how people visually explore geospatial data

How people visually exploreHow people visually exploregeospatial datageospatial data

Urška DemšarGeoinformatics, Dept of Urban Planning and EnvironmentRoyal Institute of Technology (KTH), Stockholm, Sweden

[email protected]

ICA WS on Geospatial Analysis and Modeling8th July 2006

Vienna

Developing geovisualisation tools

Developing a usable and useful information system

User-centred design

Human-Computer Interaction (HCI)

Knowledge about users and how they use the system

Geovisualisation toolsand systems

visual exploration

analysis

presentation

of geospatial data

For a long time: technology-driven development

A recent shift in attitude: user-centred development

Usefulness

Utility

Usability

Can the functionality of the system do what is needed?

How well can typical users use the system?

Usability evaluationProcess of systematically collecting & analysing data on how users use the system for a particular task in a particular environment.

User-centred design

Usability of an information system is the extent to which the system supportsusers to achieve specific goals in a given context of use and to do soeffectively, efficiently and in a satisfactory way. Nielsen 1993

of a computersystem

Evaluate system’s functionality

Assess users’ experienceIdentify specific problems

Usability testing

Formal evaluation

Exploratory usability

User testing

Observing users

Measuring the accuracy and efficiency of users’performance on typical tasks

Assessing how theusers work with thesystem

performingpredefined

tasks

questionnaires

thinking-aloudmethodology

observation,video

controlled measurements:errors, time

descriptive data: verbal protocols

Qualitativeevaluation

Quantitativeevaluation

Methods complement each other!

evaluation throughuser participation

Exploratory usability experiment

GeoVISTA - basedvisual data mining system

Dataset with clearly observable spatial and other patterns

Exploratory usabilityexperiment

How peoplevisually explore geospatial data?

Which explorationstrategies theyadopt?

Which visualisationsthey prefer to use?

Formal usabilityissues: Edsall 2003,Robinson et al. 2005

Data

Iris setosa Iris versicolor Iris virginica

Iris dataset - famous frompattern recognition

Fischer 1936

150 plants, 50 in each class, 4 attributes

Linear separability in attribute space

Original dataset

new attributesplant measurements

bedrocksoil

landuse

put in a spatialcontext

Linear separability in geographic space

Visual data mining:Data mining method which uses visualisation as a communication channel between the user and the computer to discover new patterns.

Data exploration by visual data mining

Data mining = a form of pattern recognition

the human brain

The best patternrecognition apparatus

How to use it in data mining?

Computers communicate with humans visually.

Computerised data visualisation

Visualisations

geoMap

Multiform bivariate matrix

Parallel Coordinates Plot (PCP)

Brushing &linking +

interactiveselection

Exploration system Gahegan et al. 2002, Takatsuka and Gahegan 2002GeoVISTA Studio

Participants

Small number of participants: 6 Discount usability engineering

Nielsen 1994, Tobon 2002

The majority of the usability issues are detected with 3-5 participants.

cost & stafflimitations

Students of the International Master Programme in Geodesy and Geoinformatics at KTH

non-nativeEnglish speakers,fluent in English

nationality/mother tongue

Ghanian

Russian

SlovenianSpanish

Swedish

gender50/50

engineeringbackground

familiarwith GIS

voluntary participation

Not colour-blind

Experiment design

1. Introduction:- what the test was about, consent for using the data, etc.

Usabilitytest

in English

performed individually underobservation

1-1.5 h per participant5 steps

2. Background questionnaire:- gathering information on gender, mother tongue, background, etc.

3. Training: (unlimited time: ca. 45-50 min per participant)- introduction to data and visual data mining system- independent work though a script- questions allowed

4. Free exploration: (limited time: 15 min per participant)- whatever exploration in whatever way the participant wanted- no questions allowed- Verbal Protocol analysis – “thinking-aloud”- cooperative evaluation: if the participant stops talking, the observer can ask questions (“What are you trying to do?”, “What are you thinking now?”)

5. Rating questionnaire: - gathering information on participants’ opinion about the system- measuring perceived usefulness & learnability

The main part of the test

Results

1. Perceived usefulness & learnability

The bivariate matrix the easiest to use.

The map the easiest to understand.

The PCP the most difficult to understand and use.

2. Exploratory usability

Analysis of the thinking-aloud protocols

Hypothesesextraction

classificationacc. to source

backgroundknowledge

promptedby a visualpattern

refinement of aprevious hypothesis

Countingvisualisations total

frequencyrelativefrequency

Hypothesesclassification

backgroundknowledge

promptedby a visualpattern

Refinement of aprevious hypothesis

“Higher flowers probably havelonger leaves.”

“Are sepal length and sepalwidth correlated?”

“There seem to be twoclusters in each of these scatterplots.”

“Not only are there twoclusters, butthe bigcluster consists oftwo subclustersaccording topetal length.”

assign colour acc. to petal length.

“Flowers of the same speciesprobably grow in the same area.”

Visualisationfrequencies

Hypothesesgenerated

fR(i,j)=fT(i,j)/Nj

i – visualisationj – participant

Relative frequency:

Browse

Form ideasor hypotheses

Manipulate graphicsInterpret data

Amend initialidea according

to new information

Look for content

Look for content

Adjust browsing/decide where to look

Gatherevidence

Get new/moreinformation

Evaluateinitial idea


Tobon 2002

3. Exploration strategies Model of the visual investigation of data

3 groups mapping thestrategies

as paths

Browse




to new information

Look for content

Look for content


Gatherevidence




Strategy no. 1:Confirm/reject a hypothesis based on background knowledge and then discard it. Repeat from the start.

Confirming a priori hypothesis

Browse




to new information

Look for content

Look for content


Gatherevidence




Strategy no. 2: Form a hypothesis based on what you see, interpret and adapt it, confirm/reject it and discard it. Repeat from the start.

Confirming a hypothesis based on a visual pattern

Browse




to new information

Look for content

Look for content


Gatherevidence




Strategy of group no. 3: Form a hypothesis based on what you see, explore further and adapt/refine it,according to what you see in other visualisations, confirm the refined version or adapt again and continue.

Seamless exploration

Small study size:- conclusions can not be too general, observations only

Conclusions

Training necessary:- new concepts visual data mining

unusual visualisations

interactivity of geoVISTA-based tools

Cooperative evaluation vs. strict thinking-aloud:- cooperative evaluation better (compared to a previous experiment)- no silent participants- easier to keep protocols

Discrepancy in perceived vs. actual learnability:- “PCP very difficult to understand”- PCP used most frequently of all visualisations- spaceFills almost never used

Exploration strategies:- three different exploration strategies

not related to gender

academic background

nationality/mother tongueGISexperience

Investigating spatial data visually is not so simple!

Substantial interpersonal differences in forming exploration strategies

Why? Question for the future

Thank you!

how people visually explore geospatial data

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