Modeling spatial variation in data quality using linear referencing

MGSM Zaffar Sadiq 1, Matt Duckham 2 and Gary J Hunter 2
1 Cooperative Research Centre for Spatial Information, Department of Geomatics, 
University of Melbourne, 723, Swanston Street, Victoria - 3010, Australia.
Tel.: + 061 3 83443177; Fax: + 061 3 93495185 
s.mohamedghouse@pgrad.unimelb.edu.au
2 Department of Geomatics, 
University of Melbourne, Victoria - 3010, Australia.
Tel.: + 061 3 83446935; Fax: + 061 3 93472916
mduckham@unimelb.edu.au, garyh@unimelb.edu.au

Abstract
Spatial data quality (SDQ) is conventionally presented in the form of a report. The data quality statements in the report refer to the entire data set. In reality the quality of data varies spatially due to data collection methods, data  capturing techniques,  and analysis. Thus, the quality of spatial data for one area may not  be applicable to spatial data describing other regions. The present systems for reporting and representing SDQ (data quality statements) cannot address the data user’s requirements as they are not location specific. Consequently, conventional approaches to SDQ that ignore variation in quality within a data set impair the data producer’s ability to correctly communicate knowledge about data quality and jeopardize the user’s ability to assess fitness for use. To enable proper communication of SDQ, spatially varying data quality needs to be represented in the database. This paper discusses the representation of spatial variation of data quality in spatial databases using three models: per- feature, feature-independent, and feature-hybrid. In the per-feature model, quality information is stored against each spatial feature (object) stored in the database. In the feature- independent model, quality information is stored independently of particular features (as a field). The feature-hybrid model is derived  from a combination of per-feature and feature independent models. One example of an existing data management technique that can be adapted for use as a feature-hybrid model is linear referencing. Applying linear referencing in this way is a new approach to representing spatial variation in quality. The paper concludes with a review of the relative merits of the different strategies for storing spatially varying data quality information.

Keywords: spatial data quality, uncertainty, metadata, sub-feature variation, linear referencing

In: Caetano, M. and Painho, M. (eds). Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 5 – 7 July 2006, Lisboa, Instituto Geográfico Português

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