期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 26, 期 6, 页码 963-982出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2011.619501
关键词
volunteered geographic information; OpenStreetMap UK; spatial data quality; machine learning
类别
资金
- Alexander von Humboldt Foundation
In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R-2) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R-2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.
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