Functional cluster extraction from large spatial networks

We address a problem of extracting functionally similar regions in urban streets regarded as spatial networks. Such characteristics of regions will play important roles for developing and planning city promotion, travel tours and so on, as well as understanding and improving the usage of urban streets. In order to analyze such functionally similar regions, we propose an acceleration method of the FCE (functional cluster extraction) algorithm equipped with the lazy evaluation and pivot pruning techniques, which enables to efficiently deal with several large-scale networks. In our experiments using urban streets of six cities, we show that our proposed method achieved a reasonably high acceleration performance. Then, we show that functional cluster produced by our method are useful for understanding the properties of areas in a series of visualization results.