2016 석사 2학기 첫 랩발제

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-MaIIhew Kay eI aB.
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2. Landscape
자율주행차 카톡 택시 네비게이션
& map warping
자율주행차
연대:
HCI academy
융대원:
여름
수업:
HCI
수업:
융개론
2015 여름 2015 가을 2016 봄
입학 후입학 전

3. Landscape
자율주행차 카톡 택시
네비게이션
& map warping
자율주행차
연대 여름 HCI 융개론
2015 여름 2015 가을 2016 봄
공통점:
Mobility, Transportation

4. 저자소개
Matthew Kay

5. 저자소개
Matthew Kay

6. 랩 발제
Matthew Kay

7. Overview
Background
Research Question
Method
Results
• quantile dot plots reduce the variance of probalistic estimates by ~1.15 times than density plots
• facilitate more confident estimation by end users in the context of real time transit prediction scenarios
• a novel discrete representation of continuous outcomes designed for small screens and quantile dotplots
• how well pople can use different uncertainty visualization
• identiy effective uncertainty visualization for real time decision making on a smartphone
• test for the diffrences in how precisely and confidently people extract probabliites from different
visualizations of uncertainty
• uncertainty visualization may not align with user needs
• point estimate to aid decision - making that are time-contrained using spaced constrained interfaces

8. Paper Overview
1) Background
2) Survey of Existing Users
3) Design Requirement
4) Design
5) Experiment
6) Conclusion: dotplots > density plots
>
precise

9. “Seoulbus”“OneBusAway”
시애틀과 서울의 모바일 버스앱
Survey of existing users
• 172명을 대상으로 서베이를 진행함
• 설문의 목표 :
• how users currenlty use real time bus arrival prediction
• their unaddressed needs for goal oriented uncertainty information

10. Survey of existing users
Users’ existing goals
- when to leave
- 버스 출발 시각
- wait time
- 버스 대기 시간
- time to next bus
- 배차 간격
- schedule risk
- 버스가 생각보다 늦게 왔을 경우
- schedule opporutnity
- 다음 버스 오기전까지 얼마나 시간이 남
았는지
Problems with OneBusAway
- status probability - 버스가 안와서 다른 방법을 써야할경우
- prediction variance- 예상된 시간에 버스가 안올 찬스
- schedule frequency- 얼마나 버스가 자주 오는지

11. Design
Design Rationale
• 2 different layouts better serve different use cases (bus timeline and route timeline)
-> to resolve design tensions and to match user goals
shows one predicted bus shows all predicted buses from a given route

12. shows one predicted bus shows all predicted buses from a given route
버스 번호
목적지 장소
An iterative design process
two alternative layouts: bus time line & route timeline
- to resolve design tensions and to match user goals
Design: “Uncertainty”

13. Visualization
4 types of visualizations selected for evaluation

14. Visualizing predictive distributions:
• direct estimation of arbitrary • continous probabilitstic prediction
2 existing discrete plots for visualizing predictive distribution
dotplots & stripleplots
gradient plotdiscrete analog

15. Visualizing predictive distributions:
• direct estimation of arbitrary • continous probabilitstic prediction
2 existing discrete plots for visualizing predictive distribution
dotplots & stripleplots
gradient plotdiscrete analog
point estimate vs. probabilistic estimates
점 추정법 vs 확률론적 방법

16. Visualizing predictive distributions:
• direct estimation of arbitrary • continous probabilitstic prediction
2 existing discrete plots for visualizing predictive distribution
dotplots & stripleplots
gradient plotdiscrete analog
point estimate vs. probabilistic estimates
점 추정법 vs 확률론적 방법
point estimate
- cause users to ignore the probalistic one
- giving a false sense of precision
a less glanceable point estimate
difficult to skim and frustrating to use
*goal: glanceable but not conveying false precision*
facilitated glanceability
allowing users to pay little attention to the probalistic estimates
point estimate —> probability distribution resolved this tension

17. Ex) “Uncertainty” - Trade Off
point estimate vs. probabilistic estimates
점 추정법 vs 확률론적 방법
point prediction
• most likely to give users false sense of prediction
#분뒤 버스 도착예정버스 번호
버스 지역

18. Visualization: quantile dotplots

19. Visualization: quantile dotplots

20. Visualization: quantile dotplots

21. Visualization
4 types of visualizations selected for evaluation

22. Participants
Total 221 participants were recruited
Primary reserach question: Effect of visualization types
- First 100 participants were on bus timeline condition
- Rest of 121 were randomly assigned to either bus OR route- timelines layout
221명
bus timeline route timeline
100명
121명

23. Results
to understand how well each visualization performs, the error were examined in people’s probability
estimates
- bias : (over- or under-) estimate probabilities on average
- variance: how self- consistent are people’s estimate, whether biased or not?
as long as bias is low, variance is the more important component of error in this task
the model was used to assess bias and variance more systematically and
to account for within participants effects

24. Errors
n= number

25. Results
Dotplots
- 1.15 times more precise than density plot
- yield higher conidence & rated less visually appealing
dotplots

26. Implications
visual appeal vs. estimation trade off
precision vs. glanceability
trade off
정확성? vs. 힐끗보기?
(한번에 알아들을수있는가?)
미적으로 보기 좋은가? vs. 예상/추정하는데 밀접한가?

27. Conclusion
• identfy general design requirements for visualizing uncertainty on mobile app
• propose a mobile inteface for communicating uncertainty in realtime transit predictions that
supports users’ goals
• developed evaluated candidate visualizations
• a novel discrete evaluated candidate visualizations
• quantile dotplots improved probalistics estimate (better than traditional density plots)
• quantile dotplot depicting a small # of outcome has ~1.15 times lower variance than a
density plot (= 1~3 % points more precise)
• facilitaed more confident estimate by end-users
• employ interactivity that balance precision and glanceability

28. End of Document
Thank You!