GIS Error and Uncertainty

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GIS Error and Uncertainty

• Longley et al., chs. 6 (and 15)
• Sources Berry online text,
• Dawn Wright

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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

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Uncertainty

• Attribute uncertainty (Forest vs. Ag)
• Positional uncertainty
• Definitional uncertainty
• Measurement uncertainty

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The Necessity of Fuzziness

• Its not easy to lie with maps, its
  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

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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 right
• If it has lots of decimal places, it must be
  accurate

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A conceptual view of uncertainty (U), Longley et
al., chapter 6
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Nick Chrismans View(www.wiley.com/college/chrism
an/define.html )
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Longley et al., chapter 6, pages 132-133
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Error induced by data cleaning, Longley et al.,
chapter 6, pages 132-133
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Merging. Longley et al., chapter 6, pages 132-133
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Uncertainty

• Measurements not perfectly accurate
• Maps distorted to make them readable
• Lines repositioned
• Canal and Railroad
• At this scale both objects thinner than map
  symbols
• Map is generalized
• Definitions vague, ambiguous, subjective
• Landscape has changed over time

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Forest Type
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Soil Type
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Assessing the Fuzziness

• Positions assumed accurate
• But 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

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Source Diagram
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Polygon Overlay
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Search For Soil 2 Forest 5How Good Given
Uncertainty in Input Layers?
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Spread boundary locations to a specified
distanceZone of transition, Cells on line are
uncertain
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Code cells according to distance from boundary,
which relates to uncertainty
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Based on distance from boundary, code cells with
probability of correct classification
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Same thing for Forest mapLinear Function of
increasing probabilityCould also use
inverse-distance-squared
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Overlay soil forest shadow maps to get joint
probability mapProduct of separate probabilities
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Original overlay of S2/F5Overlay implied 100
certaintyShadow map says differently!
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Nearly HALF the map is fairly uncertainof the
joint condition of S2/F5
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Towards an Honest GIS

• 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

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More Strategies

• 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
• Research on modeling uncertainty (NCGIA Intiative
  1)
• Users cant understand them all

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Strategies (cont.)

• Producer of data must describe uncertainty
• RMSE 7 m
• Metadata
• SDTS - 5 elements (semantic)
• Positional accuracy
• Attribute accuracy
• Logical consistency (logical rules? polygons
  close?)
• Completeness
• Lineage

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Strategies (cont.)

• Not effective
• 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

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Simulation Examplehttp//www.ncgia.ucsb.edu/ash
ton/demos/propagate.html
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Visualizing Uncertainty