Objectives Differentiate accuracy, precision, error, and uncertainty. Discuss the dimensions of geographic data quality. Discuss how to compute RMSE for.

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Presentation transcript:

Objectives Differentiate accuracy, precision, error, and uncertainty. Discuss the dimensions of geographic data quality. Discuss how to compute RMSE for positional accuracy. Describe why data standards are beneficial Key terms: metadata

Accuracy—how close to “true” Precision—how exactly measured and stored Error—deviation from “true” value Uncertainty—lack of confidence due to incomplete knowledge

Inherent = Source Operational = user or processing

Semantic Discrepancies

RMSE = sqrt(average(squared discrepancies)) x, y, and z (or e) p = sqrt(x²+ y²) (Positional) Use p and e for map overall

Metadata—Geographic Data Quality Lineage Positional accuracy Attribute accuracy Logical consistency Completeness

Spatial autocorrelation

Sampling

Standards vs Translators

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