Clustering and Visualizing Functionally Similar Regions in Large-Scale Spatial Networks

Abstract

We address the problem of extracting functionally similar regions in urban streets and regard such regions as spatial networks. For this purpose, based on our previous algorithm called the FCE method that extracted functional clusters for each network, we propose a new method that efficiently deals with several large-scale networks by accelerating our previous algorithm using lazy evaluation and pivot pruning techniques. Then we present our new techniques for simultaneously comparing the extracted functional clusters of several networks and an effective way of visualizing these clusters by focusing on the fact that the maximum degree of the nodes in spatial networks is restricted to relatively small numbers. In our experiments using urban streets extracted from the OpenStreetMap data of four worldwide cities, we show that our proposed method achieved a reasonably high acceleration performance. Then we show that the functional clusters extracted by it are useful for understanding the properties of areas in a series of visualization results and empirically confirm that our results are substantially different from those obtained by representative centrality measures. These region characteristics will play important roles for developing and planning city promotion and travel tours as well as understanding and improving the usage of urban streets.