Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 91-94, 2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
19 Aug 2015
N. Regnauld 1Spatial, Tennyson House, Cambridge Business Park, Cambridge CB4 0WZ, UK
Keywords: Data Quality, Quality Criteria, Automatic Generalisation, Validation, Fitness for Purpose Abstract. 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.
Conference paper (PDF, 662 KB)

Citation: Regnauld, N.: GENERALISATION AND DATA QUALITY, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 91-94, doi:10.5194/isprsarchives-XL-3-W3-91-2015, 2015.

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