Integration of Diverse Data Sources for Spatial PM2.5 Data Interpolation

作     者：Tang, Mengfan Wu, Xiao Agrawal, Pranav Pongpaichet, Siripen Jain, Ramesh 

作者机构：Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON MULTIMEDIA》 (IEEE Trans Multimedia)

年 卷 期：2017年第19卷第2期

页      面：408-417页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Program for Sichuan Provincial Science Fund for Distinguished Young Scholars [13QNJJ0149] Donald Bren Foundation 

主　　题：Diverse data data fusion and integration eventShop operators geospaital interpolation PM2.5 

摘      要：Heterogeneous data fusion from disparate geospatial sensors has drawn increasing attention in multimedia. Unfortunately, environmental sensors are usually sparsely and preferentially located, which restricts situation recognition of geographical regions and results in uncertainty in derived inferences. Spatial interpolation is an effective way to solve the problem of data sparsity, which demands the availability of related data sources. However, these data sources are usually in different resolutions, distributions, scales, and densities, which poses a major challenge in data integration. To address this problem, we present a novel spatial interpolation framework to incorporate diverse data sources and model the spatial processes explicitly at multiple resolutions. Spectral analysis is deployed to generate features at multiple spatial resolutions and to improve the interpolation accuracy at unobserved locations. A statistical operator based on the spatial Gaussian process is implemented and integrated into a geospatial situation recognition system, which can analyze heterogeneous spatio- temporal data streams derived from sensors. To verify the effectiveness and efficiency of the proposed framework, this framework is applied to the PM2.5 air pollution application. Experiments conducted in California, USA, demonstrate that the proposed method outperforms state-of-theart approaches.