论地理国情监测的技术支撑

地理国情监测是新时期空间信息科学的重要使命。对地理国情监测的必要性和内容进行了讨论,逐一剖析了空天地一体化遥感技术、全球卫星导航定位技术、网格GIS技术、地理信息网络服务、多维时空数据挖掘技术、空间信息云计算技术等在地理国情监测中的地位和作用、面临的问题和发展建议,以期为全面实现地理国情的定期监测、日常监测和应急监测奠定技术基础。     

Using Information Technologies for Interactive Learning

Abstract

Experiences in designing, offering, and evaluating joint senior undergraduate course assignments in resource and environmental management on two different occasions, which connected students in two universities in British Columbia and Ontario, are documented. The shared assignments provided students with experience in group work and consensual decision making, and allowed them to use a mix of information technologies (e-mail, chat rooms, the Web, video conferencing). The undergraduate students rated very highly the experience with information technology but found some aspects of a collaborative learning approach to be challenging. The course was less successful than had been anticipated in highlighting different regional perceptions and perspectives regarding resource and environmental problems and solutions. Clarification of objectives and expectations, more attention to allocation of time, and consideration of computer access and assistance would improve the effectiveness of the courses.     

DEM与地形分析的并行计算

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。     

云计算及其在地理信息工程中的应用

云计算是网格计算及分布式计算、并行计算发展到一定阶段后衍生出来的,是这些计算方法的商业实现。云计算是一种新型的计算模型,其基础体系结构属于云计算的技术层次,主要说明系统属性和设计思想;云计算的服务集合源于服务类型,说明能带给客户什么。云计算的核心技术有数据存储技术、数据管理技术、编程模型和云安全。云计算在地理信息工程和地理空间信息网格计算中有着广泛的应用前景。     

MatDEM-fast matrix computing of the discrete element method

Discrete element method can effectively simulate the discontinuity, inhomogeneity and large deformation and failure of rock and soil. Based on the innovative matrix computing of the discrete element method, the high-performance discrete element software MatDEM may handle millions of elements in one computer, and enables the discrete element simulation at the engineering scale. It supports heat calculation, multi-field and fluid-solid coupling numerical simulations. Furthermore, the software integrates pre-processing, solver, post-processing, and powerful secondary development, allowing recompiling new discrete element software. The basic principles of the DEM, the implement and development of the MatDEM software, and its applications are introduced in this paper. The software and sample source code are available online (http://matdem.com).     

基于微服务架构的地震站网全流程一体化监控平台设计与实现

地震站网全流程一体化监控平台是基于微服务框架,应用数据库、通信、并行计算等传统信息技术,结合新兴的云计算、大数据进行建设的信息服务平台。该平台采用Hadoop分布式处理方案,应用Spring Cloud框架搭建,进行Docker容器封装,以标准RESTful API作为服务接口的微服务架构,实现了各应用服务模块之间的高内聚、低耦合及灵活、可扩展的特性,最终实现对地震台站运行的实时监测、高效运维和一体化管理。     

网格GIS及其在数字油田中的应用探讨

数字油田是解决当前油田智能化管理问题的最好途径。该文分析了当前油田建设中所面临的问题,结合油田在勘探开发中的实际需要,特别就当前数字油田建设中所面临的共性问题,在分析现有油田技术及发展需求的基础上,指出了基于中间件的分布式网格GIS技术是解决当前数字油田领域中所存在的问题的最佳方式,并讨论了其实现过程。     

Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities

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.     

GIS典型几何计算算法的并行化及优化研究

空间数据规模的快速增长对传统矢量数据分析方法提出了更高的计算效率和处理规模要求。随着计算机硬件和软件技术的进步,并行计算为提高GIS中典型几何计算算法的计算效率、扩大问题处理规模提供了有效手段。本文在Visual Studio 2010中,使用标准C++编程语言,基于GDAL(Geospatial Data Abstraction Library)库实现空间数据的读写操作,针对线简化算法的并行化问题,在高性能计算环境下对并行任务调度策略、并行计算粒度、数据分解方法等多个核心内容开展研究。在完成相关串行算法的基础上,实现了该算法的并行化和优化设计,为相关的矢量数据空间分析方法的多核并行优化提供了思路和参考。     

地理要素的分布式空间近似查询处理方法

针对传统空间查询无法满足地理数据交互式可视化对处理时间要求的问题,以窗口查询为例,提出了一种空间近似查询处理方法。该方法包括预处理和查询两步:在预处理阶段,利用分布化的线简化算法对空间对象进行顾及误差的预处理采样,将采样过程及误差值用树型结构保存;在查询阶段,以豪斯多夫距离定义数据可视化的误差,进行误差可知的顶点即时采样与截取,从而实现针对可视化应用的高效的空间近似查询处理。在Hadoop集群上利用77GB的OpenStreetMap数据集进行了实验,证实了本方法的效力与效率。