Complex networks from time series data allow an efficient historical stage division of urban air quality information

作     者：Qiao, Honghai Deng, Zhenghong Li, Huijia Hu, Jun Song, Qun Xia, Chengyi 

作者机构：Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350000 Peoples R China Northwestern Polytech Univ Yangtze River Delta Res Inst NPU Taicang Taicang 215400 Peoples R China Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300384 Peoples R China 

出 版 物：《APPLIED MATHEMATICS AND COMPUTATION》 (应用数学和计算)

年 卷 期：2021年第410卷

页      面：126435-126435页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：National Natural Science Foundation of China [61773286, 71871233] Beijing Natural Science Foundation 

主　　题：Time series complex networks Community detection algorithm Urban air quality indexes Visibility graph Environmental science Historical stage division 

摘      要：Urban air quality is related to human health in modern life. The statistical features of urban air quality highly depend on the division of historical stages. Conventional division methods that use a fixed period (e.g., month) can result in confusion during statistical analysis. In this study, we propose a novel analysis technique based on time series complex network theories to divide the historical information of urban air quality by using flexible periods. First, air quality information is converted into time series complex networks via a multilayer visibility model. Thereafter, an improved community detection algorithm is proposed on the basis of network characteristics. In particular, the centrality of nodes is increased using a kernel density estimation model. An improved bidirectional search pattern results in the optimal modularity. Finally, the historical curves of urban air quality are divided into several stages in accordance with the optimal clustering results. The simulation experiments demonstrate important conclusions. The clustering accuracy of the proposed algorithm is superior to those of other evaluated methods on actual air quality networks. The number of historical stages is decreased constantly in accordance with clustering results, and this condition is beneficial for statistics. Our results can reasonably explain the relationship between valid time and air quality features. The proposed technique can provide effective and reliable division results of historical stages. (c) 2021 Elsevier Inc. All rights reserved.