Name
Abstract | ||
|---|---|---|
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 s treet 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. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1111/j.1467-9671.2004.00186.x | T. GIS |
Keywords | DocType | Volume |
self-organizing map,cartographic generalization,model generalization,street networks | Journal | 8 |
Issue | Citations | PageRank |
3 | 14 | 1.50 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Jiang | 1 | 127 | 237.75 |
| Lars Harrie | 2 | 68 | 13.37 |