1. “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

2. Blinded by Science? GIS databases built from maps
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

3. 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

4. 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”

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

6. Longley et al.,
1/e ch. 6, p. 132
2/e ch. 9, p. 208

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

8. Yikes. Rubbersheeting needed please. Longley et al. , 1/e ch. 6, p
Yikes! Rubbersheeting needed please! Longley et al., 1/e ch. 6, p. 132, 2/e ch. 9, p. 209

9. Uncertainty 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

11. Forest Type

12. Soil Type

13. 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

14. Polygon Overlay

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

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

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

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

19. Same thing for Forest map Linear Function of increasing probability Could also use inverse-distance-squared

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

21. Original overlay of S2/F5: Overlay implied 100% certainty Shadow map says differently!

22. Nearly HALF the map is fairly uncertain of the joint condition of S2/F5

23. 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

24. More Strategies Simulation strategy Many models out there
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

25. 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

26. 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

27. Simulation Example: Try it yourself at http://www. ncgia. ucsb