Download - Lecture 10 - Mapping Uncertainty
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7/24/2019 Lecture 10 - Mapping Uncertainty
1/12
EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
GEO 309 Spring 2010
Mapping Uncertainty
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
In 1970, Johnny Cash (the Man in Black) asked:
What is Truth?
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
According to Hunter and Goodchild
(1993), when inaccuracy is known
objectively, it can be expressed as
error; when it is not known, the term
uncertainty applies.
Uncertainty about uncertainty
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EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Gershons taxonomy of imperfect knowledge (1998)
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Pangs definitions for uncertainty
Errorcan be defined as the discrepancy between agiven value and its true value
Inaccuracyis the difference between the given valueand its modeled or simulated value
Validityencompasses both the accuracy of the datathemselves and the procedures applied to the data.
Data qualityis an even more general term thatincludes data validityand data lineage.
From Visualizing Uncertainty in Geo-spatial Data by A. Pang, 2001
How do we map what were not sure of?
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Three Sources of Uncertainty
1. The raw data
itself2. The way these
data are
processed
3. Visualization
approach
Pang et al., 1997
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EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Three Sources of Uncertainty
For every map we look at, we need to askourselves what we can know for certain.
If we cant be sure about something, why not?Which of the 3 sources of uncertainty is the
cause? Is it more than one?
1. Questionable data
2. Questionable processing
3. Questionable visualization
??
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Categories for assessing data quality
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Categories for assessing data quality
1. Lineage
2. PositionalAccuracy
3. Attribute
Accuracy
4. Completeness
5. Logical
Consistency
http://mcmcweb.er.usgs.gov/sdts/SDTS_standard_oct91/part1.html#p122
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EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Categories for assessing data quality
Historical metadatadescribing the
development of the
data, e.g., digitized or
scanned, scale and
projection
http://mcmcweb.er.usgs.gov/sdts/SDTS_standard_oct91/part1.html#p122
1. Lineage
2. Positional
Accuracy
3. Attribute
Accuracy
4. Logical
Consistency
5. Completeness
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Categories for assessing data quality
Locational accuracy of
geographic features,
both horizontal and
vertical
http://mcmcweb.er.usgs.gov/sdts/SDTS_standard_oct91/part1.html#p122
1. Lineage
2. Positional
Accuracy
3. Attribute
Accuracy
4. Logical
Consistency
5. Completeness
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Categories for assessing data quality
Are the attributesassociated with this
location correct?
For example,
address, land use
classification,
temperature, etc.
http://mcmcweb.er.usgs.gov/sdts/SDTS_standard_oct91/part1.html#p122
1. Lineage
2. PositionalAccuracy
3. Attribute
Accuracy
4. Logical
Consistency
5. Completeness
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7/24/2019 Lecture 10 - Mapping Uncertainty
5/12
EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Categories for assessing data quality
1. Lineage
2. Positional
Accuracy
3. Attribute
Accuracy
4. Logical
Consistency
5. Completeness
http://mcmcweb.er.usgs.gov/sdts/SDTS_standard_oct91/part1.html#p122
Topological integrity of the spatial
data, including:1. Do lines intersect only where intended?
2. Are any lines entered twice?3. Are all areas completely described?4. Are there any overshoots or undershoots?
5. Are any polygons too small, or any linestoo close, or polygon slivers?
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Categories for assessing data quality
http://mcmcweb.er.usgs.gov/sdts/SDTS_standard_oct91/part1.html#p122
1. Lineage
2. Positional
Accuracy
3. Attribute
Accuracy
4. Logical
Consistency
5. Completeness
Completeness has two components:spatial completeness and attribute
completeness. For example, it can refer
to how representative the sample of
objects is compared to all objects in the
universe, or whether standard
definitions are being used.
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Categories for assessing data quality
1. Lineage
2. PositionalAccuracy
3. Attribute
Accuracy
4. Logical
Consistency
5. Completeness
Most important in the
visualization of spatial data
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EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty
Visual variables
Intrinsic (already part of the display)
Extrinsic (objects added to the display)
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty
Spatial uncertainty isencoded as gaps in
contour lines. The moreuncertain, the larger thegaps. The contour lines
themselves are for someother variable such astemperature, humidity, etc.
A. Pang, Visualizing Uncertainty in Geo-spatial Data, 2001
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Clarity (MacEachren)
Crispness, Resolution & Transparency
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7/24/2019 Lecture 10 - Mapping Uncertainty
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EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Clarity (MacEachren)
Crispness, Resolution & Transparency
Judith Tyner, Principles of Map Design, 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Clarity (MacEachren)
Crispness, Resolution & Transparency
Fuzzy Britain, and Truth in Maps,Ben Terrett, 2008
(Of course,anything can
be taken to
extremes .)
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Clarity (MacEachren)
Crispness, Resolution & Transparency
High
Resolution
LowResolution
http://strangemaps.wordpress.com/2008/11/11/328-fuzzy-britain-and-truth-in-maps/http://strangemaps.wordpress.com/2008/11/11/328-fuzzy-britain-and-truth-in-maps/http://strangemaps.wordpress.com/2008/11/11/328-fuzzy-britain-and-truth-in-maps/http://strangemaps.wordpress.com/2008/11/11/328-fuzzy-britain-and-truth-in-maps/http://strangemaps.wordpress.com/2008/11/11/328-fuzzy-britain-and-truth-in-maps/ -
7/24/2019 Lecture 10 - Mapping Uncertainty
8/12
EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Clarity (MacEachren)
Crispness, Resolution & Transparency
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Other approaches (Edwards & Nelson)
Legend
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Other approaches (Edwards & Nelson)
Reliability
Diagram
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EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Other approaches (Edwards & Nelson)
Focus
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Depicting uncertainty (cont.)
Other approaches (Edwards & Nelson)
Value
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Intentional uncertainty
http://gis.chicagopolice.org/CLEARMap/startPage.htm#
Block is shown, but
not the address
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EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Intentional uncertainty (cont.)What ISNT this maptelling us?
What dont we
know?
What cant we besure of?
Why not?
http://map.measureofamerica.org/maps.aspx
The HIPAA Privacy
Rule protects the
privacy of individuallyidentifiable health
information (1996)
Govt.-mandated
uncertainty???
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Unintentional uncertainty
Density maps, often used in
mapping crime hot spots,
may create the impression ofcriminal activity (or its
likelihood) where there is no
empirical evidence for it.
Aldo Miranda, RTI International
Mapping of crime hot-spots in
Salvadoran municipalities highlight
patterns of local crime and violence,
like this map showing robbery
incidents (orange dots) in and around
public housing (black triangles)
Questionable processQuestionable visualization
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Unintentional uncertainty
Density maps, often used in
mapping crime hot spots,
may create the impression ofcriminal activity (or its
likelihood) where there is no
empirical evidence for it.
http://jamescousins.com/2009/03/london-shootings-heat-map/
Heat (intensity) map compiled from
100 shooting related incidents in and
around London, March, 2009
Questionable processQuestionable visualization
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7/24/2019 Lecture 10 - Mapping Uncertainty
11/12
EO 309 Intro. to Spatial Analysis:apping Uncertainty
pring 2010
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Remember
For every map we look at, we need to askourselves what we can know for certain.
If we cant be sure about something, why not?
1. Questionable data
2. Questionable processing
3. Questionable visualization
??
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Decision Making with Uncertainty
The research seems to take for granted that
visual depictions of uncertainty are useful fordecision making.-Fabien Girardin
But, does letting the map reader knowwhat is uncertain and to what degree it is
uncertain really help in making
productive decisions based on the data?
F. Girardin, 2006, http://liftlab.com/think/fabien/
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Decision Making with Uncertainty
Behavioral economists Tversky and Kahneman (1974) note a
conflict: some experts are dependent on statistical analyses
to incorporate uncertainty into their decision, but lay users
tend to ignore or misinterpret statistical probabilities and
instead rely on less accurate heuristics when makingdecisions.
That is, most people ignore or misinterpret
the explicit display of uncertainty and dont
end up using that information in their
decision-making processes
F. Girardin, 2006, http://liftlab.com/think/fabien/
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EO 309 Intro. to Spatial Analysis:apping Uncertainty
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Decision Making with Uncertainty
Girardin poses two interesting questions:
1. Will providing information about data uncertainty in
an explicit visual way help a lay or expert mapreader make different decisions?
2. IF they do make different decisions, will provision of
information about data uncertainty lead to better,more correct, decisions or simply cause analysts to
discount the unreliable information?
F. Girardin, 2006, http://liftlab.com/think/fabien/
Definitely worth thinking more about
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Decision Making with Uncertainty
Cliburn et al. (2002) address the idea of makingdecisions based on uncertain data with the help of
uncertainty representations. They list the depictionof uncertainty as a drawback, because policy makers
(the users for their study) typically want issuespresented without ambiguity. One participant in
their study suggested that a depiction of un-certainty could be used to discredit the modelsrather than having the intended effect of signalingunbiased results.
F. Girardin, 2006, http://liftlab.com/think/fabien/
GEO 309 Intro. to Spatial Analysis: Visualizing Uncertainty
Additional (uncertain) resources
Visualizing Uncertainty in Geo-spatial Data by A. Pang,University of California, Santa Cruz
http://www.spatial.maine.edu/~worboys/SIE565/papers/pang%20viz%20uncert.pdf
UC Santa Cruz Laboratory for Visualization and Graphics:
http://slvg.soe.ucsc.edu/index.html
Gershon, N. D. 1998. Visualization of an imperfect world.
Computer Graphics and Applications (IEEE) 18(4): 43-5.
F. Girardin, http://liftlab.com/think/fabien/
And, just for fun
Judgment under Uncertainty: Heuristics and Biases. A. Tversky;D. Kahneman, 1974
http://www.hss.caltech.edu/~camerer/Ec101/JudgementUncer
tainty.pdf
http://www.spatial.maine.edu/~worboys/SIE565/papers/pang%20viz%20uncert.pdfhttp://www.spatial.maine.edu/~worboys/SIE565/papers/pang%20viz%20uncert.pdfhttp://slvg.soe.ucsc.edu/index.htmlhttp://ieeexplore.ieee.org/iel4/38/15110/00689662.pdfhttp://liftlab.com/think/fabien/http://liftlab.com/think/fabien/http://ieeexplore.ieee.org/iel4/38/15110/00689662.pdfhttp://slvg.soe.ucsc.edu/index.htmlhttp://www.spatial.maine.edu/~worboys/SIE565/papers/pang%20viz%20uncert.pdfhttp://www.spatial.maine.edu/~worboys/SIE565/papers/pang%20viz%20uncert.pdf