3D GIS空间索引技术研究

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。     

基于空间数据库技术的地籍管理系统研究

随着GIS应用的深入发展,以文件管理空间数据的方式已不能满足当前GIS应用的需要,而空间数据库技术正是为解决GIS应用的企业化和社会化问题而发展起来的新技术,空间数据库技术及其应用研究是当前GIS技术研究的热点。本文分析了空间数据对象、空间数据库、空间数据库技术及其实现,研究了基于空间数据库技术的地籍管理系统在空间数据共享、海量数据管理、大范围查询检索、分布式空间数据处理与维护、地籍制图、数据安全控制等方面的应用实现,通过具体应用系统建设,提出了基于空间数据库技术的地籍管理信息系统的解决方案。     

Ellipsoidal quadtrees for indexing of global geographical data

Systems for landscape visualization and geographical data handling require methods for efficient data access. Retrieval of data from large geographical databases, ten to thousands of Gbytes, is usually optimized with spatial indexing mechanisms. The simplest form of spatial indexing is achieved by dividing the database into congruent grid cells. The subsequent subdivision of the grid cells can be based on so-called quadtrees. Quadtrees for two-dimensional division and subdivision are appropriate for cartographical data. A geographical database, with objects stored in geocentric or geodetic (geographical) co-ordinates, requires indexing mechanisms that take into account the shape of the Earth. In this paper, we present a method for indexing of geographical data, named Ellipsoidal Quadtrees (EQT). In contrast to other global indexing methods, EQT is based on the Earth ellipsoid and not a spherical approximation. EQT division and subdivision make it possible to divide the Earth surface into a mesh of quadrangles with equal areas. We will demonstrate that EQT is flexible. It can be used for indexing databases of various sizes, including national and global databases. Tests on real data show that the performance of EQT is good.     

3DCM的几何与纹理数据一体化管理方法研究

建设三维城市模型(3DCM)首先需要建立场景所需的海量地形地物空间数据库,从而有效组织三维实体数据来满足大范围高精度场景实时绘制的要求.该文提出一种以三维空间实体为单位的基于关系数据库(RDBMS)的3DCM的几何与纹理数据一体化管理方法.采用Direct3D 9.O三维开发平台、C#编程语言实现了3DCM空间数据库模型管理系统.实验证明:该系统在效率、安全、网络多用户访问方面都优于传统基于文件的管理模式,满足了大范围3DCM海量模型数据管理与应用需求.     

A comparative analysis of some two-dimensional orderings

Abstract

Many spatial analysis algorithms for cellular representations and data structures are based on an ordering of cells or objects to transform a two-dimensional problem to a one-dimensional one. Several orderings are available and their differing properties influence the performance of the data structures and algorithms. The relative merits of five orderings (row, row prime, Hilbert, Morton and Gray code) are assessed empirically for four paradigmatic geographical data-processing tasks in spatial analysis and data management. It is concluded that the Hilbert ordering deserves further investigation.     

Optimizing an index with spatiotemporal patterns to support GEOSS Clearinghouse

A variety of Earth observation systems monitor the Earth and provide petabytes of geospatial data to decision-makers and scientists on a daily basis. However, few studies utilize spatiotemporal patterns to optimize the management of the Big Data. This article reports a new indexing mechanism with spatiotemporal patterns integrated to support Big Earth Observation (EO) metadata indexing for global user access. Specifically, the predefined multiple indices mechanism (PMIM) categorizes heterogeneous user queries based on spatiotemporal patterns, and multiple indices are predefined for various user categories. A new indexing structure, the Access Possibility R-tree (APR-tree), is proposed to build an R-tree-based index using spatiotemporal query patterns. The proposed indexing mechanism was compared with the classic R*-tree index in a number of scenarios. The experimental result shows that the proposed indexing mechanism generally outperforms a regular R*-tree and supports better operation of Global Earth Observation System of Systems (GEOSS) Clearinghouse.     

动态广义表空间索引方法

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。     

Java环境下基于Hibernate的GIS数据库访问策略

全关系型数据存储模式将GIS数据的空间数据和属性数据统一存储于关系数据库中,分析Java应用程序通过JDBC和SQL语言访问关系数据库中存储的GIS数据的不足,提出基于。Hibernate的解决方案,并给出地理信息共享系统的开发实例。     

A constant key length coding scheme for linear quadtrees

ABSTRACT

Although the linear quadtree is popular in both image processing and GIS, most linear quadtree coding methods need in representation a bit length that is proportional to the resolution of images; the proportionality factor is replaced with a constant bit length that induces a more compact linear quadtree. We propose a linear quadtree coding scheme named constant key length quadtree coding (CKQC) that has advantages of easy implementation and decreased memory requirements. The proposed CKQC scheme is developed on the bases of node classification, order of traversal with breadth first and the Morton sequence. The major characteristic is to apply a constant bit length to construct quaternary codes. The bit length of each quaternary code is constant, either four or eight bits, no matter what the resolution of a raster image. In terms of memory space required and duration complexities, the overall performance is analyzed and compared with other methods to demonstrate the superiority of the proposed CKQC scheme. Several experimental results validate the applicability of the proposed scheme.     

空间编码QuaPA方法的改进与实验

空间编码是GIS与全球空间数据组织与共享的重要基础。针对QuaPA编码方法进行三维与全球空间编码时存在数据冗余和象限二义性问题,采用双Morton编码和象限标识后置方式进行改进,提出QuaPA 方法。介绍QuaPA 编码步骤及其反演原理,并以中国及邻区地表及地下10 km2、0 km以浅的三维球壳空间网格划分为例,分别进行了球壳网格的QuaPA和QuaPA 编码实验。实验表明,QuaPA 编码解决了象限二义性问题,缩短了编码长度,节省了存储空间。     

一种改进的DQG格网编码与经纬度高效转换算法

格网编码与经纬度间的转换效率是影响格网应用的关键因素。现有转换算法难以满足海量空间大数据的实时计算与分析需求,效率有待提升。为此,该文提出一种改进的DQG格网编码与经纬度高效转换算法:首先,引入二进制DQG格网编码代替四进制编码;然后,根据DQG格元的分布特征推导出格元行号和经度差的关系,以此改进格元列号的计算过程;最后,使用查找表进行Morton码的编码和解码,以提高转换效率。实验结果表明,该文提出的DQG格网编码与经纬度转换算法的平均效率分别是四进制DQG、单层二维SDZ算法的20.15倍和4.58倍,基本满足海量格网数据的实时转换与计算需求,为海量空间大数据的高效计算与分析奠定了基础。     

基于R树的分布式并行空间索引机制研究

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。     

中国航空联系的网络结构与区域差异

区域空间联系是不同客体之间基于空间法则下的相互作用现象,航空联系是诸多空间联系的一种。依据中国国内航班运营信息数据,运用基于O-D联系网络的GIS空间分析法,通过在大尺度数据空间内刻画每一条运营航线,深入解析全国通航中心城市间（不含港澳台）航空联系的网络结构,并与按重力模型计算的空间联系进行对比,分运距区段绘制中国城市空间相互作用联系与航空联系网络结构图谱,深入揭示航空联系的空间相互作用本质。进而分析东、中、西部及东北4区域间航空联系的网络结构差异,揭示中国航空联系的网络结构及区域结构特征。研究发现,城市航空联系网络与人口、经济之间的空间相互作用高度吻合,大城市集聚特征显著,空间分布较不平衡。依据进出港航班数量,可将通航中心城市划分为全国性、区域性、省域及地方性中心4个等级,其中北京、上海、广州为全国性中心。城市航空联系主要集中在600~2 000 km空间距离范围内,总体上服从空间距离衰减规律。全国航空客流的区域分布极不平衡,东部航空运输地位极其突出,西部相对较强,中部与东北相对较弱。     

Guest editorial

The past decade has witnessed extensive development of measures that examine characteristics of spatial subsets (local spaces) defined with respect to a complete data set (global space). Such procedures have evolved independently in fields such as geography, GIS, cartography, remote sensing, and landscape ecology. Collectively, we label these procedures as local spatial methods. We focus on those methods that share a common goal of identifying subsets whose characteristics are statistically ‘significant’ in some way. We propose the concept of local spatial statistical analysis (LoSSA) both as an integrative structure for existing methods and as a framework that facilitates the development of new local and global statistics. By formalizing what is involved when a particular local statistic is used, LoSSA helps to reveal the key features and limitations of the procedure. These include a consideration of the nature of the spatial subsets, their spatial relationship to the complete data set, and the relationship between a given global statistic and the corresponding local statistics computed for the data set.     

一种面要素语义位置的GeoSOT网格定位方法

为解决传统地图查询方法中对复杂地址对象难以定位的问题,该文面向地理网格的时空大数据分析需求,结合面要素特点与语义分析,提出一种面要素语义位置的GeoSOT网格定位方法。首先通过最小外包矩形(Minimum Bounding Rectangle,MBR)将面要素以九宫格形式分割成9个区块,单独计算各区块的网格编码集合;然后提取位置语句中的名称与空间关系,根据具体情境进行直接查询,或利用网格编码计算目标位置;最后根据语义考虑是否对编码结果做差集运算,从而得到目标的GeoSOT网格定位范围。实验表明,对比现有的地图服务,该方法能够实现语境更加复杂的位置定位,具有可行性和有效性,其提供的未知要素推理定位方法能够有效补充、扩展原有的面要素数据集,可为后续针对面状区域的时空大数据分析提供网格化的数据资源。     

Hierarchical self-organizing maps for clustering spatiotemporal data

Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonly exhibit spatial and temporal dimensions. To explore, extract, and generalize inherent patterns in large spatiotemporal data sets, clustering algorithms are indispensable. These clustering algorithms must account for the distinct special properties of space and time to outline meaningful clusters in such data sets. Therefore, this research develops a hierarchical method based on self-organizing maps. The hierarchical architecture permits independent modeling of spatial and temporal dependence. To exemplify the utility of the method, this research uses an artificial data set and a socio-economic data set of the Ostregion, Austria, from the years 1961 to 2001. The results for the artificial data set demonstrate that the proposed method produces meaningful clusters that cannot be achieved when disregarding differences in spatial and temporal dependence. The results for the socio-economic data set show that the proposed method is an effective and powerful tool for analyzing spatiotemporal patterns in a regional context.     

基于GIS的城市商业网点规划实施效果评估

商业网点规划是指导和管理城市商业空间发展的法律依据,对实施效果的评估是评价规划和监督执行的重要措施之一。中国已实施的城市规划评估大都以定性评估方法为主,难以保证评估的客观性和科学性。基于长沙市商业网点规划和实地调查数据,从空间布局和业态结构两个方面,应用GIS分析方法,对发展现状与商业网点规划进行比较研究。研究显示:规划与现状存在较大差别,商业网点规划没有充分认识城市空间结构、商业业态的发展特点和空间区位选择机制,未能有效地引导和控制商业和业态空间结构的发展和演变。     

Benefits and drawbacks of two modelling approaches for a generalist carnivore: can models predict where Wile E. Coyote will turn up next?

Wildlife ecologists frequently make use of limited information on locations of a species of interest in combination with readily available GIS data to build models to predict space use. In addition to a wide range of statistical data models that are more commonly used, machine learning approaches provide another means to develop predictive spatial models. However, comparison of output from these two families of models for the same data set is not often carried out. It is important that wildlife managers understand the pitfalls and limitations when a single set of models is used with limited GIS data to try to predict and understand species distribution. To illustrate this, we carried out two sets of models (generalized linear mixed models (GLMMs) and boosted regression trees (BRTs)) to predict geographic occupancy of the eastern coyote (Canis latrans) on the island of Newfoundland, Canada. This exercise is illustrative of common spatial questions in wildlife research and management. Our results show that models vary depending on the approach (GLMM vs. BRT) and that, overall, BRT had higher predictive ability. Although machine learning has been criticized because it is not explicitly hypothesis-driven, it has been used in other areas of spatial modelling with success. Here, we demonstrate that it may be a useful approach for predicting wildlife space use and to generate hypotheses when data are limited. The results of this comparison can help to improve other models for species distributions and also guide future sampling and modelling initiatives.     

Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart,linear discriminant analysis

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.