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

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

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

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

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

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

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.

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

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

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.