GENERALISATION AND DATA QUALITY
The quality of spatial data has a massive impact on its usability. It is therefore critical to both the producer of the data and its users. In this paper we discuss the close links between data quality and the generalisation process. The quality of the source data has an effect on how it can be gene...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-08-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W3/91/2015/isprsarchives-XL-3-W3-91-2015.pdf |
| Summary: | The quality of spatial data has a massive impact on its usability. It is therefore critical to both the producer of the data and its users.
In this paper we discuss the close links between data quality and the generalisation process. The quality of the source data has an
effect on how it can be generalised, and the generalisation process has an effect on the quality of the output data. Data quality
therefore needs to be kept under control. We explain how this can be done before, during and after the generalisation process, using
three of 1Spatial’s software products: 1Validate for assessing the conformance of a dataset against a set of rules, 1Integrate for
automatically fixing the data when non-conformances have been detected and 1Generalise for controlling the quality during the
generalisation process. These tools are very effective at managing data that need to conform to a set of quality rules, the main
remaining challenge is to be able to define a set of quality rules that reflects the fitness of a dataset for a particular purpose. |
|---|---|
| ISSN: | 1682-1750 2194-9034 |