共查询到19条相似文献，搜索用时 156 毫秒
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《地理与地理信息科学》2016,(1)
网络最小费用流算法常用来解决资源流最优分配问题,传统的串行算法因时间复杂度高而不能满足大规模网络对计算效率的要求。该文用时间复杂度低的网络单纯形算法(NSA)的并行化求解大规模网络的最小费用流问题。通过分析NSA的可并行性,使用MPI分布式并行技术,设计了NSA并行算法;分析了3种常用流网络的拓扑结构特征及其与地理网络的关系;在并行环境下对计算效率进行实验测试,结果表明该算法具有显著的加速效果,峰值可达5.4。NSA并行算法应用面宽,可为区域及全国性大规模网络流资源分配方案的快速制定与政务决策提供有力支持。 相似文献
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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
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空间剥夺视角下的乡村贫困研究述评 总被引：2，自引：1，他引：1
由快速城镇化衍生的空间剥夺行为,导致对乡村地域的资源、环境、社会及人口等一系列不公平的经济社会活动,是引发乡村贫困的重要因素。空间剥夺与乡村贫困作为不可持续发展的两大问题,已经成为各界关注的重要焦点,然而迄今从空间剥夺视角关注乡村贫困问题的研究相对较少。通过对国内外相关研究成果的分析,梳理了空间剥夺与乡村贫困的理论发展及思想流派,从区域尺度、个案尺度及研究方法上分析了空间剥夺对乡村贫困影响的研究进展,阐述了空间正义视角下乡村贫困调控的研究总结与评价。在此基础上,提出转型发展背景下,未来空间剥夺与乡村贫困应关注的重点与方向。 相似文献
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空间数据挖掘技术研究进展 总被引：22，自引：0，他引：22
空间数据具有海量、非线性、多尺度、高维和模糊性等复杂性特点,空间数据挖掘技术是对空间数据中非显性的知识、空间关系等模式的自动提取。该文从空间数据挖掘的知识类型、方法、体系结构、过程以及与GIS系统集成等方面对其进行综述。重点阐述空间特征及区分规则、空间分类及聚类规则、空间分布及关联规则、空间序列及演化规则等知识类型以及统计分析、机器学习、探索性数据分析、可视化分析等数据挖掘方法。通过对空间数据挖掘理论、应用和系统实现等方面研究方向、存在问题的分析,指出集数据库、知识库、专家系统、决策支持系统、可视化工具、网络等技术于一体的空间数据挖掘系统是其主要发展方向。 相似文献
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“生产-生活-生态”空间识别与优化研究进展 总被引：1，自引：0，他引：1
《地理科学进展》2020,(3)
"生产-生活-生态"(简称"三生")空间识别与优化研究是在识别现状空间结构、格局及问题的基础上,对未来空间进行优化布局,实现空间的均衡、可持续性发展,是一种更具综合性的国土空间分区方式,已成为当前学术前沿和国土空间规划亟需解决的实践问题。论文通过文献调研法、对比法和归纳法,系统梳理了"三生"空间概念内涵、识别与优化研究现状。目前,"三生"空间识别研究取得了阶段性成果,但"三生"空间形成的内在机理与概念探讨不足,"三生"空间的定量识别方法与技术体系研究有待完善,"三生"空间动态演化及驱动机制、空间冲突诊断和问题分析较为薄弱,"三生"空间优化理论和技术体系尚处于初级阶段。未来,应形成"三生"空间识别与优化"质""量"观,借鉴国际空间规划已有的研究成果,以"‘三生’空间形成机理与概念界定、定量识别、演化机制挖掘、冲突诊断与问题分析、优化调控与模拟"为主线,系统构建"三生"空间识别与优化的理论与技术体系;同时,重视具有水平方向和垂直方向特征的山区"三生"空间识别与优化研究。 相似文献
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基于R树的分布式并行空间索引机制研究 总被引：2，自引：0，他引：2
为提高分布式并行计算环境下海量空间数据管理与并行化处理的效率,基于并行空间索引机制的研究,设计一种多层并行R树空间索引结构。该索引结构以高效率的并行空间数据划分策略为基础,以经典的并行计算方法论为依据,使其结构设计在保证能够获得较好的负载平衡性能的前提下,更适合于海量空间数据的并行化处理。以空间范围查询并行处理的系统响应时间为性能评估指标,通过实验证明并行空间索引结构具有设计合理、性能高效的特点。 相似文献
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随着数字技术逐渐深入到乡村生产生活的各个方面,乡村社会关系及空间意义发生重构。信息化冲击下的乡村空间转变不仅体现在物质方面,其社会文化空间意义及人地关系地域系统的重构在内生逻辑上重塑乡村话语权力和主体身份。论文通过探讨数字技术与乡村发展的多维关系及其对乡村产生的多重效应,关注数字技术带来的自由与异化、数字经济引发的话语与权力流变及乡村女性主体地位和身份的重构,解释了数字技术作为非人类行动者的主体性及其建构下的乡村日常经济生活和社会关系,拟为乡村性的分析框架提供有益的补充,为数字乡村建设和乡村振兴提供新的理论借鉴。 相似文献
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在区域一体化及黄河流域生态保护和高质量发展战略背景下,黄河下游滩区将承担更加多元的生态与社会功能,滩区周边城镇空间进入新的快速成长期。基于城镇空间扩展差异指数、紧凑度、相关维数的定量测算探析2000—2020年黄河下游滩区周边城镇空间扩展格局及形态特征,在此基础上耦合随机森林回归算法与重心-时空地理加权回归（GTWR）模型,采用“主成分分析-重要性判断-驱动空间识别”的分析方法对城镇空间扩展时空变化进行驱动机制的多维度解析。结果表明：(1)黄河下游滩区周边地区城镇空间呈圈层式扩展,时空分异与空间溢出特征显著;2005—2010年为高速发展期,此后众多城镇空间重心向靠近黄河与中心城市方向迁移,局部地区城镇空间沿河流方向轴向拓展,区域一体化发展不断加强。(2)研究范围内城镇空间形态在波动变化中不断演进,经历了多点集中式扩展-局部线性蔓延-组团化协同发展的变化过程,城镇间联系日趋紧密,簇群结构日渐完善。(3)社会经济、产业结构、自然条件构成了滩区周边城镇扩张的内驱力,政策战略与水利工程形成了城镇发展的外驱力,二者耦合协同推动滩区周边城镇扩展格局与空间形态的不断变化。社会经济与产业结构是城镇扩... 相似文献
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3D GIS与3D GMS中的空间构模技术 总被引：74，自引：12，他引：62
3DGIS和3DGMS是近10年来地学领域并行发展的两大领域,该文从研究对象,数据来源,空间参照,空间构模,拓扑描述,空间量算,空间分析及应用领域等方面分析了3DGIS和3DGMS异同,并从数学模型,高程特征,属性特征和构建方式等方面讨论并重新界定了空间维数问题,重点分析了空间构模技术,将3D GIS和3D GMS中的空间构模分为基于面模型,基于体模型和混合构模3大类,并进行了分析比较和讨论,指出3D GIS和3D GMS将殊途同归,并最终建立全要素的真3D地球信息系统。 相似文献
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A strategy for raster-based geocomputation under different parallel computing platforms 总被引：1，自引：0，他引：1
Cheng-Zhi Qin Li-Jun Zhan A-Xing Zhu Cheng-Hu Zhou 《International journal of geographical information science》2014,28(11):2127-2144
The demand for parallel geocomputation based on raster data is constantly increasing with the increase of the volume of raster data for applications and the complexity of geocomputation processing. The difficulty of parallel programming and the poor portability of parallel programs between different parallel computing platforms greatly limit the development and application of parallel raster-based geocomputation algorithms. A strategy that hides the parallel details from the developer of raster-based geocomputation algorithms provides a promising way towards solving this problem. However, existing parallel raster-based libraries cannot solve the problem of the poor portability of parallel programs. This paper presents such a strategy to overcome the poor portability, along with a set of parallel raster-based geocomputation operators (PaRGO) designed and implemented under this strategy. The developed operators are compatible with three popular types of parallel computing platforms: graphics processing unit supported by compute unified device architecture, Beowulf cluster supported by message passing interface (MPI), and symmetrical multiprocessing cluster supported by MPI and open multiprocessing, which make the details of the parallel programming and the parallel hardware architecture transparent to users. By using PaRGO in a style similar to sequential program coding, geocomputation developers can quickly develop parallel raster-based geocomputation algorithms compatible with three popular parallel computing platforms. Practical applications in implementing two algorithms for digital terrain analysis show the effectiveness of PaRGO. 相似文献
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总结了数字高程模型构建、特征提取等并行算法的研究进展,概述了不同并行算法的主要内容;探讨了DTA并行技术在海量地形数据可视化和高性能地学计算的应用,随着DEM的需求日益增大,高精度、高分辨率DEM产品及其附加服务也逐步产品化。最后,通过分析并行计算发展的关键问题,提出DTA并行技术的研究趋势及研究意义,合适的数据划分和结果融合策略、通用并行算法、容错机制和负载均衡策略的设计是今后研究的重要内容,尤其是如何在多种计算模式共同发展的背景下利用并行计算解决地学难题,从而得到更接近现实世界地理环境的模拟,并扩大数字地形分析的应用范围。 相似文献
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Qingfeng Guan Keith C. Clarke 《International journal of geographical information science》2013,27(5):695-722
A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data–task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH. 相似文献
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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
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Marc P. Armstrong Richard J. Marciano 《International journal of geographical information science》2013,27(6):713-729
Abstract Large spatial interpolation problems present significant computational challenges even for the fastest workstations. In this paper we demonstrate how parallel processing can be used to reduce computation times to levels that are suitable for interactive interpolation analyses of large spatial databases. Though the approach developed in this paper can be used with a wide variety of interpolation algorithms, we specifically contrast the results obtained from a global ‘brute force’ inverse–distance weighted interpolation algorithm with those obtained using a much more efficient local approach. The parallel versions of both implementations are superior to their sequential counterparts. However, the local version of the parallel algorithm provides the best overall performance. 相似文献
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传统分布式水文模型采用串行计算模式,其计算能力无法满足大规模水文精细化、多要素、多过程耦合模拟的需求,亟需并行计算的支持。进入21世纪后,计算机技术的飞速发展和并行环境的逐步完善,为分布式水文模型并行计算提供了软硬件支撑。论文从并行环境、并行算法2个方面对已有研究进行总结概括,分析了不同并行环境和并行算法的优势与不足,并提出提高模型并行效率的手段,即合理分配进程/线程数缩减通信开销,采用混合并行环境增强模型可扩展性,空间或时空离散化提高模型的可并行性及动态分配计算任务、平衡工作负载等。最后,论文对高性能并行分布式模型的未来研究方向进行展望。 相似文献
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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
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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
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Jayakrishnan Ajayakumar Eric Shook 《International journal of geographical information science》2020,34(9):1683-1707
ABSTRACT Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis. 相似文献