Geospatial data quality indicators

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Abstract

Indicators which summarise the characteristics of spatiotemporal data coverages significantly simplify quality evaluation, decision making and justification processes by providing a number of quality cues that are easy to manage and avoiding information overflow. Criteria which are commonly prioritised in evaluating spatial data quality and assessing a dataset’s fitness for use include lineage, completeness, logical consistency, positional accuracy, temporal and attribute accuracy. However, user requirements may go far beyond these broadlyaccepted spatial quality metrics, to incorporate specific and complex factors which are less easily measured. This paper discusses the results of a study of high level user requirements in geospatial data selection and data quality evaluation. It reports on the geospatial data quality indicators which were identified as user priorities, and which can potentially be standardised to enable intercomparison of datasets against user requirements. We briefly describe the implications for tools and standards to support the communication and intercomparison of data quality, and the ways in which these can contribute to the generation of a GEO label.

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  • Geospatial data quality indicators

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Publication date2012
Publication titleProceedings of the 10th international symposium on spatial accuracy assessment in natural resources and environmental sciences
EditorsCarlos Vieira, Vania Bogorny, Artur R. Aquino
Pages121-126
Number of pages6
Original languageEnglish
Event10th international symposium on spatial accuracy assessment in natural resources and environmental sciences - Florianópolis, SC, Brazil
Duration: 10 Jul 201213 Jul 2012

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Other10th international symposium on spatial accuracy assessment in natural resources and environmental sciences
CountryBrazil
CityFlorianópolis, SC
Period10/07/1213/07/12

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The Users can download and copy the information, documents and other data for User's personal, non-commercial use, without any right to resell, redistribute or create derivative works.

    Keywords

  • geospatial data, quality evaluation, geospatial data quality indicators, geospatial data quality

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