印度地理信息产业发展基础与现状研究

主要从地理信息资源生产管理体制、空间基础设施建设、产业政策3个方面分析了印度地理信息产业发展基础,并对当前印度地理信息产业规模、市场、企业、人才培养以及发展潜力等方面的现状进行了研究,对比我国分析了当前印度地理信息产业发展的优势和存在的制约。     

城市地理空间数据集成应用探讨

城市地理空间数据是表达城市地理要素的数量、质量、分布特征、相互关系、变化规律的位置信息和属性信息。分析城市地理空间数据的组成结构、组织管理、集成利用,并将这些数据有效应用于城市地理信息系统建设之中,将在城市信息化道路上起到举足轻重的作用。该文以开发的"兰州市城关区地理信息集成系统"为例,重点阐述了城市地理空间数据的构成与获取方式,研究了城市地理空间数据与业务部门数据的集成方法,并对城市地理空间数据在晕渲图制作、三维景观生成、数据可视化等主要应用上进行了分析和探讨。     

广东西江流域地名调查工作特点与实例分析

地名调查工作涉及地域划分,地理空间,语言文化,历史文化等方面,运用"3S"技术,结合当地语言文化开展此项工作,能提高调查工作效率和准确度,有利于对存在尚未掌握的地理实体及地名信息的判断、界定和甄别。通过广东郁南县地名调查工作实例,探讨在"3S"技术背景下,根据具有地域性文化特征地区的地名调查工作所呈现的新问题、新特点,可作范例为粤语地区的地名调查提供可操作性建议。     

面向地理空间框架建设的城市大比例尺数据整合改造研究与实践

城市大比例尺基础地理信息数据蕴含了丰富的信息,是其他信息空间化、标准化处理的基础和空间依据,是数字城市地理空间框架建设的重要组成部分。本文以福建省明溪县为例,讨论了城市大比例尺数据整合改造方法的研究,以及数据制作过程中所遇技术难题的解决办法,探索了一条高效可行的城市大比例尺数据改造方法。     

A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology

ABSTRACT

Big data have shifted spatial optimization from a purely computational-intensive problem to a data-intensive challenge. This is especially the case for spatiotemporal (ST) land use/land cover change (LUCC) research. In addition to greater variety, for example, from sensing platforms, big data offer datasets at higher spatial and temporal resolutions; these new offerings require new methods to optimize data handling and analysis. We propose a LUCC-based geospatial cyberinfrastructure (GCI) that optimizes big data handling and analysis, in this case with raster data. The GCI provides three levels of optimization. First, we employ spatial optimization with graph-based image segmentation. Second, we propose ST Atom Model to temporally optimize the image segments for LUCC. At last, the first two domain ST optimizations are supported by the computational optimization for big data analysis. The evaluation is conducted using DMTI (DMTI Spatial Inc.) Satellite StreetView imagery datasets acquired for the Greater Montreal area, Canada in 2006, 2009, and 2012 (534 GB, 60 cm spatial resolution, RGB image). Our LUCC-based GCI builds an optimization bridge among LUCC, ST modelling, and big data.     

Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function

Performing point pattern analysis using Ripley’s K function on point events of large size is computationally intensive as it involves massive point-wise comparisons, time-consuming edge effect correction weights calculation, and a large number of simulations. This article presented two strategies to optimize the algorithm for point pattern analysis using Ripley’s K function and utilized cloud computing to further accelerate the optimized algorithm. The first optimization sorted the points on their x and y coordinates and thus narrowed the scope of searching for neighboring points down to a rectangular area around each point in estimating K function. Using the actual study area in computing edge effect correction weights is essential to estimate an unbiased K function, but is very computationally intensive if the study area is of complex shape. The second optimization reused the previously computed weights to avoid repeating expensive weights calculation. The optimized algorithm was then parallelized using Open Multi-Processing (OpenMP) and hybrid Message Passing Interface (MPI)/OpenMP on the cloud computing platform. Performance testing showed that the optimizations effectively accelerated point pattern analysis using K function by a factor of 8 using both the sequential version and the OpenMP-parallel version of the optimized algorithm. While the OpenMP-based parallelization achieved good scalability with respect to the number of CPU cores utilized and the problem size, the hybrid MPI/OpenMP-based parallelization significantly shortened the time for estimating K function and performing simulations by utilizing computing resources on multiple computing nodes. Computational challenge imposed by point pattern analysis tasks on point events of large size involving a large number of simulations can be addressed by utilizing elastic, distributed cloud resources.     

南京市图书零售业空间布局及其影响因素

以南京市图书零售业为研究对象,借助GIS空间分析方法,从城市空间的角度研究图书零售业空间布局结构及其影响因素。结果表明:南京市图书零售业存在中心聚集-外围分散、地区差异显著、道路沿线布局、与商业中心和学校布局高度相关等空间布局特征。在此基础上分析了地区发展水平、人口、交通、地价对南京市图书零售业空间布局的影响,并从图书零售业商圈、社区、校园导向3种空间布局模式视角提出了南京市图书零售业空间布局的优化建议。     

Free and open source software for geospatial applications (FOSS4G) to support Future Earth

ABSTRACT

The development, integration, and distribution of the information and spatial data infrastructure (i.e. Digital Earth; DE) necessary to support the vision and goals of Future Earth (FE) will occur in a distributed fashion, in very diverse technological, institutional, socio-cultural, and economic contexts around the world. This complex context and ambitious goals require bringing to bear not only the best minds, but also the best science and technologies available. Free and Open Source Software for Geospatial Applications (FOSS4G) offers mature, capable and reliable software to contribute to the creation of this infrastructure. In this paper we point to a selected set of some of the most mature and reliable FOSS4G solutions that can be used to develop the functionality required as part of DE and FE. We provide examples of large-scale, sophisticated, mission-critical applications of each software to illustrate their power and capabilities in systems where they perform roles or functionality similar to the ones they could perform as part of DE and FE. We provide information and resources to assist the readers in carrying out their own assessments to select the best FOSS4G solutions for their particular contexts and system development needs.     

基于BERT—BiLSTM—CRF模型的中文岩石描述文本命名实体与关系联合提取

地质调查正在从“数字化”走向“智能化”,需要在大数据思维的指导下,面向非结构化数据开展机器阅读和地质知识的自动提取。地学命名实体和关系联合提取是当前研究的难点和核心。本文采用基于大规模预训练中文语言模型的BERT—BiLSTM—CRF方法开展岩石描述文本命名实体与关系联合提取。首先,通过收集数字地质填图工作中的剖面测量和路线地质观测数据,建立岩石描述语料;然后,在岩石学理论指导下分析岩石知识组成,完成岩石知识图谱命名实体与关系的模式设计,标注岩石语料;最后,开展岩石描述语料知识提取的深度学习训练和消融试验对比。试验结果显示,大规模预训练中文语言模型（BERT）对岩石描述语料知识提取具有较高的适用性。推荐的BERT—BiLSTM—CRF模型方法对岩石命名实体与关系联合提取的准确率（F1值）为91.75%,对岩石命名实体识别的准确率（F1值）为97.38%。消融试验证明基于BERT的词嵌入层对岩石描述知识提取的性能提升影响显著,双向长短时记忆网络模型层（BiLSTM Layer）能提升实体关系联合提取性能。