representation - Department of Geography

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esf geovis 2002, mar. 14, 2002

rererepresentation

sara irina fabrikantgeography departmentuc santa barbara, u.s.a

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René Magritte

a famous pipe

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representation

is the cat’s tail represented in above graphic?

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1Mark and Smith (ongoing)

representation

is western Wyoming represented in above map?

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overview

motivation

background

building of theory

application of theory

conclusion outlook

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ontologyrepresentation follows reality...

1. philosopher: “the nature of what exists”

– ontology <> epistemology

– what/why is it: ponder nature and organization of stuff

• things, parts and wholes, relationships

2. information scientist: “explicit account ofconceptualization”

– ontologies <> data models

– what is it for: solve the tower of babel problem

• formalisms to share knowledge across communities

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why care for GeoVis?

representation formalisms beyond depiction

• generalization

– taxonomic relations: a map is-a kind of graphic representation

– semantic and semiotic abstractions: distance is proximity

• association

– membership relations: choro maps/cartograms are value-by-area maps

– semantic and semiotic mappings: magnitude is like size

• aggregation

– part-whole relations: a legend is-a-part of a map

– level of detail, scale: a county is-a part of an enumeration hierarchy

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proximity is like similarity

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Jung CG (1964)

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of parts and wholes

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Levy (1990)

overview

motivation

background

building of theory


conclusion outlook

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GeoVis < Y2K

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MacEachren (1994)

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GeoVis is like InfoVis

• dealing with the information overload problem– information space is always multidimensional (automation)

– GeoVis: 1-3D usually reserved

– cognizable space is 3/4 dimensional (human processing)

• graphics as cognitive aids for knowledge discovery (EDA)– task models for problem solving

• data and space transformations

– raw data � data table � visual structures

• spaces as visual structure

– metric space (spherical, hyperbolic, cartesian grid)

– topological space (node-link networks, trees)

• direct data manipulation/collaboration through GUIs

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InfoVis3

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wwwwhhhhyyyy //// wwwwhhhhaaaatttt ffffoooorrrr ????

wwwwiiiitttthhhh wwwwhhhhoooommmm ????

wwwwhhhhaaaatttt ????

InfoVis3

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CS/EEHCI

cartography

HCIcognitive science

information scienceGIScience etc.

technical/engineering issues...

• scalability

• graphics/information density

• speed and efficiency

theoretical/scientific issues...

• lack of theory– what are the foundations?

• usability– does it work?

InfoVis challenges

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cognitively plausible representations to solve problems

• task primitives?– task ontology

• semantic primitives?– domain ontology

• semiotic primitives?

– Bertin Y2K (+ time!)

• theory informed by empirical evidence

� cognitively adequate representations!

problem-driven representation

mappings, metaphors

data transformations

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cognitively plausible InfoVis

metaphorssemantic gen.semiotic gen.

task modelsprimitives

space/time typestransformations

sign-vehiclesmappings

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task primitives?• task models in geovisualization

– goals: why visualize?

– tasks: what needs to be visualized to solve goal?

• task analytical approaches

– psychology, cognitive science, HCI (Leont’ev, 1978; Nardi, 1995)

GOMS: Goals, Operators, Methods and Selection(Card, Moran, & Newell, 1980, 1983)

– task-based usability engineering(Lewis, 1990, Norman 1990, Nielsen, 1993)

– object-action model (Shneiderman, 1998)

• and for ill-structured problems

– Wehrend and Lewis (1990)

– Knapp (1995)

– Zhou and Feiner (1998)

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preservation of meaning through abstraction

• semantic generalization

– source domain ontologies

• geographic primitives: point, line, areal features

• formalized spatial structure: space types, scale continuum

graphic representation of meaning

• semiotic generalization

– target domain mappings

• semantic primitives: entities, trajectories, aggregates

• formalized symbology: sign-vehicles

InfoVis process

(Fabrikant, 2000; Fabrikant and Buttenfield, 2001)

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semantic generalization

metaphoricalmapping

ontological design

what exists

real worldgeographic environment

feature location

world has time

feature

scale

source domain

semantic entity

entity location

space is time

granularity

what it represents

semantic worlddata archive

target domain

object&

relations

object&

relations

contextcontext

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s o u r c e d o m a i n ss o u r c e d o m a i n ss o u r c e d o m a i n ss o u r c e d o m a i n ss o u r c e d o m a i n ss o u r c e d o m a i n s

w o r l d sw o r l d sw o r l d s

source domain ontologies

primitives navigable vista hierarchictopologic

experiential historic

entity landmark feature occurence object event(pit)

trajectory path route relation link event(pot)

boundary edge border partition boundary change

aggregate district region set container state

(see also Lynch, Golledge, Lakoff and Johnson, Szegö, Smith)

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source domain ontologies

s o u r c e d o m a i n ss o u r c e d o m a i n ss o u r c e d o m a i n ss o u r c e d o m a i n ss o u r c e d o m a i n ss o u r c e d o m a i n s

w o r l d sw o r l d sw o r l d s

primitives navigable vista hierarchictopologic

experiential historic

entity landmark feature occurence object event(pit)

trajectory path route relation link event(pot)

boundary edge border partition boundary change

aggregate district region set container state

(see also Lynch, Golledge, Lakoff and Johnson, Szegö, Smith)

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target domain mappings

semantic

primitives

semiotic

primitives

entity point

trajectory line

boundary line

aggregate area

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target domain mappings

semantic

primitives

semiotic

primitives

entity point

trajectory line

boundary line/area

aggregate area

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(Bertin, 1967, MacEachren, 1995)

target domain mappingspointpoint lineline areaarea

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semantic

primitives

semiotic

primitives

entity point

trajectory line

boundary line

aggregate area

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a space is a space is a space

it depends!

are a coffee cup and a donut the same?

...on spatial perspective, context, task etc.

naïve physics (Hayes, 1985)naïve geography (Egenhofer & Mark, 1995)

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space transformations• metric space � cognitive space � topological space

– physical space shapes navigational space shapes mental

space

a library ADL

(after Hillier, 1996)

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time-space transformations• metric space � movement space � cartesian space

– physical space shapes functional space shapes interaction

space

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ADL 3

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(after Imfeld, 2001)

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overview

motivation

background

building of theory

application example

conclusion outlook

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text database representation

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National Imagery and Mapping Agency (NIMA)(NMA-201-00-1-2005)

collaborators:

UCSB: D. Montello, M. Ruocco, R. Middleton

UB: D. Mark, C. Jorgensen

acknowledgements




• geographic primitives: features, regions, boundaries





• semantic primitives: entities, aggregates, trajectories


spatialization in two steps

(Fabrikant, 2000; Fabrikant and Buttenfield, 2001 )

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semantic news wire space

• unstructured reuters news articles– 504 full-text news stories

– snapshot: February 9/10, 2000

www.reuters.com/

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FOCUS-Confusion over Afghan hijack motives Jane BarrettAFGHAN PLANE 5THLD (PICTURE, GRAPHIC)STANSTED England British police restored calm on ahijacked Afghan airliner on Wednesday after the surpriseescape of the four-man flight crew had sent tension soaring.But confusion reigned over the motives of the hijackers. Kabul's national airline said it could be a mass asylum bid but Britishpolice said they were ``not aware of any asylumapplications.'' ``We are back on an even keel now -- overnightthere was a degree of tiredness and frustration,'' said aspokeswoman for Essex police handling the hijack at Stanstedairport east of London . Assistant chief constable Joe Edwardssaid the crew escaped using a rope ladder through a cockpitwindow at around 1045 GMT on Tuesday after ``up anddown'' negotiations with the hijackers, who took over the planeafter it took off from Kabul on Sunday. [...]

the reuters data base• XML extracts from starlight database (PNL)

• automatically indexed and formatted

one article out of 504

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latent semantic analysis (LSA)

• latent semantic indexing (LSI) (Deerwester et al., 1990)

– automatic indexing by latent associations of terms– based on semantic proximity

• large vector space of term-document associations

– two mode factor analysis (singular value decomposition)– SVD into term-term, doc-doc, term-doc matrices

– terms and documents are vectors in n-dimensional space

• angular proximity is similarity between documents

– e.g. cosine or dot product

• result: document * document proximity matrix

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• geographic primitives: features, regions, boundaries





• semantic primitives: entities, aggregates, trajectories


spatialization in two steps

(Fabrikant, 2000; Fabrikant and Buttenfield, 2001 )

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task primitivesdescribe, summarize, locate

features � entities

spring-node algorithm(Kamada and Kawai 1989)

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solid cluster effect

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Fabrikant et al. (ongoing)

DISCLAIM

ER:

prelim

inary re

sults!

task primitivescategorize, distinguish, compare

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intramax (Masser and Brown 1975),voronoi tessellation

regions � aggregatesscale 1:2

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task primitivesidentify, associate, locate

scale � level of detail

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task primitivesrelate, associate, emphasize

paths � trajectories

ppppaaaatttthhhhffffiiiinnnnddddeeeerrrr (Schvaneveldt and Durso, 1988)

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solid network effect


task primitivesenjoy the fruit salad!

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boundaries, paths � trajectories

combo effects?

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task primitivesfilter, reveal

mt. rigi in winter

Remote Sensing Lab, Geography Dept., University of Zürich

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information filterscale 1:2

information filterscale 1:4

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information densityscale 1:9

overview

motivation

background

building of theory


conclusions & outlook

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conclusions & outlooksummary

• ontological approach to representation– what are the primitives?– what are the transformations rules?

• thrive for cognitively plausible InfoVis3

• based on sound theory and formalisms

– towards problem-driven, collaborative representations

next steps...

• many more empirical evaluations needed

• cognitively adequate information visualizations!

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thank you.

esf geovis 2002, mar. 14, 2002

representation - Department of Geography

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