Visual Exploration of Classifiers for Hybrid Textual and Geospatial Matching

The availability of large geospatial data from different sources has dramatically increased, but for the usage of such data in geo-mashup or contextaware systems, a data fusion component is necessary. To solve the integration issue classifiers are obtained by supervised training, with feature vectors derived from textual and geospatial attributes. In an application example, a coherent part of Germany was annotated by humans and used for supervised learning. Annotation by humans is not free of errors, which decreases the performance of the classifier. We show how visual analytics techniques can be used to efficiently detect such false annotations. Especially the textual features introduce high-dimensional feature vectors, where visual analytics becomes important and helps to understand and improve the trained classifiers. Particular technical components used in our systems are scatterplots, multiple coordinated views, and interactive data drill-down.