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Selection of streets from a network using self-organizing maps
University of Gävle, Department of Technology and Built Environment, Ämnesavdelningen för samhällsbyggnad.ORCID iD: 0000-0002-2337-2486
2004 (English)In: Transactions on GIS, ISSN 1361-1682, E-ISSN 1467-9671, Vol. 8, no 3, 335-350 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2004. Vol. 8, no 3, 335-350 p.
Identifiers
URN: urn:nbn:se:hig:diva-2637DOI: 10.1111/j.1467-9671.2004.00186.xOAI: oai:DiVA.org:hig-2637DiVA: diva2:119299
Available from: 2007-10-03 Created: 2007-10-03 Last updated: 2015-12-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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