Download Terminology - GIS and Mapping - Lecture Slides and more Slides Geochemistry in PDF only on Docsity! 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 Docsity.com Terminology • Data quality and errors • Accuracy and precision • Bias • Resolution and generalisation • Currency and completeness • Compatibility and consistency • Applicability Docsity.com 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 Docsity.com 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 Docsity.com 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. Docsity.com 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. Docsity.com
Data exchange format
Data summary
Lineage
Co-ordinate system
Spatial data model
Feature coding system
Classification completeness
Geographical coverage
Positional accuracy
Attribute accuracy
“val accuracy
i representation
Data storage format.
Data sources, areal coverage, classification used, date collected,
scale, etc.
Agency of origin, method of data collection, primary survey
techniques, digitising method. Dates updated. Processing history:
co-ordinate transformations, data model translations, attribute
transformations.
Type of co-ordinate system. Map projection parameters.
Specification of primitive spatial objects. Topological data stored.
Definition of feature codes and classification system.
Documentation on the extent of usage of classification system.
Overall extent. Detailed specification of coverage if not complete.
Statistics on co-ordinate errors.
Statistics on attribute errors.
Methods of topology validation employed.
Graphical symbolism for each feature class. Text fonts for
annotation.
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