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Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.

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Data quality assessment in volunteered geographic decision support De Tré, G.; De Mol, R.; Van Heteren, S.; Stafleu, J.; Chademenos, V.; Missiaen, T.; Kint, L.; Terseleer, N.; Van Lancker, V. (2018). Data quality assessment in volunteered geographic decision support, in: Bordogna, G. et al. Mobile information systems leveraging volunteered geographic information for Earth observation. Earth Systems Data and Models, : pp. 173-192. https://dx.doi.org/10.1007/978-3-319-70878-2_9 In: Bordogna, G.; Carrara, P. (Ed.) (2018). Mobile information systems leveraging volunteered geographic information for Earth observation. Earth Systems Data and Models. Springer: Cham. ISBN 978-3-319-70877-5. https://dx.doi.org/10.1007/978-3-319-70878-2, meer In: Earth Systems Data and Models. Springer: Berlin; Heidelberg. ISSN 2364-5830, meer

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VLIZ: Non-open access 310667 [ aanvragen ]

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Auteurs		Top
De Tré, G., meer De Mol, R., meer Van Heteren, S.	Stafleu, J. Chademenos, V., meer Missiaen, T., meer	Kint, L., meer Terseleer, N., meer Van Lancker, V., meer

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Abstract

Geographic decision support systems aim to integrate and process data originating from different sources and different data providers in order to create suitability models. A suitability model denotes how suitable geographic locations are for a specific purpose on which decision-makers need to make a decision. Particularly in the presence of volunteered information, data quality assessment becomes an important aspect of a decision-making process. Geographic data are commonly prone to incompleteness, imprecision and uncertainty, and this is even more the case with volunteered data. To correctly inform the users, it is essential to communicate not only the suitability degrees highlighted in a suitability model, but also the confidence about these suitability degrees as can be derived from data quality assessment. In this chapter, a novel hierarchical approach for data quality assessment, supporting the computation of associated confidence degrees, is introduced. To illustrate its added value, aspects of the project Transnational and Integrated Long-term marine Exploitation Strategies (TILES) are used. Providing confidence information adds an extra dimension to the decision-making process and leads to more sound decisions.

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