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EYE TRACKING IN THE GEO-DOMAIN 
A PERCEPTION ON CARTOGRAPHY, 
NAVIGATION AND LANDSCAPE DESIGN 
Research Conducted at the L...
2 
Eye tracking in the Geo-Domain 
1. Visual impact of wind turbines in the landscape 
• Master Thesis Fanny Van den Haute...
VISUAL IMPACT OF WIND 
TURBINES IN THE LANDSCAPE 
MASTER THESIS 
FANNY VAN DEN HAUTE 
InDOG – 13-16/10/2014 
Palacký Unive...
4 
Research Objective & Questions 
▪ Sustainable energy >> wind turbines >> spatial planning 
• Appropriate in the landsca...
5 
Research Objective & Questions 
▪ Stimuli 
• Panoramic photos 
• Simulations in photoshop 
• 5 different landscape type...
6 
Resultaten 
▪ Wind turbine 
• Viewed at after avg 1,5 s 
• 86,8 % eye catchers 
• 86,3% longest viewings 
▪ Wind turbin...
7 
Resultaten 
 Eye catchers 
• Type changes > wind turbine 
• Viewed at faster 
 Fixations 
• More and longer fixations...
8 
Resultaten 
 Similarity 
• Type eye catcher> wind turbine 
• Type longest viewed object > wind turbine 
• Timing of vi...
9 
Resultaten 
 Similarity 
• Timing of perceiving wind turbine 
 Difference 
• Type eye catcher and object viewed at lo...
THE USE OF EYE-TRACKING IN 
LANDSCAPE PERCEPTION 
RESEARCH 
PHD RESEARCH 
LIEN DUPONT 
InDOG – 13-16/10/2014 
Palacký Univ...
11 
Research Questions 
Which elements in a landscape catch the attention and in 
which context are they most eye-catching...
12 
Research Questions 
 How do people observe landscapes in general? 
• Influence of the photograph properties? 
‒ Focal...
13 
Study design – Experiment 1 
 Photograph sampling 
Focal 
length 
18 landscapes 
Horizontal 
view angle 
90 photograp...
14 
Enclosed Semi-open Open 
Homogeneous Heterogeneous 
90 photographs in total 
21 landscape expert participants 
23 novi...
15 
Study design – Experiment 2&3 
21 landscape expert participants 
InDOG – 13-16/10/2014 
Palacký University – Olomouc 
...
16 
Methodology 
 Eye tracking technology 
• Non-portable RED-system (SMI) 
 Eye tracking experiments 
• Random order 
•...
17 
Results – Experiment 1 
Panoramic Open 
 More fixations 
 Shorter saccades 
More information extraction 
 Shorter...
18 
InDOG – 13-16/10/2014 
Palacký University – Olomouc 
Expert Novice 
More 
fixations & 
saccades 
Less 
fixations & 
sa...
19 
1050 x 1680 matrices 
Saliency map 
Focus map 
Correlation between focus maps and saliency maps? 
InDOG – 13-16/10/201...
20 
Results experiment 3 
▪ Significant effect of landscape type, 
▪ No effect of expertstatus, no significant interaction...
SEARCH STRATEGIES ON TIME 
INTERVALS IN 1D AND 2D 
REPRESENTATIONS 
MASTER THESIS 
PIETER LASEURE 
InDOG – 13-16/10/2014 
...
22 
Research Objective 
Evaluate added value of the 
Triangular Model 
to depict time intervals, compared to the ‘traditio...
23 
Relevance and Research Questions 
▪ Importance in education: 
“How to depict temporal information most efficiently?” 
...
24 
Study Design 
LM TM 
 25 novice participant; some removed 
 3 expert participants 
 8 stimuli & questions for LM 
...
25 
Results: Quantitative 
Students’ response time 
Students’ nr of fixations per second 
InDOG – 13-16/10/2014 
Palacký U...
26 
Results: Qualitative 
InDOG – 13-16/10/2014 
Palacký University – Olomouc
27 
Results: Qualitative 
InDOG – 13-16/10/2014 
Palacký University – Olomouc 
Part. Gender SCANPAD STRING 
P01 M 
MMBACCD...
28 
Results: Qualitative 
InDOG – 13-16/10/2014 
Palacký University – Olomouc
COMPARING MAP READING ON 
PAPER AND DIGITAL MAPS 
MASTER THESIS 
ANNELIES INCOUL 
InDOG – 13-16/10/2014 
Palacký Universit...
30 
Introduction 
▪ Paper versus digital maps 
▪ Drawbacks of digital maps: 
• Resolution 
• Colour ranges 
• Dimensions 
...
31 
Study Design 
▪ Participants 
• 32 Master students or researchers 
• Department of Geography, Ghent University 
• Simi...
32 
Study Design 
▪ Task 
• Visual search 
• Locate three labels in the map image 
• Questionnaire 
- Background informati...
33 
Methodology 
▪ Data selection 
• Calibration accuracy: < 1° 
• Tracking ratio: > 85% 
• Visual verification 
• Shift c...
34 
Methodology 
▪ Creating the gridded visualisation 
• Areas Of Interest (AOIs) 
• Fixation counts and distribution 
• G...
paper digital paper digital paper digital paper digital 
35 
Results 
Mean search times 
(P = 0.956 > 0.05) 
InDOG – 13-16...
36 
Conclusion & Future Work 
▪ Users’ attentive behaviour on paper and digital maps 
▪ Controlled study design 
▪ No unid...
INFLUENCE OF TOPONYMS’ 
COLOURS ON THEIR 
READABILITY 
PHD RESEARCH 
RASHA DEEB 
InDOG – 13-16/10/2014 
Palacký University...
38 
Research Context 
▪ Typography on maps 
• Semiotics according to Bertin 
• Bold, italic, shape (font), orientation, et...
39 
Research Questions 
▪ Influence of complementary colors (background-label) on the 
users’ search efficiency; 
▪ Is thi...
40 
Study Design 
Color 
system 
Design conditions Display conditions 
HSV RGB CIE XYZ 
Color 
No. 
H° S% V% R G B 
L* 
(D...
41 
Study Design 
31 participants 
15 experts 
- 7 females 
- 8 males 
16 novices 
- 7 females 
- 9 males 
InDOG – 13-16...
42 
Results 
Users’ responses (s) between black and colored labels 
Map 
Number 
(M= Mean, SD= Standard Deviation). 
Black...
43 
Results 
▪ Colour difference 
ΔE*ab= {(ΔL*)2+(Δa*)2+(Δb*)2}1/2 where: ΔL*= L foreground* - L background*; 
Δa*= a fore...
44 
Results 
▪ Luminance difference 
ΔY= Y foreground –Y background 
calculated from the measured Y-value in the XYZ-syste...
MAPS, 
HOW DO USERS SEE THEM? 
PHD & POSTDOC RESEARCH 
KRISTIEN OOMS 
InDOG – 13-16/10/2014 
Palacký University – Olomouc
46 
Maps are … a medium to communicate 
Research Aims: 
How do map users 
Read 
Interpet 
Store 
Retrieve 
information on ...
47 
Maps are … visual 
Eye Tracking 
• Evaluate maps: UCD 
- Log users’ Point of Regard 
∙ Location 
∙ Duration 
∙ …in scr...
48 
User studies 
▪ PhD Research 
Basic map design 
Expert vs. novices 
Label placement 
InDOG – 13-16/10/2014 
Palacký...
49 
User studies 
▪ PhD Research 
Complex map design 
Expert vs novices 
Adaptations in symbology 
Mirroring of map ob...
50 
Maps are … interactive 
• ‘Maps on the Internet/Web’ 
• Typical user interactions 
- Panning 
 changing extent 
- Zoo...
51 
Eye Tracking & Interactivity? 
▪ Georeferencing eye movement data 
Changing point of 
origin 
Applying map 
projection...
52 
Case Study 
▪ Three eye tracking systems 
• SMI RED 250 
• Tobii T120 
• SR Research EyeLink 1000 
Panning 
InDOG – 13...
53 
Case Study 
▪ Three eye tracking systems 
InDOG – 13-16/10/2014 
Palacký University – Olomouc 
Panning
54 
Evaluation of panning in Google Maps 
▪ Alteration map - satellite view 
▪ Panning along a route 
• Zoom level 13 
▪ F...
55 
Future Work 
▪ Zooming? 
• In theory: same concept, only change in R value 
• Logging change in zoom levels 
- Scroll ...
IN SEARCH OF INDOOR 
LANDMARKS 
MASTER THESIS & PHD RESEARCH 
PEPIJN VIAENE 
InDOG – 13-16/10/2014 
Palacký University – O...
57 
Introduction 
▪ What is a landmark? 
= a wayfinding tool 
 a location or a direction 
 view-action pair 
▪ How to id...
58 
Study Design 
thinking aloud 
[CTA] 
[CRTA] 
eye tracking 
[fixation locus] 
[duration] 
InDOG – 13-16/10/2014 
Palack...
59 
Study Design 
[CTA (x2)] 
[CRTA ] 
▪ 13 recordings 
▪ 1924 verbalisation 
segments 
InDOG – 13-16/10/2014 
Palacký Uni...
60 
Study Design 
InDOG – 13-16/10/2014 
Palacký University – Olomouc
61 
Results 
41 % Referral to a landmark 
59 % No referral to a landmark 
InDOG – 13-16/10/2014 
Palacký University – Olom...
62 
Results 
= [59] 
≠ [73] 
Ø [89] 
eye tracking 
DP landmark category object landmark 
1 door (route) grey double door 
...
63 
Conclusion 
For the identification of (indoor) landmarks 
eye tracking can provide qualitative and complete data, 
in ...
SOME FUTURE PLANS… 
InDOG – 13-16/10/2014 
Palacký University – Olomouc
65 
Future Plans 
▪ Evaluation of the school’s textbooks 
▪ Evaluation of the new 25K symbology 
• Together with 
• 1 : 20...
66 
Future Plans 
▪ Evaluation of Neogeography maps 
▪ Evaluation of maps on different devices 
• Touch-interactions 
InDO...
THANK YOU FOR YOUR ATTENTION 
QUESTIONS? 
Fanny. 
VandenHaute 
@UGent.be 
Lien.Dupont 
@UGent.be 
PieterLaseure 
@hotmail....
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OGiC - Kristien Ooms - Eye tracking in the Geo-domain: a perception on cartography, navigation and landscape design

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Presentation from Third InDOG Doctoral Conference in Olomouc, Czech Republic. 13. - 16. October 2014

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OGiC - Kristien Ooms - Eye tracking in the Geo-domain: a perception on cartography, navigation and landscape design

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 10. THE USE OF EYE-TRACKING IN LANDSCAPE PERCEPTION RESEARCH PHD RESEARCH LIEN DUPONT InDOG – 13-16/10/2014 Palacký University – Olomouc
  11. 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. 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. 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. 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. 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
  16. 16. 16 Methodology  Eye tracking technology • Non-portable RED-system (SMI)  Eye tracking experiments • Random order • 5 or 10 seconds per photograph • Free-viewing • Measured eye tracking metrics • Fixations: number, duration (ms) • Saccades: number, amplitude (°), velocity (°/s) • Derived products: focus maps InDOG – 13-16/10/2014 Palacký University – Olomouc
  17. 17. 17 Results – Experiment 1 Panoramic Open  More fixations  Shorter saccades More information extraction  Shorter fixation duration Easier information extraction  More saccades  Larger saccades  Faster saccades Stronger visual exploration InDOG – 13-16/10/2014 Palacký University – Olomouc  Less & longer fixations  Less saccades Weaker visual exploration Homogeneous  Less fixations  Less & longer saccades Weaker visual exploration
  18. 18. 18 InDOG – 13-16/10/2014 Palacký University – Olomouc Expert Novice More fixations & saccades Less fixations & saccades Shorter fixations Longer fixations Longer scan path Shorter scan path Larger visual span Smaller visual span Smaller Voronoi cells Larger Vorornoi cells Scan paths Focus maps Voronoi cells Results – Experiment 2
  19. 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. 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. 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. 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. 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. 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. 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
  26. 26. 26 Results: Qualitative InDOG – 13-16/10/2014 Palacký University – Olomouc
  27. 27. 27 Results: Qualitative InDOG – 13-16/10/2014 Palacký University – Olomouc Part. Gender SCANPAD STRING P01 M MMBACCDEDCCCCDDEEBBBBBCBCDEDDE EDDSWWRSSSSSSSSSSSSSSNSRWSSSSS SSSWWSSMNSSDEEDCCDDDEFDDRSXWS P02 F MLAABBBBCCDDDDDDDEDEEDDDWWXSSR RRSSSSSSSSWCDEEXWSXSSWXSSSSSSS WSSSSSSSNSRDEBDDRSSSSSNNSSSRRM MLRRNSSWXXXXWXDDEWSSSSSSNSNSSS SWNSSSSS P03 M MMHBABBCDDCCDERWSSSSSXXIDEBBBBC CCCDDDEESSSXXRSSSSSSSXDESRRWSSS SNSSSSSSSD P05 F MMLBCCCCDDDDEENXXWSSSSSSSSSSXW RCDDCBCBBRSSSRSWWRMRLLIRRWWR P06 F MMBBABBCDDDEEDEDEWWWWWXSSSSSS SRSSSSSWSSSXXWSSWN Scanpad String Similarities
  28. 28. 28 Results: Qualitative InDOG – 13-16/10/2014 Palacký University – Olomouc
  29. 29. COMPARING MAP READING ON PAPER AND DIGITAL MAPS MASTER THESIS ANNELIES INCOUL InDOG – 13-16/10/2014 Palacký University – Olomouc
  30. 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. 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. 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. 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. 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. 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. 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. 37. INFLUENCE OF TOPONYMS’ COLOURS ON THEIR READABILITY PHD RESEARCH RASHA DEEB InDOG – 13-16/10/2014 Palacký University – Olomouc
  38. 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. 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
  40. 40. 40 Study Design Color system Design conditions Display conditions HSV RGB CIE XYZ Color No. H° S% V% R G B L* (D65) a* (D65) b* (D65) X Y Z 1 0, 100 100 255 0 0 69.9 95.7 77.5 76.09 40.18 4.617 2 30 100 100 255 128 0 86.0 48.6 79.7 88.28 67.98 11.92 3 60 100 100 255 255 0 121.8 -24.3 101.1 140.21 167.63 34.10 4 90 100 100 128 255 0 115.3 -90.6 90.3 81.46 145.01 33.79 5 120 100 100 0 255 0 112.3 -111.5 86.9 65.28 135.30 32.49 6 150 100 100 0 255 128 111.2 -99.6 40.6 68.50 131.85 76.55 7 180 100 100 0 255 255 116.5 -64.8 -39.4 98.45 149.03 257.74 8 210 100 100 0 128 255 70.6 20.4 -109.4 46.27 41.60 232.25 9 240 100 100 0 0 255 45.6 87.8 -148.7 33.45 14.97 222.16 10 270 100 100 128 0 255 55.5 94.3 -132.2 49.45 23.41 223.65 11 300 100 100 255 0 255 71.7 101.5 -6.3 83.62 43.21 52.41 12 330 100 100 255 0 128 79.1 114.9 -92.2 109.63 55.10 225.46 Black 0 0 0 0 0 0 1.5 0.8 -5 0 0.2 0.2 InDOG – 13-16/10/2014 Palacký University – Olomouc
  41. 41. 41 Study Design 31 participants 15 experts - 7 females - 8 males 16 novices - 7 females - 9 males InDOG – 13-16/10/2014 Palacký University – Olomouc
  42. 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. 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. 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. 45. MAPS, HOW DO USERS SEE THEM? PHD & POSTDOC RESEARCH KRISTIEN OOMS InDOG – 13-16/10/2014 Palacký University – Olomouc
  46. 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. 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. 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. 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. 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. 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. 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. 53 Case Study ▪ Three eye tracking systems InDOG – 13-16/10/2014 Palacký University – Olomouc Panning
  54. 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. 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. 56. IN SEARCH OF INDOOR LANDMARKS MASTER THESIS & PHD RESEARCH PEPIJN VIAENE InDOG – 13-16/10/2014 Palacký University – Olomouc
  57. 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
  58. 58. 58 Study Design thinking aloud [CTA] [CRTA] eye tracking [fixation locus] [duration] InDOG – 13-16/10/2014 Palacký University – Olomouc eye-mind hypothesis saliency = “eye catching”
  59. 59. 59 Study Design [CTA (x2)] [CRTA ] ▪ 13 recordings ▪ 1924 verbalisation segments InDOG – 13-16/10/2014 Palacký University – Olomouc
  60. 60. 60 Study Design InDOG – 13-16/10/2014 Palacký University – Olomouc
  61. 61. 61 Results 41 % Referral to a landmark 59 % No referral to a landmark InDOG – 13-16/10/2014 Palacký University – Olomouc
  62. 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. 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
  64. 64. SOME FUTURE PLANS… InDOG – 13-16/10/2014 Palacký University – Olomouc
  65. 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. 66 Future Plans ▪ Evaluation of Neogeography maps ▪ Evaluation of maps on different devices • Touch-interactions InDOG – 13-16/10/2014 Palacký University – Olomouc
  67. 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

Presentation from Third InDOG Doctoral Conference in Olomouc, Czech Republic. 13. - 16. October 2014

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