Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks

被引:108
作者
Hagenauer, Julian [1 ]
Helbich, Marco [1 ]
机构
[1] Heidelberg Univ, Inst Geog, D-69120 Heidelberg, Germany
关键词
volunteered geographic information; OpenStreetMap UK; spatial data quality; machine learning; OPENSTREETMAP; SELECTION; GIS;
D O I
10.1080/13658816.2011.619501
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:963 / 982
页数:20
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