Introduction

1. Introduction • There is a tendancy to assume all data in a GIS, both locational and attribute, is accurate. • This is never the case. • Today we will look at: • Terminology to describe data quality; • Sources of error in GIS ; and • How errors can be modelled

2. Terminology • Data quality and errors • Accuracy and precision • Bias • Resolution and generalisation • Currency and completeness • Compatibility and consistency • Applicability

3. Sources Of Error • Types of error: • Locational • Attribute • Temporal • We will not consider temperal errors further. • Stages at which errors may be introduced: • Data input • Data processing • Data display

4. Data Input Errors • Data acquisition errors: • Primary: data capture or measurement • Secondary: data entry • Digitising checklist (ESRI): • No entities missing • No surplus entities • Entities are in the correct place, size etc. • Entities are correctly connected • Polygons each have one label point • All entities are within registration marks

5. Data Input Errors(2) • Digitising Issues • Sliver lines • Dangling nodes (undershoot and overshoot) • Weird polygons / polygonal knots • Snapping tolerance • Spatial and attribute pseudo nodes • Attribute Data Errors • Primary • Secondary

6. Processing And Display Errors • Some processing errors: • Conversions between raster and vector • Interpolation of field data • Rounding errors • Use errors • Data display errors: • May involve vector to raster conversions

7. Modelling Data Errors • Apart from trying to eliminate errors, good practice should entail some attempt to model the errors. • Attribute data can be modelled using conventional statistical methods – e.g. standard errors • If interpolating surfaces from sample points, methods such as kriging permit an estimate of the variance to be made for interpolated points. • Categorical data errors can be quantified using a misclassification matrix.

8. Positional Error Models • Point data: can be modelled using circular standard error (CSE)

9. Positional Error Models(2) • Line data: epsilon bands • Polygon data:

10. Metadata • Given that errors can never be completely eliminated, good practice entails providing metadata (data about data). • Various standards have arisen (e.g. INSPIRE). • The following table provides an indication of the sort of thing that should be included.