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How people visually explore
geospatial data
ICA WS on Geospatial Analysis and Modeling
8th July 2006
Vienna
Urška Demšar
Geoinformatics, Dept of Urban Planning and Environment
Royal Institute of Technology (KTH), Stockholm, Sweden
[email protected]
Developing geovisualisation tools
visual exploration
Geovisualisation tools
and systems
of geospatial data
analysis
presentation
For a long time: technology-driven development
A recent shift in attitude: user-centred development
Human-Computer
Interaction (HCI)
Developing a usable
and useful information
system
Knowledge about users and how
they use the system
User-centred design
User-centred design
Usefulness
of a
computer
system
Utility
Can the functionality of the system do what
is needed?
Usability
How well can typical users use the
system?
Usability of an information system is the extent to which the system supports
users to achieve specific goals in a given context of use and to do so
effectively, efficiently and in a satisfactory way.
Nielsen 1993
Usability evaluation
Process of systematically collecting & analysing data on how users use
the system for a particular task in a particular environment.
Evaluate system’s
functionality
Identify specific problems
Assess users’
experience
User testing
Measuring the
accuracy and
efficiency of users’
performance on
typical tasks
Quantitative
evaluation
Formal evaluation
Usability
testing
evaluation through
user participation
performing
predefined
tasks
questionnaires
controlled measurements:
errors, time
Methods complement each other!
Exploratory usability
Qualitative
evaluation
Observing users
Assessing how the
users work with the
system
thinking-aloud
methodology
observation,
video
descriptive data: verbal protocols
Exploratory usability experiment
GeoVISTA - based
visual data mining system
Dataset with clearly observable
spatial and other patterns
Formal usability
issues: Edsall 2003,
Robinson et al. 2005
Exploratory usability
experiment
How people
visually explore
geospatial data?
Which visualisations
they prefer to use?
Which exploration
strategies they
adopt?
Data
Iris dataset - famous from
pattern recognition
Fischer 1936
Original dataset
Iris setosa
Iris versicolor Iris virginica
150 plants, 50 in each class, 4 attributes
Linear separability in attribute space
new attributes
Linear separability in geographic space
put in a
spatial
context
bedrock
soil
landuse
plant measurements
Data exploration by visual data mining
Data mining = a form of pattern recognition
the human brain
The best pattern
recognition apparatus
How to use it in data mining?
Computers communicate
with humans visually.
Computerised data visualisation
Visual data mining:
Data mining method which uses visualisation as a communication
channel between the user and the computer to discover new patterns.
Exploration system
GeoVISTA Studio
Gahegan et al. 2002, Takatsuka and Gahegan 2002
Multiform bivariate matrix
Visualisations
geoMap
Brushing &
linking +
interactive
selection
Parallel Coordinates Plot (PCP)
Participants
Small number of participants: 6
Discount usability engineering
The majority of the usability issues
are detected with 3-5 participants.
Nielsen 1994, Tobon 2002
cost & staff
limitations
voluntary participation
Students of the International Master
Programme in Geodesy and
Geoinformatics at KTH
non-native
English speakers,
fluent in English
Ghanian
familiar
with GIS
gender
50/50
engineering
background
nationality/
mother tongue
Russian
Swedish
Spanish
Not colour-blind
Slovenian
Experiment design
Usability
test
5 steps
in English
performed individually under
observation
1-1.5 h per participant
1. Introduction:
- what the test was about, consent for using the data, etc.
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
The main part of the test
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
Results
The bivariate matrix
the easiest to use.
1. Perceived usefulness
& learnability
The map the easiest
to understand.
The PCP the most difficult
to understand and use.
2. Exploratory usability
Analysis of the
thinking-aloud
protocols
background
knowledge
Hypotheses
extraction
Counting
visualisations
relative
frequency
classification
acc. to source
total
frequency
prompted
by a visual
pattern
refinement of a
previous hypothesis
Hypotheses
classification
Refinement of a
previous hypothesis
“Not only
are there two
clusters, but
the big
cluster
consists of
two subclusters
according to
petal length.”
background
knowledge
“Higher flowers probably have
longer leaves.”
“Are sepal length and sepal
width correlated?”
prompted
by a visual
pattern
“Flowers of the same species
probably grow in the same area.”
“There seem to be two
clusters in each of these
scatterplots.”
assign colour
acc. to petal
length.
Hypotheses
generated
Visualisation
frequencies
Relative frequency:
fR(i,j)=fT(i,j)/Nj
i – visualisation
j – participant
3. Exploration strategies
Model of the visual investigation of data
Tobon 2002
3 groups
mapping the
strategies
as paths
Browse
Adjust browsing/
decide where to look
Look for content
Amend initial
idea according
to new information
Form ideas
or hypotheses
Look for content
Evaluate
initial idea
Interpret data
Adjust browsing/
decide where to look
Get new/more
information
Gather
evidence
Manipulate graphics
Strategy no. 1:
Confirming a priori hypothesis
Confirm/reject a hypothesis based on background knowledge and then
discard it. Repeat from the start.
Browse
Adjust browsing/
decide where to look
Look for content
Amend initial
idea according
to new information
Form ideas
or hypotheses
Look for content
Evaluate
initial idea
Interpret data
Adjust browsing/
decide where to look
Get new/more
information
Gather
evidence
Manipulate graphics
Strategy no. 2:
Confirming a hypothesis based on a visual pattern
Form a hypothesis based on what you see, interpret and adapt it, confirm/
reject it and discard it. Repeat from the start.
Browse
Adjust browsing/
decide where to look
Look for content
Amend initial
idea according
to new information
Form ideas
or hypotheses
Look for content
Evaluate
initial idea
Interpret data
Adjust browsing/
decide where to look
Get new/more
information
Gather
evidence
Manipulate graphics
Strategy of group no. 3:
Seamless exploration
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.
Browse
Adjust browsing/
decide where to look
Look for content
Amend initial
idea according
to new information
Form ideas
or hypotheses
Look for content
Evaluate
initial idea
Interpret data
Adjust browsing/
decide where to look
Get new/more
information
Gather
evidence
Manipulate graphics
Conclusions
Small study size:
- conclusions can not be too general, observations only
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
GIS
experience
gender
nationality/mother tongue
academic
background
Investigating spatial data visually is not so simple!
Substantial interpersonal differences in forming exploration strategies
Why?
Question for the future
Thank you!