Cognitive Engineering for Geographic Information Science

Transcription

1 Cognitive Engineering for Geographic Information Science Martin Raubal Department of Geography, UCSB 21 Jan 2009 ThinkSpatial, UCSB 1

2 GIScience Motivation systematic study of all aspects regarding geographic information foundation for development of geospatial tools & services that support people in their spatiotemporal decision-making Useful & useable solutions => principles of human spatial cognition regarding representation & processing of spatial and temporal aspects of phenomena 21 Jan 2009 ThinkSpatial, UCSB 2

3 Cognitive aspects in GIScience GIS: An organized collection of computer hardware, software, geographical data, and personnel designed to efficiently capture, store, update, manipulate, analyze, and display all forms of geographically referenced information. [ESRI] 21 Jan 2009 ThinkSpatial, UCSB 3

4 Where are they? 21 Jan 2009 ThinkSpatial, UCSB 4

5 Mobile GIS Paradigm shift in GIScience & Systems Past: GIS in large organizations Today: different providers offer services for geospatial problem solving Mobile information society => GI in small quantities for many users. People s information needs depend highly on situational and personal context. 21 Jan 2009 ThinkSpatial, UCSB 5

6 Location-Based Service 21 Jan 2009 ThinkSpatial, UCSB 6

7 Useful and usable solutions to people s geospatial problems can only be found by considering people s cognition, abilities, and strategies brought to the problemsolving process. 21 Jan 2009 ThinkSpatial, UCSB 7

8 Outline Cognitive engineering Spatial cognitive engineering Classifying cognitive user parameters Cognitively engineered applications Conclusions and future work 21 Jan 2009 ThinkSpatial, UCSB 8

9 Cognitive engineering Donald Norman [1986]: integrate cognitive & computer science approaches to the design and construction of machines. a type of applied cognitive science People s interactions with everyday things (telephones, faucets, doors) => discrepancy between psychological user variables & physical system variables. 21 Jan 2009 ThinkSpatial, UCSB 9

10 BRIDGE THE GAP 21 Jan 2009 ThinkSpatial, UCSB 10

11 Spatial cognitive engineering Focuses on human-computer interaction regarding spatial & temporal aspects of phenomena in the world. Spatio-temporal reasoning is complex. People s questions when using geospatial tools have a spatio-temporal context => semantics of location or road? Goes beyond user-interface level! 21 Jan 2009 ThinkSpatial, UCSB 11

12 Noise abatement planning residential residential area road road area residential area User System 21 Jan 2009 ThinkSpatial, UCSB 12

13 Spatial cognition How people perceive and cognize spatial information; How they reason and communicate about it; How they represent spatial information; How they use it. Importance of research on education, experience, and age differences to design better geospatial tools [Montello & Freundschuh 2005] => applied spatial cognition [Allen 2007]. 21 Jan 2009 ThinkSpatial, UCSB 13

14 Personalization the action of making something personal, or focused on or concerned with a certain individual or individuals; emphasis on or attention to individual persons or personal details. [Oxford English Dictionary] GIScience => customizing & adapting geospatial services to their users. 21 Jan 2009 ThinkSpatial, UCSB 14

15 Personalization cont. Consumers of GI differ in their cognitive styles, abilities, and preferences. Information needs depend on location, time, and personal context. 21 Jan 2009 ThinkSpatial, UCSB 15

16 Classifying cognitive user parameters 21 Jan 2009 ThinkSpatial, UCSB 16

17 Generic Experimental stream of psychology: general set of cognitive parameters assumed to be applicable to all people. People use landmarks for finding their ways and for communicating wayfinding directions to others. People share numerous cultural universals [Montello 1995] and with regard to these a finer distinction is not required. 21 Jan 2009 ThinkSpatial, UCSB 17

18 Group User groups defined by common sets of cognitive parameters: similar abilities, interests, concerns, goals, beliefs, or behavioral practices. Correlational stream of psychology: variation of parameters between groups. Occur at different hierarchical levels: male vs. female, blind UCSB students What kinds of differences should be taken into account when forming a user group within a particular spatio-temporal context? 21 Jan 2009 ThinkSpatial, UCSB 18

19 Individual Relates to correlational psychology with a focus on individual variation. The more parameters there are that need to be adapted the more difficult and complex it becomes. Individual user preferences: I want to go from location X to location Y by public transport. 21 Jan 2009 ThinkSpatial, UCSB 19

20 Landmark-Based Wayfinding Instructions People usually employ landmarks when mentally representing, describing, and communicating routes in their environment [Lynch 1960, Lovelace et al. 1999, Denis et al. 2006] => generic cognitive strategy. Methods for automatic detection of landmarks for wayfinding instructions [Sadeghian and Kantardzic 2008]. 21 Jan 2009 ThinkSpatial, UCSB 20

21 Landmark saliency measure Measure Property Value Significance (Property) Significance (Measure) Weight Weighted Significance Total Visual attraction α s α β 1 s β1 β 2 s β2 γ s γ δ s δ ε s ε Semantic attraction ζ s ζ η s η Structural attraction θ s θ s vis = (s α + s β1 +s β2 + s γ +s δ ) / 5 s sem = (s ε +s ζ ) / 2 s str = (s η +s θ ) / 2 21 Jan 2009 ThinkSpatial, UCSB 21 w vis w sem w str s vis *w vis s sem *w sem s str *w str s vis *w vis + s sem *w sem + s str *w str [Raubal & Winter 2002]

22 Wayfinding instruction [ AT landmark X ] [ TURN LEFT ] RIGHT MOVE STRAIGHT ] { ONTO streetname Stephansplatz } } { PASSING Stephansdom, CROSSING landmark a visually salient } world cultural heritage building } [ UNTIL landmark Haas building, ] a big building of architectural significance ] 21 Jan 2009 ThinkSpatial, UCSB 22

23 At large yellow building turn left. Walk straight until light green historical building. Turn right 21 Jan 2009 ThinkSpatial, UCSB 23

24 User groups Human participants test on people s landmark selection [Winter et al. 2005] Differences with regard to age, gender, and familiarity with the environment. Differences in landmark selection between day and night. Relative importance of salience measures. Behavioral variation => different weight sets for automatic landmark extraction. 21 Jan 2009 ThinkSpatial, UCSB 24

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26 Representing different contexts People select different landmarks by day and night. Weights from subjects scoring of facades. Area Shape Color Visibility Identif. Day Night [Winter, Raubal, Nothegger 2005] 21 Jan 2009 ThinkSpatial, UCSB 26

27 Hotel-Finder Service Influence of different user groups and decision strategies on outcomes of location-based analyses. Ultimate goal: service personalization for the individual. Often user types instead of each user as an individual. [Rinner & Raubal 2004] 21 Jan 2009 ThinkSpatial, UCSB 27

28 Task: find suitable hotel in unfamiliar environment. Current LBS: all hotels within 500m User criteria: proximity price free WiFi check-out time 21 Jan 2009 ThinkSpatial, UCSB 28

29 Approach Subjectively weighted criteria Integrating qualitative user preferences & multi-criteria decision analysis Compensatory vs. non-compensatory decision rules OWA (Ordered Weighted Averaging): Aggregate individual criteria to total evaluation score [Yager 1988] 21 Jan 2009 ThinkSpatial, UCSB 29

30 Interactive approach 1. Select decision criteria. 2. Express preferred criteria values on qualitative scale. 3. Define importance of each criterion. 4. Define personal decision strategy through settings of OWA method. 21 Jan 2009 ThinkSpatial, UCSB 30

31 Calculation b ik = w j a ij (b i1 > > b in ) a ij standardized value of alternative i for criterion j w j weight of criterion j => Alternative i: s i = Σv k b ik v k order weight for the k-th position in reordered sequence of b ik 21 Jan 2009 ThinkSpatial, UCSB 31

32 Decision strategies Cognitive component: How much risk does the user take? v k = (k/n) α -((k-1)/n) α [Yager 1988] 21 Jan 2009 ThinkSpatial, UCSB 32

33 Implementation Data for Toronto, Münster Analysis of different user types (business traveler, tourist, low-budget tourist) 21 Jan 2009 ThinkSpatial, UCSB 33

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35 Outcomes Personalized results through variation of user type & decision strategy. User preferences => parameters for multicriteria decision analysis. HPT => applying multi-criteria decision strategy enhances people s decision support in unfamiliar environments. [Bäumer, Panov, Raubal 2007] 21 Jan 2009 ThinkSpatial, UCSB 35

36 Critical issues Calculation 21 Jan 2009 ThinkSpatial, UCSB 36

37 Critical issues Calculation User interface 21 Jan 2009 ThinkSpatial, UCSB 37

38 Conclusions GIScience needs cognitive engineering! Geospatial services must be designed and engineered by incorporating principles of human cognition with respect to spatial and temporal aspects of phenomena. This will facilitate HCI and provide highquality support for their users spatiotemporal decision-making. 21 Jan 2009 ThinkSpatial, UCSB 38

39 Conclusions cont. Geospatial services are unique in the way they incorporate spatial & temporal data => spatial cognitive engineering. Users of GI differ in their cognitive styles, abilities, and preferences => personalization of geospatial services. Framework for classifying cognitive user parameters into generic, group, and individual. 21 Jan 2009 ThinkSpatial, UCSB 39

40 Future work Formal Conceptual Representations processing of geographic knowledge validity and limitations of conceptual representations cognitively based spatio-temporal ontologies Spatio-Temporal Decision-Making aspects of uncertainty and vagueness effects of geographic and temporal scales on people s information requirements 21 Jan 2009 ThinkSpatial, UCSB 40

41 Future work cont. Mobile context 21 Jan 2009 ThinkSpatial, UCSB 41 [Raubal & Panov 2009]

42 Cognitive Surveying M.-P. Kwan (Dara-Abrams 2008) 21 Jan 2009 ThinkSpatial, UCSB 42

43 Interdisciplinarity Geography / Geosciences Cognitive Sciences Geoinformatics Computer Science Social Sciences 21 Jan 2009 ThinkSpatial, UCSB 43

44 Thank you for your attention! 21 Jan 2009 ThinkSpatial, UCSB 44

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