“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? 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

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

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

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 5 How 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 map Linear Function of increasing probability Could 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% certainty Shadow map says differently!

Nearly HALF the map is fairly uncertain of 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 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

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 at http://www. ncgia. ucsb