain knowledge in geography and cartography
• Familiar with the design of the Belgian topographic maps
▪ Stimuli
• 6 topographic maps on 1 : 10 000
• Regions in the Southern part of Belgium
• Two similar groups of participants
• Three paper and three digital maps (alternately)
InDOG – 13-16/10/2014
Palacký University – Olomouc
32. 32
Study Design
▪ Task
• Visual search
• Locate three labels in the map image
• Questionnaire
- Background information
- Familiarity with the depicted regions
- Search strategy
▪ Apparatus and Set-up
• Eye tracker: SMI RED system 120Hz
• 50 inch television screen
• Stand alone mode
InDOG – 13-16/10/2014
Palacký University – Olomouc
33. 33
Methodology
▪ Data selection
• Calibration accuracy: < 1°
• Tracking ratio: > 85%
• Visual verification
• Shift correction
- At least 10 individuals for each stimulus
- In total: 25 participants
- 68 paper and 70 digital stimuli
Part. 1D 2P 3D 4P 5D 6P Part. 1P 2D 3P 4D 5P 6D
P01 x x x x x x P10 x x x x x
P05 x x x x x x P14 x x x x x
P07 x x x x P16 x x x x x x
P09 x x x x x x P18 x x x x x x
P11 x x x x x x P20 x x x x x x
P13 x x x x x x P22 x x x x
P15 x x x x x x P24 x x x x x x
P17 x x x x x x P28 x x x x x
P21 x x x x x P30 x x x x x x
P25 x x x x x x P32 x x x x x
P27 x x x x P34 x x x x x x
P29 x x x x x x P36 x x x x x x
P33 x x x x x
TOT. 13 11 12 12 12 12 TOT. 10 10 11 11 12 12
InDOG – 13-16/10/2014
Palacký University – Olomouc
34. 34
Methodology
▪ Creating the gridded visualisation
• Areas Of Interest (AOIs)
• Fixation counts and distribution
• Grid of 32 x 22 cells
• AOIs of 40 x 40 pixels
InDOG – 13-16/10/2014
Palacký University – Olomouc
35. paper digital paper digital paper digital paper digital
35
Results
Mean search times
(P = 0.956 > 0.05)
InDOG – 13-16/10/2014
Palacký University – Olomouc
Fixations per second
(P < 0.000)
Digital maps were less difficult
to interpret than paper maps
Mean fixation duration
(P = 0.210 > 0.05)
Shorter saccades digital maps
1
2
3
4
5
6
1
2
3
4
5
6
Fixation count Fixation duration
36. 36
Conclusion & Future Work
▪ Users’ attentive behaviour on paper and digital maps
▪ Controlled study design
▪ No unidirectional conclusions concerning efficiency
▪ Distribution of the fixations was similar
▪ No real-life situations:
• Generally, digital maps are presented on smaller screens
▪ Further research, taking into account (digital maps):
• Different screen sizes
• Interaction tools
• Specific design
InDOG – 13-16/10/2014
Palacký University – Olomouc
37. INFLUENCE OF TOPONYMS’
COLOURS ON THEIR
READABILITY
PHD RESEARCH
RASHA DEEB
InDOG – 13-16/10/2014
Palacký University – Olomouc
38. 38
Research Context
▪ Typography on maps
• Semiotics according to Bertin
• Bold, italic, shape (font), orientation, etc.
▪ Preference?
▪ Efficiency?
▪ Lettering system?
▪ Colour?
InDOG – 13-16/10/2014
Palacký University – Olomouc
39. 39
Research Questions
▪ Influence of complementary colors (background-label) on the
users’ search efficiency;
▪ Is this further influenced by the user’s characteristics
(gender and expertise)
▪ Are the users’ preference and search efficiency linked?
▪ The findings are compared to the ‘traditionally’ black labels
InDOG – 13-16/10/2014
Palacký University – Olomouc
42. 42
Results
Users’ responses (s) between black and colored labels
Map
Number
(M= Mean, SD= Standard Deviation).
Black Color
F P
M SD M SD
1 15.932 10.603 20.955 15.622 2.077 0.155
2 20.252 21.420 13.672 10.090 2.217 0.142
3 18.075 13.104 17.174 13.829 0.069 0.793
4 14.972 22.713 17.785 14.344 0.319 0.574
5 13.814 14.905 18.299 21.648 0.089 0.766
6 23.342 198.80 32.562 38.221 1.328 0.254
7 20.653 14.476 14.876 13.489 2.476 0.122
8 14.511 12.934 14.822 13.136 0.009 0.927
9 13.501 11.750 18.277 13.847 2.144 0.148
10 16.589 12.404 20.589 12.404 1.300 0.259
11 26.218 25.308 16.940 12.609 0.179 0.674
12 14.560 10.138 35.918 38.613 8.314 0.006
InDOG – 13-16/10/2014
Palacký University – Olomouc
MANOVA tests
Only map number (labels’ colour) significant
Source df
Reaction Time(s)
Fixation Duration
(s)
Fixation count
(Fix/s)
F P F P F P
Corrected Model 117 2.079 0.000 2.240 0.000 1.518 0.001
Intercept 1 354.591 0.000 535.231 0.000
3343.52
0
0.000
Map number 23 4.519 0.000 2.756 0.000 1.930 0.000
Expertise 1 1.361 0.244 0.055 0.814 0.185 0.667
Gender 1 0.996 0.370 0.037 0.964 0.290 0.748
Map number * Expertise 23 1.000 0.463 0.105 1.000 0.878 0.629
Expertise * Gender 1 0.009 0.925 1.024 0.312 0.082 0.775
Map number * Gender 44 1.037 0.410 0.244 1.000 0.679 0.944
Map number * Expertise *
23 0.605 0.927 1.033 0.420 0.706 0.842
Gender
43. 43
Results
▪ Colour difference
ΔE*ab= {(ΔL*)2+(Δa*)2+(Δb*)2}1/2 where: ΔL*= L foreground* - L background*;
Δa*= a foreground* -a background*;
Δb*= b foreground* -b background*.
InDOG – 13-16/10/2014
Palacký University – Olomouc
Colour difference vs. average fixation count per second
44. 44
Results
▪ Luminance difference
ΔY= Y foreground –Y background
calculated from the measured Y-value in the XYZ-system
InDOG – 13-16/10/2014
Palacký University – Olomouc
luminance difference vs. the target fixation duration
45. MAPS,
HOW DO USERS SEE THEM?
PHD & POSTDOC RESEARCH
KRISTIEN OOMS
InDOG – 13-16/10/2014
Palacký University – Olomouc
46. 46
Maps are … a medium to communicate
Research Aims:
How do map users
Read
Interpet
Store
Retrieve
information on
digital cartographic
products?
Advice for design
(syntax, semiotics)
of digital
cartographic
products:
Guidelines
Implement in online
tools
...
InDOG – 13-16/10/2014
Palacký University – Olomouc
47. 47
Maps are … visual
Eye Tracking
• Evaluate maps: UCD
- Log users’ Point of Regard
∙ Location
∙ Duration
∙ …in screen-coordinates (px)
- Combination with other methods
∙ Reaction time measurements
∙ Thinking alound
∙ Sketch maps
∙ Questionnaires
∙ …
InDOG – 13-16/10/2014
Palacký University – Olomouc
48. 48
User studies
▪ PhD Research
Basic map design
Expert vs. novices
Label placement
InDOG – 13-16/10/2014
Palacký University – Olomouc
original
view
total-design
border-design
49. 49
User studies
▪ PhD Research
Complex map design
Expert vs novices
Adaptations in symbology
Mirroring of map objects
....
InDOG – 13-16/10/2014
Palacký University – Olomouc
50. 50
Maps are … interactive
• ‘Maps on the Internet/Web’
• Typical user interactions
- Panning
changing extent
- Zooming
changing scale & extent
• Influence on users’ cognitive processes?
Read
Interpet
Store
Retrieve
Benifical for user?
e.g. memory, change blindness, …
InDOG – 13-16/10/2014
Palacký University – Olomouc
51. 51
Eye Tracking & Interactivity?
▪ Georeferencing eye movement data
Changing point of
origin
Applying map
projection formula
Spherical Mercator
(inverse)
휆 = 휆0 +
푥
푅
휑 = 2 푡푎푛−1 푒푥푝
푦
푅
−
휋
2
InDOG – 13-16/10/2014
Palacký University – Olomouc
52. 52
Case Study
▪ Three eye tracking systems
• SMI RED 250
• Tobii T120
• SR Research EyeLink 1000
Panning
InDOG – 13-16/10/2014
Palacký University – Olomouc
53. 53
Case Study
▪ Three eye tracking systems
InDOG – 13-16/10/2014
Palacký University – Olomouc
Panning
54. 54
Evaluation of panning in Google Maps
▪ Alteration map - satellite view
▪ Panning along a route
• Zoom level 13
▪ Find Belgium
• Zoom level 7
InDOG – 13-16/10/2014
Palacký University – Olomouc
55. 55
Future Work
▪ Zooming?
• In theory: same concept, only change in R value
• Logging change in zoom levels
- Scroll wheel…
▪ Other map projections?
• In theory: same concept, only change in map projection formula
• Example: Google Earth
- Spherical General Perspective Azimuthal projection
InDOG – 13-16/10/2014
Palacký University – Olomouc
56. IN SEARCH OF INDOOR
LANDMARKS
MASTER THESIS & PHD RESEARCH
PEPIJN VIAENE
InDOG – 13-16/10/2014
Palacký University – Olomouc
57. 57
Introduction
▪ What is a landmark?
= a wayfinding tool
a location or a direction
view-action pair
▪ How to identify a landmark?
• Asking observers
picture based object recognition, verbal protocols,
verbal eye-catcher detection, Wizard of Oz Prototyping,
picture based object description ...
• Quantifying
= object + saliency
» Visual – Semantic – Structural
InDOG – 13-16/10/2014
Palacký University – Olomouc
60. 60
Study Design
InDOG – 13-16/10/2014
Palacký University – Olomouc
61. 61
Results
41 % Referral to a landmark
59 % No referral to a landmark
InDOG – 13-16/10/2014
Palacký University – Olomouc
62. 62
Results
= [59]
≠ [73]
Ø [89]
eye tracking
DP landmark category object landmark
1 door (route) grey double door
2 other / route indicator exhibition display
3 route indicator sign (“Geography”)
4 door (route) brown double door
5 window window and view
6 door (route) / other pair of sticks / car batteries
7 door (route) brown doors with windows
8 ornament big plant
9 elevator red elevator
10 poster wooden information board
11 door (other) grey double door
12 door (other) glass main entrance
13 route indicator / other sign (“Paleontology”)
14 door (other) brown double door
15 window / route indicator window and view
16 door (route) brown double door
17 door (route) / poster single door
thinking
aloud
InDOG – 13-16/10/2014
Palacký University – Olomouc
63. 63
Conclusion
For the identification of (indoor) landmarks
eye tracking can provide qualitative and complete data,
in addition verbal protocols can clarify specific fixations.
InDOG – 13-16/10/2014
Palacký University – Olomouc
65. 65
Future Plans
▪ Evaluation of the school’s textbooks
▪ Evaluation of the new 25K symbology
• Together with
• 1 : 20 000 1 : 25 000
• Paper maps, over whole Belgium
InDOG – 13-16/10/2014
Palacký University – Olomouc
66. 66
Future Plans
▪ Evaluation of Neogeography maps
▪ Evaluation of maps on different devices
• Touch-interactions
InDOG – 13-16/10/2014
Palacký University – Olomouc
67. THANK YOU FOR YOUR ATTENTION
QUESTIONS?
Fanny.
VandenHaute
@UGent.be
Lien.Dupont
@UGent.be
PieterLaseure
@hotmail.com
Annelies.Incoul
@UGent.be
Rasha.Deeb
@UGent.be
Kristien.Ooms
@UGent.be
Pepijn.Viaene
@UGent.be
Editor's Notes
De cursus GI Platform speelt in op de huidige trends in de GIS-wereld en nog meer op de noden en behoeften van vele bedrijven.
Net zoals het bedrijf waar ik als consultant werk: GEO Solutions.
De cursus GI Platform speelt in op de huidige trends in de GIS-wereld en nog meer op de noden en behoeften van vele bedrijven.
Net zoals het bedrijf waar ik als consultant werk: GEO Solutions.
>Correlation between heat map column and saliency map column to check how close the viewing pattern of the participant is to the predicted saliency map
>Correlations of experts and non-experts are compared
>Correlations of different groups of landscapes are compared
De cursus GI Platform speelt in op de huidige trends in de GIS-wereld en nog meer op de noden en behoeften van vele bedrijven.
Net zoals het bedrijf waar ik als consultant werk: GEO Solutions.
De cursus GI Platform speelt in op de huidige trends in de GIS-wereld en nog meer op de noden en behoeften van vele bedrijven.
Net zoals het bedrijf waar ik als consultant werk: GEO Solutions.
De cursus GI Platform speelt in op de huidige trends in de GIS-wereld en nog meer op de noden en behoeften van vele bedrijven.
Net zoals het bedrijf waar ik als consultant werk: GEO Solutions.
De cursus GI Platform speelt in op de huidige trends in de GIS-wereld en nog meer op de noden en behoeften van vele bedrijven.
Net zoals het bedrijf waar ik als consultant werk: GEO Solutions.
De cursus GI Platform speelt in op de huidige trends in de GIS-wereld en nog meer op de noden en behoeften van vele bedrijven.
Net zoals het bedrijf waar ik als consultant werk: GEO Solutions.
Let us start with the function of a landmark. A landmark is a wayfinding tool that either specifies a specific location or a certain direction. For example, coming from the left, I can say at the green landmark go right. Or I can say, go towards the green landmark, coming from top. Furthermore, these examples also indicate that a landmark is normally part of a view-action pair, meaning that the location or direction is linked to a specific action. As part of these view-action pairs, landmarks form an essential part of route instructions.
In order to examine whether or not eye tracking can provide us relevant information with respect to the identification of landmarks, we compared the eye tracking data with verbalisation protocols.
These protocols were examined by using two variants of thinking aloud: concurrent thinking aloud, whereby people had to verbalise their thoughts while navigating through the task, and cued retrospective think aloud. Here participants watched a recording of their (second) traversal of a route in the building on which the eye fixation where also displayed. This video and eye locations serve as a cue that may trigger them to provide useful information. The reason why we used CRTA is to detect possible reactivity caused by the additional task of verbalising during CTA.
The verbal protocols were analyzed by using Elan Eudico Linguistic annotator.
The eye tracking data was analyzed by using BeGaze to transfer all fixations to a reference image.
All participants completed a route that comprised various floor levels and zones in the building twice. The first time they followed the experimenter, the second time they were asked to complete the route independently.
In total 9 participants participated. 4 or them applied CTA, which resulted in 8 recordings because they had to complete the route twice. 5 applied CRTA, which resulted in 5 recordings because the watched the video after the second traversal of the route
The verbal protocols related to each recording were split into verbalisation segments. Each segment was for example one landmark referral, one explanation, one silence.
This reference image depicted several structural and object landmarks.
Hallway, staircases, rooms
Doors, windows, closets, fire fighting equipment, lights, garbage can, posters, radiator
Server, elevator, ornament, unclear fixation, other, people
Name sign (office), evacuation plan, route indicator, emergency signs (pictogram), written emergency signs
After collecting the needed information, we compared each verbal segment with the eye fixation at (approximately) the same time.
41 % of the verbalisation segments comprised a referral to a landmark, either structural or an object. In 70 % of those cases this was reflected in the eye fixations. In almost 18 % of the cases, there was also a match, but it was impossible to tell on what object a person fixated on without the context offered by the verbalisations. For example, when fixating on a wall, the verbalisations clarified that the colour of the wall was considered to be salient. In total, 13 % of the verbalisations were not reflected in the eye fixations. Often because people referred to objects that they had encountered earlier or are expected to encounter in other rooms.
When looking at the verbalisation segments that did not refer to a potential landmark and as such was not reflected in the eye tracking data, it becomes clear that these segments mostly comprise non relevant information as silences and random verbalisations that are not related to the wayfinding experience. However, 14 % of the segments comprised information that told us more about certain navigational difficulties or were explanations why a certain wayfinding action was executed or why certain objects were perceived salient.
In a next step, we determined for each decision point what the landmark category was that was most fixated on in terms of number of fixation and duration of these fixations. At this stage we only focussed on the object landmark categories, because we noticed that a fixation on for example a staircase is difficult to interpret. Do they see the staircase of are they just paying attention where they are placing their feet so they don’t fall or step on something.
Furthermore, it became apparent that in most of the times a single object was responsible for this rise in fixations.
Then we checked whether or not these object landmarks were mentioned at the specific decision point. 59 times this was the case. 73 times they mentioned other landmarks (43 of them were structural landmarks, mostly staircases). Finally, in 89 of the cases the participant did not mention any potential landmark.
Nonetheless, based on the earlier mentioned reasons (slide 4) we come to the following conclusion. [...] As such, verbal protocols can offer more context in support of eye tracking but should not be the subject of time consuming analysis.
De cursus GI Platform speelt in op de huidige trends in de GIS-wereld en nog meer op de noden en behoeften van vele bedrijven.
Net zoals het bedrijf waar ik als consultant werk: GEO Solutions.