How people visually explore geospatial data

1. 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 urska.demsar@infra.kth.se

2. Developing geovisualisation tools visual exploration of geospatial data Geovisualisation tools and systems 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

3. User-centred design Can the functionality of the system do what is needed? Utility Usefulness of a computer system How well can typical users use the system? Usability 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. Assess users’ experience Evaluate system’s functionality Identify specific problems

4. Quantitative evaluation Methods complement each other! Qualitative evaluation User testing performing predefined tasks Measuring the accuracy and efficiency of users’ performance on typical tasks questionnaires Formal evaluation controlled measurements: errors, time Usability testing Exploratory usability thinking-aloud methodology evaluation through user participation Observing users Assessing how the users work with the system observation, video descriptive data: verbal protocols

5. Exploratory usability experiment Dataset with clearly observable spatial and other patterns GeoVISTA - based visual data mining system 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?

6. Iris setosa Iris versicolor Iris virginica plant measurements new attributes bedrock soil landuse Linear separability in geographic space put in a spatial context Data Iris dataset - famous from pattern recognition Fischer 1936 Original dataset 150 plants, 50 in each class, 4 attributes Linear separability in attribute space

7. The best pattern recognition apparatus Data exploration by visual data mining Data mining = a form of pattern recognition the human brain 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 communicationchannel between the user and the computer to discover new patterns.

8. Brushing & linking + interactive selection GeoVISTA Studio Exploration system Gahegan et al. 2002, Takatsuka and Gahegan 2002 Multiform bivariate matrix Visualisations geoMap Parallel Coordinates Plot (PCP)

9. 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 nationality/ mother tongue familiar with GIS gender 50/50 Russian Swedish Slovenian Spanish engineering background Not colour-blind

10. Experiment design in English Usability test performed individually under observation 5 steps 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

11. 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

12. 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 background knowledge Analysis of the thinking-aloud protocols Hypotheses extraction classification acc. to source prompted by a visual pattern Counting visualisations total frequency refinement of a previous hypothesis relative frequency

13. background knowledge “Higher flowers probably have longer leaves.” Hypotheses classification “Are sepal length and sepal width correlated?” “Flowers of the same species probably grow in the same area.” prompted by a visual pattern Refinement of a previous hypothesis “There seem to be two clusters in each of these scatterplots.” “Not only are there two clusters, but the big cluster consists of two subclusters according to petal length.” assign colour acc. to petal length.

14. Hypotheses generated Visualisation frequencies Relative frequency: fR(i,j)=fT(i,j)/Nj i – visualisation j – participant

15. Model of the visual investigation of data 3. Exploration strategies 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 Gather evidence Adjust browsing/ decide where to look Evaluate initial idea Get new/more information Interpret data Manipulate graphics

16. Strategy no. 1: Confirm/reject a hypothesis based on background knowledge and then discard it. Repeat from the start. Confirming a priori hypothesis Browse Adjust browsing/ decide where to look Look for content Amend initial idea according to new information Form ideas or hypotheses Look for content Gather evidence Adjust browsing/ decide where to look Evaluate initial idea Get new/more information Interpret data Manipulate graphics

17. 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 Adjust browsing/ decide where to look Look for content Amend initial idea according to new information Form ideas or hypotheses Look for content Gather evidence Adjust browsing/ decide where to look Evaluate initial idea Get new/more information Interpret data Manipulate graphics

18. 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 Browse Adjust browsing/ decide where to look Look for content Amend initial idea according to new information Form ideas or hypotheses Look for content Gather evidence Adjust browsing/ decide where to look Evaluate initial idea Get new/more information Interpret data Manipulate graphics

19. 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

20. Exploration strategies: • three different exploration strategies not related to gender nationality/mother tongue GIS experience academic background Investigating spatial data visually is not so simple! Substantial interpersonal differences in forming exploration strategies Why? Question for the future

21. Thank you!