所属 コンピュータサイエンス学部 コンピュータサイエンス学科 職種 専任講師
|標題||Clustering and Visualizing Functionally Similar Regions in Large-Scale Spatial Networks|
|掲載誌名||Journal of Information Processing, Special Issue of Network Science|
|著者・共著者||Takayasu Fushimi，Kazumi Saito，Tetsuo Ikeda，Kazuhiro Kazama|
|概要||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.