1. CHARACTERIZING SPATIAL DATA UNCERTAINTY IN GIS
Ashton Shortridge
Michael Goodchild
Pete Fohl

2. Uncertainty: the gap between a spatially extensive phenomenon and its digital representation.

3. Propagation: manner in which data error affects the results of operations on the data.

4. Accuracy Reporting
Error may be known at a small number of locations, and uncertainty about the data may be communicated for the entire dataset from this sample. Examples:
USGS 7.5’ DEMs: RMSE of 28 points
Landcover: Confusion matrix or PCC

5. Global accuracy measures are INADEQUATE
Assume no trends in accuracy exist throughout the data domain.
Ex: Absolute magnitude of DEM elevation error correlated with elevation.
Assume independence of error at neighboring locations.
Error always spatially autocorrelated.

6. Uncertainty Model
Models are used to characterize uncertainty at every point (or at some subset of points which are of interest) in a data set.
Model uses existing data and information about accuracy of that data.
Model must characterize the spatial persistence, or autocorrelation, of the phenomenon.
Model produces a statistically valid realization of the true phenomenon.

7. Uncertainty Simulation

8. Summarizing Results What percentage of a landcover map is wetland?
Calculate a 200 meter buffer around all wetlands.
Generate a suitability map.

9. An Example Gap land cover map for Goleta Quad, CA.
Truth data from McGwire (1992)
Uncert. model: Goodchild & Wang (1988)
Confusion Matrix:

10. Model Discussion
Filter used to model spatial dependence of phenomenon - ad hoc.
Doesn’t honor matrix class proportions.
Some boundaries shift more than others
Inclusions dependent on matrix probability
Variance generally reduced by filter, mean affected in complex ways

11. Proposed GIS/metadata linkage for uncertainty modeling
User specifies an application to the GIS.
Application consists of a series of operations on the data, culminating in an “answer”, or application result.
Data includes uncertainty metadata:
Appropriate model
Parameters for the model

12. Role of GIS GIS should: Identify the type of result desired
Identify the manner in which uncertainty may affect that outcome
Employ the appropriate uncertainty model
Summarize results to user

13. Plenty of issues remain....
Identification of appropriate models
Development of models in data production
Linkage of GIS to metadata/propagation routines
Communication of results
Education of user community