J. Brus, V. Pechanec, H. Kilianova
Monday 5 August 2013 by Libadmin2013

References: 13th SGEM GeoConference on Informatics, Geoinformatics And Remote Sensing, www.sgem.org, SGEM2013 Conference Proceedings, ISBN 978-954-91818-9-0 / ISSN 1314-2704, June 16-22, 2013, Vol. 1, 1017 - 1024 pp


The term of data quality should be used with caution because the concept of uncertainty and quality of spatial data are not synonymous, even though in many cases they are remarkably close to each other. They have highly similar characteristics and solve similar domains. In many cases, the quality and uncertainty are confused. Uncertainty in this sense is considered to be a characteristic that allows us to assess the quality of the data and how uncertainty affects the quality of spatial data. The basic difference is that the uncertainty can be introduced at any stage of the production of maps and GIS analyses (observation, conceptual modelling, measurement, analysis, etc.) and as such is not only related to the quality of spatial data themselves, but to the entire process. Sometimes we can see examples in the geoinformatics and geomatics when the pursuit of perfectly accurate and precision data are achieved, but perfect or near-perfect representation are the exception rather than the rule. Much common is the situation when is practically impossible to capture every single aspect due to intricate details of the world. In addition, different individuals perceive the world in different ways, and it is difficult to identify one valid and universally accepted theory. Visualisation is a strong tool to showing results by representation in a better way than text description of the quality of data. In the other hand, unsophisticated visualisation is insufficient due to the complexity of the spatial quality data problem. Based on this statement it is necessary to visualise also information about data quality and uncertainty. This paper describes different perspectives of uncertainty and spatial data quality visualisation in context of modelling the spatial distributions of ecotones.

Keywords: uncertainty, ecotones, spatial data quality

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