Map Accuracy

Map accuracy describes how closely mapped features and attributes match their true positions, shapes, and values. It encompasses positional accuracy (horizontal and vertical), attribute accuracy, temporal currency, and logical consistency (topology, completeness). Accuracy depends on source data quality, scale of compilation, projection, digitization tolerance, and generalization choices. Standards such as the National Map Accuracy Standards and ASPRS guidelines provide benchmarks, but modern practice emphasizes metadata with error statistics (RMSE, CE90/LE90) and fitness-for-use. Accuracy is contextual: a 5‑meter error might be fine for regional planning yet unacceptable for parcel surveys. Communicating uncertainty—confidence intervals, heatmaps of error, scale disclaimers—prevents misuse. QA/QC processes include checkpoints, cross-dataset comparisons, and automated topology tests (gaps, overlaps, dangles). Provide explicit methods, QA notes, and version history so others can reuse the layer responsibly. Provide explicit methods, QA notes, and version history so others can reuse the layer responsibly. Users often conflate visual sharpness with accuracy. A crisp hillshade can mask large vertical errors; explicitly separating cartographic appearance from quantitative accuracy helps prevent misinterpretation. Including small ‘truth panels’ that show checkpoints and residual vectors teaches readers how to evaluate quality.