In the era of Earth Observation(EO)big data,interactive spatiotemporal aggregation analysis is a critical tool for exploring geographic patterns.However,existing methods are inefficient and complex.Their interactive p...In the era of Earth Observation(EO)big data,interactive spatiotemporal aggregation analysis is a critical tool for exploring geographic patterns.However,existing methods are inefficient and complex.Their interactive performance greatly depends on large-scale computing resources,especially data cube infrastructure.In this study,from a green computing perspective,we propose a lightweight data cube model based on the preaggregation concept,in which the frequency histogram of EO data is employed as a specific measure.The cube space was divided into lattice pyramids by the Google S2 grid system,and histogram statistics of the EO data were injected into in-memory cuboids.Therefore,exploratory aggregation analysis of EO datasets could be rapidly converted into multidimensional-view query processes.We implemented the prototype system on a local PC and conducted a case study of global vegetation index aggregation.The experiments showed that the proposed model is smaller,faster and consumes less energy than ArcGIS Pro and XCube,and facilitates green computing strategies involving a cube infrastructure.Due to the standalone mode,larger dataset will result in longer cube building time with indexing latency.The efficiency of the approach comes at the expense of accuracy,and the inherent uncertainties were examined in this paper.展开更多
Objective To clarify the epidemiological characteristics and spatial distribution patterns of human norovirus outbreaks in China, identify high-risk areas, and provide guidance for epidemic prevention and control.Meth...Objective To clarify the epidemiological characteristics and spatial distribution patterns of human norovirus outbreaks in China, identify high-risk areas, and provide guidance for epidemic prevention and control.Methods This study analyzed 964 human norovirus outbreaks involving 50,548 cases in 26 provinces reported from 2012 to 2018. Epidemiological analysis and spatiotemporal scanning analysis were conducted to analyze the distribution of norovirus outbreaks in China.Results The outbreaks showed typical seasonality, with more outbreaks in winter and fewer in summer, and the total number of infected cases increased over time. Schools, especially middle schools and primary schools, are the most common settings of norovirus outbreaks, with the major transmission route being life contact. More outbreaks occurred in southeast coastal areas in China and showed significant spatial aggregation. The highly clustered areas of norovirus outbreaks have expanded northeast over time.Conclusion By identifying the epidemiological characteristics and high-risk areas of norovirus outbreaks, this study provides important scientific support for the development of preventive and control measures for norovirus outbreaks, which is conducive to the administrative management of high-risk settings and reduction of disease burden in susceptible areas.展开更多
基金supported by Key Laboratory of National Geographic Census and Monitoring,Ministry of Natural Resources,China:[Grant Number 2020NGCM05]Natural Science Foundation of Shaanxi Province,China:[Grant Number 2020JQ-413].
文摘In the era of Earth Observation(EO)big data,interactive spatiotemporal aggregation analysis is a critical tool for exploring geographic patterns.However,existing methods are inefficient and complex.Their interactive performance greatly depends on large-scale computing resources,especially data cube infrastructure.In this study,from a green computing perspective,we propose a lightweight data cube model based on the preaggregation concept,in which the frequency histogram of EO data is employed as a specific measure.The cube space was divided into lattice pyramids by the Google S2 grid system,and histogram statistics of the EO data were injected into in-memory cuboids.Therefore,exploratory aggregation analysis of EO datasets could be rapidly converted into multidimensional-view query processes.We implemented the prototype system on a local PC and conducted a case study of global vegetation index aggregation.The experiments showed that the proposed model is smaller,faster and consumes less energy than ArcGIS Pro and XCube,and facilitates green computing strategies involving a cube infrastructure.Due to the standalone mode,larger dataset will result in longer cube building time with indexing latency.The efficiency of the approach comes at the expense of accuracy,and the inherent uncertainties were examined in this paper.
基金supported by the National Natural Science Foundation of China[grant number 81903377]Young Scholar Foundation of NIEH[grant number 19qnjj]。
文摘Objective To clarify the epidemiological characteristics and spatial distribution patterns of human norovirus outbreaks in China, identify high-risk areas, and provide guidance for epidemic prevention and control.Methods This study analyzed 964 human norovirus outbreaks involving 50,548 cases in 26 provinces reported from 2012 to 2018. Epidemiological analysis and spatiotemporal scanning analysis were conducted to analyze the distribution of norovirus outbreaks in China.Results The outbreaks showed typical seasonality, with more outbreaks in winter and fewer in summer, and the total number of infected cases increased over time. Schools, especially middle schools and primary schools, are the most common settings of norovirus outbreaks, with the major transmission route being life contact. More outbreaks occurred in southeast coastal areas in China and showed significant spatial aggregation. The highly clustered areas of norovirus outbreaks have expanded northeast over time.Conclusion By identifying the epidemiological characteristics and high-risk areas of norovirus outbreaks, this study provides important scientific support for the development of preventive and control measures for norovirus outbreaks, which is conducive to the administrative management of high-risk settings and reduction of disease burden in susceptible areas.
