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CHARACTERIZING SPATIAL DATA UNCERTAINTY IN GIS

CHARACTERIZING SPATIAL DATA UNCERTAINTY IN GIS. Ashton Shortridge Michael Goodchild Pete Fohl. Uncertainty: the gap between a spatially extensive phenomenon and its digital representation. Propagation: manner in which data error affects the results of operations on the data.

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CHARACTERIZING SPATIAL DATA UNCERTAINTY IN GIS

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  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

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