共查询到19条相似文献，搜索用时 625 毫秒
1.
作为GIS的核心功能之一,空间分析逐步向处理数据海量化及分析过程复杂化方向发展,以往的串行算法渐渐不能满足人们对空间分析在计算效率、性能等方面的需求,并行空间分析算法作为解决目前问题的有效途径受到越来越多的关注。该文在简要介绍空间分析方法和并行计算技术的基础上,着重从矢量算法与栅格算法两方面阐述了目前并行空间分析算法的研究进展,评述了在空间数据自身特殊性的影响下并行空间分析算法的发展方向及存在的问题,探讨了在计算机软硬件技术高速发展的新背景下并行空间分析算法设计面临的机遇与挑战。 相似文献
2.
面向集聚分布空间数据的混合式索引方法研究 总被引：2，自引：0，他引：2
空间数据索引技术可以有效地提高空间数据在存储、处理、分析以及地图可视化中的效率,其性能优劣直接影响GIS的整体性能。该文针对格网索引和四叉树索引存在的问题,提出将四叉树嵌入格网形成一种混合式空间索引结构,并分析其原理、数据结构与影响参数。理论分析及实验证明,对于空间集聚分布状态的海量地理数据而言,混合式索引方法以略高的存储代价换取了更高的检索、插入和删除效率,是一种有效的空间索引方案。 相似文献
3.
地图叠加分析是一种计算密集型算法,并行化计算是加快算法执行速度的一种有效方法。该文研究分布式环境下的点面图层并行化叠加分析方法与实现。首先根据点面叠加的特点设置并行数据分解的方式,基于分治法分解空间数据,在并行系统下将地理要素分而治之。然后引入双层索引的并行叠加机制,一是对面图层根据Hilbert空间索引的排序方式分发数据,二是对点图层建立四叉树索引,对每一个进行相交运算的多边形进行快速过滤和求交。最后在Linux集群系统下实现该并行算法,其一利用MPI分布式计算环境实现在整体计算框架下的消息通讯模式的并行,其二在每个子节点中实现基于多核OpenMP工具的本地并行化。结果表明,利用双层空间索引分治的方法可实现并行数据分块,各子节点实现独立计算,减少并行系统中的I/O冲突,并行加速比明显。该方法对矢量地图运算的并行化进行了有益的尝试,为大数据时代的空间数据分析提供一种有效的途径。 相似文献
4.
针对当前空间填充曲线(Space-Filling Curve,SFC)类NoSQL空间索引对复杂几何索引支持较差、字典序映射成本较高等问题,该文提出一种基于NoSQL的分布式R树空间索引.基于NoSQL的分区存储模式,通过优化后的STR(Sort Tile Recursive)均衡策略配置分布式R树,借助R树路径实现索引、数据的编码存储,并提出批处理模式的索引并行构建方法;借助NoSQL的SSPT(Server-Side Scripts)计算框架构建查询、应用并行处理机制.选用土地利用、规划数据进行对比实验,结果表明:该索引的平均构建耗时为GeoMesa的30.0％,500万量级下耗时仅为GeoMesa的18.6％;执行MBR查询、多边形几何查询、最邻近查询的平均耗时分别为GeoMesa的26.5％ 、53.4％ 、52.3％;执行自然资源开发项目合规性审查应用的平均耗时分别为ArcGIS、GeoMesa的10.6％ 、72.7％.该索引在构建性能、空间查询性能以及应用性能方面均具有优越性,能为基于NoSQL的海量空间数据高性能存储、检索与应用提供一种优良解决方案. 相似文献
5.
空间索引技术可提供高效的空间数据组织与管理方式,以支撑海量空间数据的挖掘与分析。针对当前空间索引存在的知识体系不明晰、选择难等问题,该文通过文献调查法和CiteSpace工具,依据空间划分及映射方法将空间索引划分为基于树结构、格网、空间填充曲线和地址编码的空间索引四大类,并综述其原理、空间结构、适用范围及在GIS领域的应用,最后对空间索引在数据组织、高效计算、可视化、可靠性等方面的研究进行展望。结论如下：基于树结构的空间索引最具普适性且可以处理多维度及多层次的数据,查询性能依赖于树结构的平衡性及数据的分布;基于格网的空间索引可以均匀划分空间以便于高效范围查询,却不适用于非结构化或动态数据集;基于空间填充曲线的空间索引可以在实现维度压缩的同时保持局部邻近性,但插入或删除数据可能导致整个曲线的重构难以频繁更新;基于地址编码的空间索引将语义地址信息转化为编码信息,便于高效检索,然而语义地址匹配仍存在较大误差和不确定性。研究结果可为空间数据组织和结构设计提供参考。 相似文献
6.
1引言与传统的数据库管理系统相比,空间数据库涉及对现实世界大量空间目标的处理,空间目标具有不规则的几何形状、目标间的空间关系复杂,空间数据具有不可排序性、相关性和数据复杂性等特点.针对一维属性数据的主关键字索引而设计的传统数据库索引技术,不能直接应用于空间数据库的索引.空间索引技术一直是数据库技术及相关研究领域的热点问题,本文针对空间数据的特点,讨论了目前主要的空间索引方法,并对空间索引技术在商用数据库中的应用作了介绍。2空间数据及其特点空间数据是指带有空间坐标信息的数据,它不仅能表示实体本身的空间位置及形态,而且还包含实体属性和空间关系的信息。空间数据具有以下特点： 相似文献
7.
基于HBase的矢量空间数据分布式存储研究 总被引：1，自引：0，他引：1
分析了分布式数据库HBase的存储模型;结合对HBase集群技术的研究,设计了基于HBase的矢量空间数据存储模型和一种基于MapReduce的并行构建网格空间索引方法,使得海量空间矢量数据的网格索引构建分配到各子节点进行,大大加快索引构建的处理速度;最后,利用HBase集群环境对所提出的方法进行验证,该方法具有较好的可行性和较高的效率. 相似文献
8.
多级地理空间网格框架及其关键技术初探 总被引：1，自引：0，他引：1
为了有效管理、组织和利用海量空间数据,解决存储架构与现有空间数据结构不一致的矛盾,在融合国内外各种球面剖分模型优点基础上,设计了一种多级地理空间网格框架。该网格框架以地图分幅划分方式为基础,利用经纬度间隔对全球进行层次性剖分,形成遥感数据、测绘数据及其他空间数据的统一组织框架。通过对网格单元的地址与属性编码,实现空间数据的直接存储和索引,从而完成对空间信息的无缝拼接与多尺度管理。最后阐述了实现地理空间网格框架的关键技术,包括空时一体化技术、计算集群存储技术和空间索引技术等。 相似文献
9.
P2P环境中的全局空间数据目录研究 总被引：10，自引：1，他引：9
P2P计算通过大量自治的节点协作共享资源与计算,为空间数据和空间操作的分布提供了新的分布式计算模式。分布在不同Peer上的空间数据库节点通过P2P协作构成一个超级全局空间数据库,全局空间数据目录是P2P环境下快速定位空间数据资源和空间计算节点的关键技术。Peer数据库节点的数据模式、元数据、资源状态参数等抽象为一系列关键词集合。全局目录基于Peer空间数据库节点的外包矩形进行动态聚类并建立P2P环境下的空间索引,支持Peer空间数据库节点的动态加入和退出,支持复杂空间查询和关键词查询。该文给出了全局空间数据目录的组织模型、P2P空间数据索引及空间资源发现算法。 相似文献
10.
《地理与地理信息科学》2017,(2)
针对当前分布式多尺度空间数据服务检索效率较低,无法满足集成应用需求的问题,该文提出一种基于两级空间索引结构的分布式多尺度空间数据索引方法,即以空间网格索引为基础,结合空间数据服务元数据管理,实现了空间数据服务及其空间对象的索引。在检索过程中,先通过一级索引检索出目标空间数据服务,再使用二级索引在目标服务内检索出目标数据对象。对二级索引结构和基于服务注册的检索效率的对比测试表明,该方法的效率优于服务注册方法,且随服务数量的增加,效率优势更显著;对单个服务的数据量对二级索引性能的影响进行测试,发现单个数据服务随着数据对象的增加,二级索引的检索时间逐渐增加,但总体检索时间基本可以满足多数应用需求。 相似文献
11.
3D building roof reconstruction from airborne LiDAR point clouds: a framework based on a spatial database 总被引：1，自引：0，他引：1
Rujun Cao Xinyi Liu Zongze Zhao 《International journal of geographical information science》2017,31(7):1359-1380
Three-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance. 相似文献
12.
Yanli Zhao Anand Padmanabhan 《International journal of geographical information science》2013,27(2):363-384
Viewshed analysis, often supported by geographic information system, is widely used in many application domains. However, as terrain data continue to become increasingly large and available at high resolutions, data-intensive viewshed analysis poses significant computational challenges. General-purpose computation on graphics processing units (GPUs) provides a promising means to address such challenges. This article describes a parallel computing approach to data-intensive viewshed analysis of large terrain data using GPUs. Our approach exploits the high-bandwidth memory of GPUs and the parallelism of massive spatial data to enable memory-intensive and computation-intensive tasks while central processing units are used to achieve efficient input/output (I/O) management. Furthermore, a two-level spatial domain decomposition strategy has been developed to mitigate a performance bottleneck caused by data transfer in the memory hierarchy of GPU-based architecture. Computational experiments were designed to evaluate computational performance of the approach. The experiments demonstrate significant performance improvement over a well-known sequential computing method, and an enhanced ability of analyzing sizable datasets that the sequential computing method cannot handle. 相似文献
13.
Wenwu Tang Wenpeng Feng Meijuan Jia 《International journal of geographical information science》2013,27(3):412-439
This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments. 相似文献
14.
分布式水文模型的并行计算研究进展 总被引：3，自引：1，他引：2
大流域、高分辨率、多过程耦合的分布式水文模拟计算量巨大,传统串行计算技术不能满足其对计算能力的需求,因此需要借助于并行计算的支持。本文首先从空间、时间和子过程三个角度对分布式水文模型的可并行性进行了分析,指出空间分解的方式是分布式水文模型并行计算的首选方式,并从空间分解的角度对水文子过程计算方法和分布式水文模型进行了分类。然后对分布式水文模型的并行计算研究现状进行了总结。其中,在空间分解方式的并行计算方面,现有研究大多以子流域作为并行计算的基本调度单元;在时间角度的并行计算方面,有学者对时空域双重离散的并行计算方法进行了初步研究。最后,从并行算法设计、流域系统综合模拟的并行计算框架和支持并行计算的高性能数据读写方法3个方面讨论了当前存在的关键问题和未来的发展方向。 相似文献
15.
A high-performance cellular automata model for urban simulation based on vectorization and parallel computing technology 总被引：1，自引：0，他引：1
Chang Xia Anqi Zhang Wenting Zhang 《International journal of geographical information science》2018,32(2):399-424
Cellular automata (CA) models can simulate complex urban systems through simple rules and have become important tools for studying the spatio-temporal evolution of urban land use. However, the multiple and large-volume data layers, massive geospatial processing and complicated algorithms for automatic calibration in the urban CA models require a high level of computational capability. Unfortunately, the limited performance of sequential computation on a single computing unit (i.e. a central processing unit (CPU) or a graphics processing unit (GPU)) and the high cost of parallel design and programming make it difficult to establish a high-performance urban CA model. As a result of its powerful computational ability and scalability, the vectorization paradigm is becoming increasingly important and has received wide attention with regard to this kind of computational problem. This paper presents a high-performance CA model using vectorization and parallel computing technology for the computation-intensive and data-intensive geospatial processing in urban simulation. To transfer the original algorithm to a vectorized algorithm, we define the neighborhood set of the cell space and improve the operation paradigm of neighborhood computation, transition probability calculation, and cell state transition. The experiments undertaken in this study demonstrate that the vectorized algorithm can greatly reduce the computation time, especially in the environment of a vector programming language, and it is possible to parallelize the algorithm as the data volume increases. The execution time for the simulation of 5-m resolution and 3 × 3 neighborhood decreased from 38,220.43 s to 803.36 s with the vectorized algorithm and was further shortened to 476.54 s by dividing the domain into four computing units. The experiments also indicated that the computational efficiency of the vectorized algorithm is closely related to the neighborhood size and configuration, as well as the shape of the research domain. We can conclude that the combination of vectorization and parallel computing technology can provide scalable solutions to significantly improve the applicability of urban CA. 相似文献
16.
3D GIS空间索引技术研究 总被引：13，自引：0，他引：13
概括并分析3D GIS中使用的空间索引技术,介绍各类技术方法的基本思想;对典型的空间索引方法进行分类,综合比较其优缺点和适用对象;按照空间分割方式将三维空间索引分为规则分割和对象分割两大类,规则分割包括规则网格、BSP树、八叉树、KD树、KDB树和R树系列等,对象分割则通过层次包围体来实现。指出在3D GIS实际应用中,应根据实际情况和应用需要组合多种索引技术,进而生成灵活、高效的索引机制。 相似文献
17.
Guiming Zhang Qunying Huang 《International journal of geographical information science》2017,31(10):2068-2097
Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data. 相似文献
18.
提出了一种新的动态空间索引结构X-Lists,设计实现了X-Lists的动态插入、动态删除、查找等算法,并进行了算法实验。X-Lists是一种支持高维点查询和区域查询的广义表,实验表明,X-Lists在索引构建与区域查找方面性能明显优于现有R-Tree及其改进索引结构。 相似文献
19.
Michael J. Widener Neal C. Crago Jared Aldstadt 《International journal of geographical information science》2013,27(9):1707-1723
As geospatial researchers' access to high-performance computing clusters continues to increase alongside the availability of high-resolution spatial data, it is imperative that techniques are devised to exploit these clusters' ability to quickly process and analyze large amounts of information. This research concentrates on the parallel computation of A Multidirectional Optimal Ecotope-Based Algorithm (AMOEBA). AMOEBA is used to derive spatial weight matrices for spatial autoregressive models and as a method for identifying irregularly shaped spatial clusters. While improvements have been made to the original ‘exhaustive’ algorithm, the resulting ‘constructive’ algorithm can still take a significant amount of time to complete with large datasets. This article outlines a parallel implementation of AMOEBA (the P-AMOEBA) written in Java utilizing the message passing library MPJ Express. In order to account for differing types of spatial grid data, two decomposition methods are developed and tested. The benefits of using the new parallel algorithm are demonstrated on an example dataset. Results show that different decompositions of spatial data affect the computational load balance across multiple processors and that the parallel version of AMOEBA achieves substantially faster runtimes than those reported in related publications. 相似文献