When (iHh) iH My .uH- UHer-cenIered KiHuaBizaIiEnH Ef UncerIainIy in 0Keryday, MEbiBe 5redicIiKe 6yHIeCH+ /12 2(),-MaIIhew Kay eI aB./유혜PM 2()+ 6prin?

Landscape

자율주행차 카톡 택시 네비게이션 & map warping

자율주행차

연대: HCI academy

융대원: 여름

수업: HCI

수업: 융개론

2015 여름 2015 가을 2016 봄

입학 후 입학 전

Landscape

자율주행차 카톡 택시네비게이션

& map warping자율주행차

연대 여름 HCI 융개론

2015 여름 2015 가을 2016 봄

공통점:

Mobility, Transportation

저자소개

Matthew Kay

저자소개

Matthew Kay

랩 발제

Matthew Kay

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

Paper Overview

1) Background

2) Survey of Existing Users

3) Design Requirement

4) Design

5) Experiment

6) Conclusion: dotplots > density plots

>

precise

“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

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

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

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”

Visualization

4 types of visualizations selected for evaluation

Visualizing predictive distributions:

• direct estimation of arbitrary • continous probabilitstic prediction

2 existing discrete plots for visualizing predictive distribution

dotplots & stripleplots

gradient plot discrete analog

Visualizing predictive distributions:

• direct estimation of arbitrary • continous probabilitstic prediction

2 existing discrete plots for visualizing predictive distribution

dotplots & stripleplots

gradient plot discrete analog

point estimate vs. probabilistic estimates 점 추정법 vs 확률론적 방법

Visualizing predictive distributions:

• direct estimation of arbitrary • continous probabilitstic prediction

2 existing discrete plots for visualizing predictive distribution

dotplots & stripleplots

gradient plot discrete 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

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

Visualization: quantile dotplots

Visualization: quantile dotplots

Visualization: quantile dotplots

Visualization

4 types of visualizations selected for evaluation

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

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

Errors

n= number

Results

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

dotplots

Implications

visual appeal vs. estimation trade off

precision vs. glanceability

trade off

정확성? vs. 힐끗보기? (한번에 알아들을수있는가?)

미적으로 보기 좋은가? vs. 예상/추정하는데 밀접한가?

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

End of Document

Thank You!