Honest gis error and uncertainty l.jpg
Sponsored Links
This presentation is the property of its rightful owner.
1 / 27

“Honest GIS”: Error and Uncertainty PowerPoint PPT Presentation

  • Updated On :
  • Presentation posted in: General

“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

Related searches for “Honest GIS”: Error and Uncertainty

Download Presentation

“Honest GIS”: Error and Uncertainty

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

“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

    • Not necessarily objective, scientific

    • measurements

  • Impossible to create perfect representation of world

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.)

  • 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”

A conceptual view of uncertainty (U), Longley et al., chapter 6

Longley et al.,

1/e ch. 6, p. 132

2/e ch. 9, p. 208

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

Yikes! Rubbersheeting needed please! Longley et al., 1/e ch. 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

Soil Type

Assessing the Fuzziness

  • 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

Search For Soil 2 & Forest 5How Good Given Uncertainty in Input Layers?

Spread boundary locations to a specified distance:Zone of transition, Cells on line are uncertain

Code cells according to distance from boundary, which relates to uncertainty

Based on distance from boundary, code cells with probability of correct classification

Same thing for Forest mapLinear Function of increasing probabilityCould also use inverse-distance-squared

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

Original overlay of S2/F5:Overlay implied 100% certaintyShadow map says differently!

Nearly HALF the map is fairly uncertainof the joint condition of S2/F5

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

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

    • Recent research on modeling uncertainty (NCGIA Intiative 1)

    • Users can’t understand them all

Strategies (cont.)

  • 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 (cont.)

  • 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 athttp://www.ncgia.ucsb.edu/~ashton/demos/propagate.html

  • Login