ICC2017 Washington - http://icc2017.org/ 6105.4 Efficiency and effectiveness in case of disaster: a visual damage assessment tests Kristien Ooms, PhD Ghent University Julia Åhlén University of Gävle Stefan Seipel University of Gävle

1. EFFICIENCY AND EFFECTIVENESS
IN CASE OF DISASTER:
A VISUAL DAMAGE ASSESSMENT TEST
Dr. Kristien Ooms, dr. Julia Åhlén, prof. dr. Stefan Seipel
DEPARTMENT GEOGRAPHY
DEPARTMENT OF INDUSTRIAL DEVELOPMENT, IT AND LAND MANAGEMENT

2. INTRO
̶ Disasters…
2
ManagementTypes Information!!!
Urban environment
Collapsed buildings
✓ Efficient = Fast
✓ Effective = Correct

3. INTRO
̶ Information?
3
VGI - Consult the crowd
Detect Collapsed Buildings
Compare before vs after
Visualize?

4. STUDY DESIGN
4
̶ Visualisation 1 - Page Flipping (PF)
̶ Imperfections

5. STUDY DESIGN
5
̶ Visualisation 1 - Page Flipping (PF)
̶ Imperfections

6. STUDY DESIGN
6
̶ Visualisation 1 - Page Flipping (PF)
̶ Imperfections

7. STUDY DESIGN
7
̶ Visualisation 1 - Page Flipping (PF)
̶ Imperfections

8. STUDY DESIGN
8
̶ Visualisation 2 - Difference Image (DIFF)
̶ Imperfections

9. STUDY DESIGN
9
̶ Visualisation 2 - Difference Image (DIFF)
̶ Imperfections

10. STUDY DESIGN
11

11. STUDY DESIGN
Department of Industrial Development, IT and Land
Management, University of Gävle, Sweden
2x3 factorial design:
• 2 visalizations: pf vs diff
• 3 gradations of imperfections:
0.1 deg - 0.25 deg – 0.5 deg
72 stimuli:
• 12 areas of 1x1 km (city of Ghent)
• 2 visualizations
• 3 rotations

12. STUDY DESIGN
26 participants:
Employees & Students  familiar with GIS
Department of Industrial Development, IT and Land
Management, University of Gävle, Sweden
Nr PartID Start Visualization Data Set
1 1101 Page Flipping Set 1
2 2102 Difference Image Set 1
3 1203 Page Flipping Set 2
4 2204 Difference Image Set 2
5 1105 Page Flipping Set 1
6 2106 Difference Image Set 1
7 1207 Page Flipping Set 2
8 2208 Difference Image Set 2
… … … …

13. PRELIMINARY
RESULTS
15

14. RESULTS
Factors & Parameters:
Foreach Participant - Image
16
1. locCorrect
2. locWrong
3. locMultiple
4. locMissed
1. trialNr
2. trialDuration
1. timeFirstClick (since start trial)
2. timeLastClick (since start trial)
3. intervalFirstLastClick (time in between both)
4. timeAfterLastClick (until end trial)
1. timeFirstPF
2. timeLastPF
3. timeAfterLastPF
4. intervalFirstLastPF
5. countPF_beforeFirstClick
6. countPF_afterLastClick
7. countPF_during
8. countPF_total
9. countClickRight
10.countClickLeft
11.countClickMult
12.speedPF_beforeFirstClick
13.speedPF_afterLastClick
14.speedPF_during
15.speedPF_total
16.ratioClickLeftRight
17.ratioClickPFRight
18.ratioClickPFLeft
19.ratioClickPFTotal
1. imgType
2. imgArea
3. imgNrDem
4. imgRot
5. imgSet
Timing ClicksTrial Info
Stimulus Info
Correct?
Arrows vs. clicks (only PF)

15. RESULTS
17
Multivariate ANOVA imgType imgRot
imgType
*imgRot
trialDur 0,005 0,000 0,000
N
imgType diff 460
pf 441
imgRot .10 306
.25 302
.50 293
timeFirstClick 0,776 0,000 0,000
timeLastClick 0,000 0,000 0,000
intervalFirstLastClick 0,000 0,000 0,000
timeAfterLastClick 0,000 0,000 0,000
Mean
(sec)
Standard
Deviation
(sec)

16. RESULTS
18
Mean (sec) Standard Deviation(sec)
Multivariate ANOVA imgType imgRot imgType *imgRot
trialDur 0,005 0,000 0,000

17. RESULTS
19
Mean (sec) Standard Deviation(sec)
Multivariate ANOVA imgType imgRot imgType *imgRot
timeFirstClick 0,776 0,000 0,000

18. RESULTS
20
Mean (sec) Standard Deviation(sec)
Multivariate ANOVA imgType imgRot imgType *imgRot
timeLastClick 0,000 0,000 0,000

19. RESULTS
21
Mean (sec) Standard Deviation(sec)
Multivariate ANOVA imgType imgRot imgType *imgRot
intervalFirstLastClick 0,000 0,000 0,000

20. RESULTS
22
Mean (sec) Standard Deviation(sec)
Multivariate ANOVA imgType imgRot imgType *imgRot
timeAfterLastClick 0,000 0,000 0,000

21. RESULTS
23
Mean (count)
Multivariate ANOVA imgType imgRot
imgType
*imgRot
locCorrect 0,000 0,000 0,000
locWrong 0,000 0,000 0,000
locMult 0,748 0,000 0,518
locMissed 0,000 0,000 0,000
N
imgType diff 460
pf 441
imgRot .10 306
.25 302
.50 293

22. RESULTS
24
one-way ANOVA P
timeFirstPF 0,279
timeLastPF 0,000
timeAfterLastPF 0,161
intervalFirstLastPF 0,000
one-way ANOVA P
countPF_afterLastClick 0,402
countPF_during 0,000
countPF_total 0,000
(only pf) N
imgRot 143≤ N ≤ 151

23. RESULTS
25
rawET
SMI Experiment
Center:
Record Data
SMI BeGaze:
Analyse Data
filterRawET.py
Raw samples
@ 120Hz
Split (raw) ET files
Per part-img
EyeMMV
✓Average fixation duration
✓no. of fix per second
✓Saccade length
Fixations -
saccades
Per part-img
SPSS
Statistical
resultsSequence
Analyses.py
✓ Based on Levenshtein
distances
✓ python library available
Fix2grid.py
✓Number of fixations
✓Dwell time
✓Time series ( 1s, 2s, 5s, 10s …
✓Math: subtracting results different
visualizations
✓Statistics: find statistical
differences across visualizations
✓MatLab toolbox
Similarity
measures
Search strategy

24. RESULTS
imgType imgRot
imgType *
imgRot
avgFixDur 0,000 0,382 0,565
fix/s 0,000 0,009 0,567
SPSS
Statistical
results
N
imgRot 133≤ N ≤ 138
Fixations

25. CONCLUSION
̶ PF vs DIFF
̶ Efficiency: related to timings
̶ Effectiveness: related to ‘correct’ indications
̶ Interaction with rotation
27
Better
performance
Distortion before vs after
Difference images
Page flipping
?

26. FUTURE WORK
̶ Location damage site, severity damage site
̶ Saccades, grids, sequence analysis
̶ Familiarity of the area (e.g. residents of Ghent?)
̶ Familiarity with GIS/cartography
̶ Border representations, size of area
̶ 3D visualisation: RGBcolor vs. greyscale
viewing angle
28

27. Questions?
Kristien Ooms
kristien.ooms@ugent.be
dr. Julia Åhlén
prof. dr. Stefan Seipel