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Selection of Streets from a Network Using Self-Organizing Maps

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We propose a novel approach to selection of important streets from a network, based on the technique of a self-organizing map (SOM), an artificial neural network algorithm for data clustering and visualization. Using the SOM training process, the approach derives a set of neurons by considering multiple attributes including topological, geometric and semantic properties of streets. The set of neurons constitutes a SOM, with which each neuron corresponds to a set of streets with similar properties. Our approach creates an exploratory linkage between the SOM and a street network, thus providing a visual tool to cluster streets interactively. The approach is validated with a case study applied to the street network in Munich, Germany.
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Document Type: Research Article

Affiliations: 1: Division of Geomatics Department of Technology and Built Environment University of Gävle 2: GIS Centre Lund University

Publication date: June 1, 2004

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