Parallel vector buffer analysis approaches can be classified into 2 types:algorithm-oriented parallel strategy and the data-oriented parallel strategy.These methods do not take its applicability on the existing geogra...Parallel vector buffer analysis approaches can be classified into 2 types:algorithm-oriented parallel strategy and the data-oriented parallel strategy.These methods do not take its applicability on the existing geographic information systems(GIS)platforms into consideration.In order to address the problem,a spatial decomposition approach for accelerating buffer analysis of vector data is proposed.The relationship between the number of vertices of each feature and the buffer analysis computing time is analyzed to generate computational intensity transformation functions(CITFs).Then,computational intensity grids(CIGs)of polyline and polygon are constructed based on the relative CITFs.Using the corresponding CIGs,a spatial decomposition method for parallel buffer analysis is developed.Based on the computational intensity of the features and the sub-domains generated in the decomposition,the features are averagely assigned within the sub-domains into parallel buffer analysis tasks for load balance.Compared with typical regular domain decomposition methods,the new approach accomplishes greater balanced decomposition of computational intensity for parallel buffer analysis and achieves near-linear speedups.展开更多
为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical de...为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。展开更多
介绍ArcGIS Data Reviewer基本功能和特性,对其应用于林业地理信息矢量数据质量检查,如图斑重复、重叠,图斑间有间隙、多部件、狭长面、急锐角化、漏绘等空间关系,以及属性字段之间的逻辑性检查等的方法和步骤,举例进行了详细叙述,可为...介绍ArcGIS Data Reviewer基本功能和特性,对其应用于林业地理信息矢量数据质量检查,如图斑重复、重叠,图斑间有间隙、多部件、狭长面、急锐角化、漏绘等空间关系,以及属性字段之间的逻辑性检查等的方法和步骤,举例进行了详细叙述,可为该软件模块的使用提供参考。展开更多
Spatial vector data with high-precision and wide-coverage has exploded globally,such as land cover,social media,and other data-sets,which provides a good opportunity to enhance the national macroscopic decision-making...Spatial vector data with high-precision and wide-coverage has exploded globally,such as land cover,social media,and other data-sets,which provides a good opportunity to enhance the national macroscopic decision-making,social supervision,public services,and emergency capabilities.Simultaneously,it also brings great challenges in management technology for big spatial vector data(BSVD).In recent years,a large number of new concepts,parallel algorithms,processing tools,platforms,and applications have been proposed and developed to improve the value of BSVD from both academia and industry.To better understand BSVD and take advantage of its value effectively,this paper presents a review that surveys recent studies and research work in the data management field for BSVD.In this paper,we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management.It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures.To support them more fully,firstly,we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era.Then,we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD,including data storage and organization,spatial index,processing methods,and spatial analysis.Finally,based on the above commentary and related work,several opportunities and challenges are listed as the future research interests and directions for reference.展开更多
基金the National Natural Science Foundation of China(No.41971356,41701446)National Key Research and Development Program of China(No.2017YFB0503600,2018YFB0505500,2017YFC0602204).
文摘Parallel vector buffer analysis approaches can be classified into 2 types:algorithm-oriented parallel strategy and the data-oriented parallel strategy.These methods do not take its applicability on the existing geographic information systems(GIS)platforms into consideration.In order to address the problem,a spatial decomposition approach for accelerating buffer analysis of vector data is proposed.The relationship between the number of vertices of each feature and the buffer analysis computing time is analyzed to generate computational intensity transformation functions(CITFs).Then,computational intensity grids(CIGs)of polyline and polygon are constructed based on the relative CITFs.Using the corresponding CIGs,a spatial decomposition method for parallel buffer analysis is developed.Based on the computational intensity of the features and the sub-domains generated in the decomposition,the features are averagely assigned within the sub-domains into parallel buffer analysis tasks for load balance.Compared with typical regular domain decomposition methods,the new approach accomplishes greater balanced decomposition of computational intensity for parallel buffer analysis and achieves near-linear speedups.
文摘为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。
基金This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences[grant number XDA19020201].
文摘Spatial vector data with high-precision and wide-coverage has exploded globally,such as land cover,social media,and other data-sets,which provides a good opportunity to enhance the national macroscopic decision-making,social supervision,public services,and emergency capabilities.Simultaneously,it also brings great challenges in management technology for big spatial vector data(BSVD).In recent years,a large number of new concepts,parallel algorithms,processing tools,platforms,and applications have been proposed and developed to improve the value of BSVD from both academia and industry.To better understand BSVD and take advantage of its value effectively,this paper presents a review that surveys recent studies and research work in the data management field for BSVD.In this paper,we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management.It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures.To support them more fully,firstly,we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era.Then,we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD,including data storage and organization,spatial index,processing methods,and spatial analysis.Finally,based on the above commentary and related work,several opportunities and challenges are listed as the future research interests and directions for reference.
