\[ \newcommand\si[1]{\mathrm{#1}} \newcommand\SI[2]{#1\,\si{#2}} \newcommand\matr[1]{\mathbf{#1}} \DeclareMathOperator*{\argmax}{arg\,max} \DeclareMathOperator*{\argmin}{arg\,min} \]
GIS data quality
1 Data quality
Indication of how good data are.
Need to be complete, compatible, consistent, and applicable for the analysis being performed.
Problems may include errors, accuracy, precision, bias, resolution, and generalization.
2 Accuracy vs. precision
3 Resolution and generalization of raster datasets
5 Completeness
5.1 Data completeness
Covers the entire study area and period.
Attributes exist for each feature.
Only one set of attributes are assigned to each feature.
5.2 Quality completeness
Compatibility: Spatial scale and measurement scale (e.g., ratio vs. ordinal)
Consistency: Data sources, calibration, boundary changes, different data encoders
Applicability: Using appropriate data layers for functions
6 Sources of error
Conceptual errors: The way in which we perceive, study, and model reality
Errors in source data: Human error, equipment problems, conversion errors
Data encoding errors: Methods and conditions under which it is carried out are important
Data editing and conversion errors
Data processing and analysis errors
Data output errors
7 Checking for errors
Visual inspection
Double digitizing
Examination of error signatures (e.g., feature ID, vertex ID, field name)
Statistical analysis: Checking outliers and extreme values