Data and the context in which data is interpreted are subject to imperfection, and the interpretation of the data accordingly depends on the choice of the context. Data quality and fitness for purpose can thus not be assessed without any choice of a context, a fact that can be regarded as an inevitable calibration of the data quality assessment. The paper examines this effect, why Volunteered Geographic Information is particularly prone to such influences of the choice of a context, and how this influence on the data quality assessment can possibly be reduced. Different types of contexts are discussed at the example of OpenStreetMap data.
|Number of pages||5|
|Publication status||Published - 9 May 2017|
|Event||20th AGILE Conference on Geographic Information Science, AGILE 2017 - Wageningen, Netherlands|
Duration: 9 May 2017 → 12 May 2017
Conference number: 20
|Conference||20th AGILE Conference on Geographic Information Science, AGILE 2017|
|Period||9/05/17 → 12/05/17|