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“Honest GIS”: Error and Uncertainty . Longley et al., 1/e, chs. 6 and 15 Longley et al., 2/e, ch. 6 See also GEO 565 Lecture 12 Berry online text. Blinded by Science?. Result of “accurate” scientific measurement Reveal agenda, biases of their creators. GIS databases built from maps

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Honest gis error and uncertainty l.jpg

“Honest GIS”:Error and Uncertainty

Longley et al., 1/e, chs. 6 and 15

Longley et al., 2/e, ch. 6

See also GEO 565 Lecture 12

Berry online text


Blinded by science l.jpg
Blinded by Science?

  • Result of “accurate” scientific measurement

    • Reveal agenda, biases of their creators

  • GIS databases built from maps

    • Not necessarily objective, scientific

    • measurements

  • Impossible to create perfect representation of world


The necessity of fuzziness l.jpg
The Necessity of “Fuzziness”

  • “It’s not easy to lie with maps, it’s essential...to present a useful and truthful picture, an accurate map must tell white lies.” -- Mark Monmonier

  • distort 3-D world into 2-D abstraction

  • characterize most important aspects of spatial reality

  • portray abstractions (e.g., gradients, contours) as distinct spatial objects


Fuzziness cont l.jpg
Fuzziness (cont.)

  • All GIS subject to uncertainty

  • What the data tell us about the real world

  • Range of possible “truths”

  • Uncertainty affects results of analysis

  • Confidence limits - “plus or minus”

    • Difficult to determine

  • “If it comes from a computer it must be wright”



Slide6 l.jpg

Longley et al., chapter 6

1/e ch. 6, p. 132

2/e ch. 9, p. 208


Slide7 l.jpg

Error chapter 6induced by data cleaning, Longley et al., 1/e ch. 6, p. 132, 2/e ch. 9, p. 209



Uncertainty l.jpg
Uncertainty 6, p. 132, 2/e ch. 9, p. 209

  • Measurements not perfectly accurate

  • Maps distorted to make them readable

    • Lines repositioned

    • 5th St. and railroad through Corvallis at scale of 1:250,000

    • At this scale both objects thinner than map symbols

  • Map is generalized

  • Definitions vague, ambiguous, subjective

  • Landscape has changed over time


Forest type l.jpg
Forest Type 6, p. 132, 2/e ch. 9, p. 209


Soil type l.jpg
Soil Type 6, p. 132, 2/e ch. 9, p. 209


Assessing the fuzziness l.jpg
Assessing the Fuzziness 6, p. 132, 2/e ch. 9, p. 209

  • positions assumed accurate

  • really just best guess

  • differentiate best guesses from “truth”

  • “shadow map of certainty”

    • where an estimate is likely to be the most accurate

  • tracking error propagation


Polygon overlay l.jpg
Polygon Overlay 6, p. 132, 2/e ch. 9, p. 209


Search for soil 2 forest 5 how good given uncertainty in input layers l.jpg
Search For Soil 2 & Forest 5 6, p. 132, 2/e ch. 9, p. 209How Good Given Uncertainty in Input Layers?


Spread boundary locations to a specified distance zone of transition cells on line are uncertain l.jpg
Spread boundary locations to a specified distance: 6, p. 132, 2/e ch. 9, p. 209Zone of transition, Cells on line are uncertain




Slide19 l.jpg
Same thing for Forest map of correct classificationLinear Function of increasing probabilityCould also use inverse-distance-squared


Overlay soil forest shadow maps to get joint probability map product of separate probabilities l.jpg
Overlay soil & forest shadow maps to get joint probability map:Product of separate probabilities


Original overlay of s2 f5 overlay implied 100 certainty shadow map says differently l.jpg
Original overlay of S2/F5: map:Overlay implied 100% certaintyShadow map says differently!


Nearly half the map is fairly uncertain of the joint condition of s2 f5 l.jpg
Nearly HALF the map is fairly uncertain map:of the joint condition of S2/F5


Towards an honest gis l.jpg
Towards an “Honest GIS” map:

  • can map a simple feature location

  • can also map a continuum of certainty

  • model of the propagation of error (when maps are combined)

  • assessing error on continuous surfaces

    • verify performance of interpolation scheme


More strategies l.jpg
More Strategies map:

  • Simulation strategy

    • Complex models

    • Describing uncertainty as “a spatially autoregressive model with parameter rho” not helpful

    • How to get message across

  • Many models out there

    • Recent research on modeling uncertainty (NCGIA Intiative 1)

    • Users can’t understand them all


Strategies cont l.jpg
Strategies (cont.) map:

  • Producer of data must describe uncertainty

    • “RMSE 7 m” (Lab 6, your Mt. Hood DEM)

    • Metadata

  • FGDC - 5 elements

    • Positional accuracy

    • Attribute accuracy

    • Logical consistency (logical rules? polygons close?)

    • Completeness

    • Lineage


Strategies cont26 l.jpg
Strategies (cont.) map:

  • What impact will uncertainty have on results of analysis??

    (1) Ignore the issue completely

    (2) Describe uncertainty with measures (shadow map or RMSE)

    (3) Simulate equally probable versions of data


Simulation example try it yourself at http www ncgia ucsb edu ashton demos propagate html l.jpg
Simulation Example: map:Try it yourself athttp://www.ncgia.ucsb.edu/~ashton/demos/propagate.html


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