1) The document discusses various techniques for visualizing geospatial data and representing uncertainty in geospatial information. It proposes a typology of uncertainty sources including accuracy, precision, completeness, consistency, lineage, currency/timing, and credibility. 2) Static and dynamic visualization methods are described for representing uncertain geospatial data. Techniques like transparency, color manipulation, and animated blurring can indicate levels of uncertainty. 3) The role of visualization in the scientific research process is discussed. Visualizations can be used to present known information, reveal unknowns, test hypotheses, and discover new hypotheses.

1. Geographie et visualisations
Relativité des présentations d’informations

2. Spatial is special

3. Les acteurs de l'Internet isarien
Nombre de sites
1
2
Noyon
5
10
Saint-Just-en-Chaussée
20
Beauvais Compiègne 40 et +
Pont-Ste-Maxence
Particuliers
Creil
Méru Senlis Associations
Crépy-en-Valois
Chantilly Acteurs publics et
para-publics
0 5 10 km Entreprises, artisans
et indépendants
Source : crawl de janvier 2005, renupi.org

4. Santerre-Oise
79%
68%
Beauvais
Compiègne
Origine
du lien
200 liens
Destination
du lien
Sud-Oise
0 5 10 km
Source : crawl de janvier 2005, renupi.org
73%

8. Spatial (and everything else) is build

9. première loi de la géographie de Tobler :
chaque phénomène est relié à tous les autres, mais des
phénomènes proches dans l'espace auront tendance à être
d'avantage liés que des phénomènes éloignés

10. De quoi est-on certain ?

12. ecision-making process.
involves propagating
n uncertainty measures,
scenario identification
he risk in decisions that
mya and Hunter (2002)
ethods for reducing the
ate to the concept of
nclude the practice of
vidual accepts the risk
cope with it and the
ntity to another, either
tractual agreement such
a purchased insurance
est their risk analysis
h users, however. They Figure 1. Impact of ambiguity and deception on success
blem of how to signify of intelligence analysis. [After Graves (2000); reproduced
by permission.]

13. Table 1 Types of uncertainty in four models of geographic space (Source: Gahegan and Ehlers, 2000)
visualization that matches data type—scalar, lead to more rather than less uncertainty about the
multivariate, vector, and tensor—to visualization data depicted.
form—discrete and continuous. For each of the eight
cells in the resulting matrix (e.g., continuous scalar Typology of geospatial intelligence information
data, discrete multivariate data), they proposed some uncertainty
logical representation methods, including both static
and dynamic representation forms. Building on the typology efforts above, three of the
From an InfoVis rather than SciVis perspective, current authors and two additional colleagues
Gershon (1998) took a very different approach than propose a typology of uncertainty relevant to
Pang, focusing on kinds of “imperfection” in the geospatial information visualization in the context of

14. Uncertainty sources
Accuracy/error : difference between observation and reality
Precision : exactness of measurement
Completeness : extent to which info is comprehensive
Consistency : extent to which info components agree
Lineage : conduit through which info passed
Currency/timing : temporal gaps between occurrence, info collection & use
Credibility : reliability of info source
Subjectivity : amount of interpretation or judgment included
Interrelatedness : source independence from other information

15. Precision ! Precision of collection ! resolution of satellite imagery
capability
Completeness ! Composite completeness ! images of a site may not be available on
! Information completeness a particular day, due to adverse weather
! Incomplete sequence conditions.
! an intercepted conversation may have
words that were not clear
! the lack of confirming information
might signal incompleteness
Consistency ! Multi-INT Conflict ! multiple sources may actually conflict
! Model/observation ! models of events may differ from
Consistency observations
Lineage ! Translation ! Machine translation is more uncertain
! Transformation than human linguist translation
! Interpretation ! Measurements or signals may have been
transformed
! Information that comes directly from the
collection capabilities has a different
lineage than an interpretive report
produced by an analyst
Currency/timing ! Temporal gaps ! Images that show new objects do not
! Versioning show when the object first appeared
! Time between when events occurred,
when they were reported, and when the
information is available to analysts
! Reports may have multiple versions,
sometimes with major changes.
Credibility ! Reliability ! Possibility of deliberate disinformation

16. Table 2: Typology of uncertainty of geospatial information, (Adapted from (Thomson et al., 2004).
representing uncertain information using static used by Gershon (1992) who created an application
methods. Others have also emphasized the creative that animated through increasingly blurred versions
usage of color attributes to signify uncertainty. Jiang of data to signify fuzzy sea-surface temperature data
et al. (1995) described a technique in which hue, (see dynamic representation section below).
lightness, and saturation are manipulated to depict MacEachren’s initial application of transparency

17. Geographical Information System

20. Visualization in the Earth Sciences at Penn State 15/02/09 21:52
Figure 1. The range of functions of visual methods in an
idealized research sequence.
Despite this bias, visual methods are common and perform a range of
functions in scientific research. Figure 1 idealizes the research process as a

24. 125
Figure 42: Visualization of music albums with cover art (see appendix A.7). (A) A table of
musical genres shows top picks by moving selected rows to the top. (B) The albums table is
sorted on increasing distance from the current mouse point in the (track, time, year)
scatter plot matrix. (C) Compound brushing of albums. Names are drawn in black if selected

25. 176
Figure 75: A visualization of county-level election results for the State of Michigan from 1998
to 2004 (see appendix A.3). A tinted lens highlights views, using labeled arrows to reveal
coordination on the user’s selection of counties in the Votes v. Counties scatter plot.
user with a tabbed page metaphor (figure 75). Internal frames contain a tree of panels in which
views and other controls are the leaves. Metavisualizations are stored in the same XML file

26. (geo)Web Information System ?

27. présenter révéler
contrainte
le connu l'inconnu liste,
contrainte
synthèse et tableau
tester les présentation
visualisation
visualisation hypothèses
scénario de
découverte
trouver de
libre
nouvelles hypothèses cartes,
libre
graphes
sphère privée : sphère publique :
réflexion communication