Abstract
Changes are immanent to digital geographic vector datasets. While the majority of changes nowadays are quantitatively detectable by the use of geographic information systems their classification and impact assessment on a qualitative level with respect to specific data usage scenarios is often neglected. To close this gap, this work proposes a classification approach consisting of three parts: (1) a taxonomy for classifying quantitatively detectable edits in digital feature datasets (e.g. attribute or geometry changes), (2) a taxonomy for classifying edits into qualitative and therefore meaningful change types (e.g. feature revision or identity change) and (3) a mapping scheme providing the link from quantitative to qualitative classifications. In the context of a case study with OpenStreetMap history data the proposed classification approach is used to automatically detect and classify feature changes with respect to two feature types, namely streets and buildings, leading to a refined mapping scheme for two selected data usage scenarios, namely vehicle routing and map rendering. Results show the applicability of the approach, especially for assessing the impact of feature changes on different data usage scenarios, and provide a useful foundation for any change detection task in the context of geographic vector datasets.
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This work was partly funded by the Austrian Federal Ministry for Transport, Innovation and Technology.
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Rehrl, K., Brunauer, R., Gröchenig, S. (2015). Towards a Qualitative Assessment of Changes in Geographic Vector Datasets. In: Bacao, F., Santos, M., Painho, M. (eds) AGILE 2015. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-16787-9_11
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DOI: https://doi.org/10.1007/978-3-319-16787-9_11
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