Classification in GIS Using Support Vector Machines

Support Vector Machines (SVM) are powerful tools for classification of data. This article describes the functionality of SVM including their design and operation. SVM have been shown to provide high classification accuracies and have good generalization capabilities. SVM can classify linearly separable data as well as nonlinearly separable data through the use of the kernel function. The advantages of using SVM are discussed along with the standard types of kernel functions. Furthermore, the effectiveness of applying SVM to large, spatial datasets derived from Geographic Information Systems (GIS) is also described. Future trends and applications are also discussed – the described extracted dataset contains seven independent variables related to urban development plus a class label which denotes the urban areas versus the rural areas. This large dataset, with over a million instances really proves the generalization capabilities of the SVM methods. Also, the spatial property allows experts to analyze the error signal.