地图目标移位的空间关系维护

在分析有限元方法处理地图目标移位问题的基本思路和地图目标移位产生的空间关系冲突传播特征的基础上,提出了一个利用空间推理和有限元分析相结合的方法维护地图目标空间关系的算法。     

空间目标的方向关系定性推理

由于空间问题固有的复杂性和不确定性,空间关系的描述和推理普遍采用定性的方法来符合人们的认知。方向关系是一类重要的空间关系,以往对方向关系的定性推理大多在2个参照系下进行,将目标对象抽象为点或者认为目标的大小近似相等。为了更细致地推理方向关系,提出四种不同情况下基于1个参照系的定性方向关系推理方法,并就本方法存在的问题和今后的研究方向提出了一些看法和思路。     

空间目标方向关系的定性扩展描述

由于空间问题固有复杂性不确定性，空间关系的描述和推理普遍采用定性方法以符合人们的空间认知行为，方向关系是一类重要的空间关系，用以确写目标的方位，以往的方向关系描述模型通常将空间目标作为一个抽象点，或者用目标的最小外接矩形表示目标，这两种方法都丰在明为不足，为了更细致，准确的描述方向方法，文章提出一种方面关系描述的分层多处理方法,在以点，线，面为参照目标的空间目标间建立方向关系描述的3层模式结构，每层模式又由基本方向关系描述，推理模型和方向关系描述，推理化模型组成，从而扩展了方向关系的描述。     

2维矢量地图空间目标关系的组合式表达

矢量模式的地图平面空间目标是地图数据处理中常见的,其空间关系的描述与处理非常重要本文首先分析了平面地图上的尺度空间和拓扑空间的统一,以及点和直线段两两相互间的基本空间关系,并以这些基本窨绵组合为基础,试图利用组合式方法区分和描述地图空间点状、线状和面状空间目标两两相互间的空间关系,且得到各类空间关系的种数。     

基于空间映射观念的地图综合概念模式

基于关系代数的映射变换思想，将地图综合的概念框架划分为2种映射形式：空间实体映射和空间关系映射，根据变换前后空间实体数目的变化，空间实体映射进一步区分为1－1，n-1,n-m映射。空间关系映射则视为拓扑关系、距离关系、方向关系3种基本关系的复合变换。为描述综合过程中粗化表达，建立了“空间关系分辨率”概念，基于9交叉、主方向模型以及本文自行建立的“等距离关系”模型分别给出了拓扑关系、方向关系、距离关系中分辨率的表达方式。在概念级上讨论了2种映射在地图综合过程的表现形式，这一综合模式与传统方法的显著区别是，它强调了关系也存在综合的问题。     

三个目标间空间方位关系表达模型及其GIS应用

空间方位是一类重要的空间关系,它不同于目前研究较多的方向关系。空间方位关系描述的是三个目标间的空间关系,它的理论基础是基于射影几何中的三点共线不变性。本文首先阐述了空间方位的基本定义、表达基础以及它与拓扑关系、方向关系的区别。然后,重点回顾了6种较为有代表性的方位关系描述模型,指出了这些模型的适用性,并对这些模型进行了比较分析。最后,简要地分析了空间方位关系在GIS中的应用前景以及有待进一步研究的若干问题。     

地图综合中地图目标上下文分析

针对地图综合中地图目标上下文分析,提出一个区分不同要素类层面、尺度层次及人文、自然特点的上下文结构体系,在此基础上简单探讨了上下文的概念、内容、表现形式及相关计算方法。     

地图目标移位的拓扑关系变化检测方法

利用带约束Delaunay三角网所蕴涵的地图目标之间的拓扑及拓扑邻近关系，研究地图目标移位的安全区和警戒线，并探讨不同条件下的安全区和警戒线的变形情况，基于这种分析，给出地图目标移位的拓扑关系变化检测方法。     

三维体目标间拓扑关系与方向关系的混合推理

重点研究了三维空间中拓扑关系和方向关系间的混合空间关系推理。用Allen区间关系对描述基于投影的空间划分方法得到的方向区域和用九交矩阵描述的拓扑关系,用定义法研究混合空间关系推理,推理结果用组合推理表表示。     

基于Delaunay三角网的空间邻近关系推理

空间邻近关系是一种重要的空间关系,对这种关系的识别是任何空间建模系统所必须的。Delaunay三角网是根据最小角最大规则建立的一种三角剖分,可以较好地表达空间目标之间的邻近关系。利用Delaunay三角网对空间邻近关系进行了描述,在此基础上提出了空间邻近关系推理方法。     

On the spatial distribution of buildings for map generalization

Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization.     

用于数字地图自动综合的多边形合并算法

从计算几何出发,实现了能考虑到空间关系和形态要求的一种新的多边形合并算法--"凸包演化"算法,并用于自动综合软伴中,取得了较好的效果.     

Topological relations between spherical spatial regions with holes

ABSTRACT

There is growing interest in globally modelling the entire planet. Although topological relations between spherical simple regions and topological relations between regions with holes in the plane have been investigated, few studies have focused on the topological relations between spherical spatial regions with holes. The 16-intersection model (16IM) is proposed to describe the topological relations between spatial regions with holes. A total of 25 negative conditions are proposed to eliminate the impossible topological relations between spherical spatial regions with holes. The results show that (1) 3 disjoint relations, 3 meet relations, 66 overlap relations, 7 cover relations, 3 contain relations, 1 equal relation, 7 coveredBy relations, 3 inside relations, 1 attach relation, 52 entwined relations, and 28 embrace relations can be distinguished by the 16IM and that (2) the formalisms of attach, entwined, and embrace relations between the spherical spatial regions without holes based on the 9IM and that between the spherical spatial regions with holes based on the simplified 16IM are different, whereas the formalisms of other types of relations between spherical spatial regions without holes based on the 9IM and that between the spherical spatial regions with holes based on a simplified 16IM are the same.     

Quantitative relations between spatial similarity degree and map scale change of individual linear objects in multi-scale map spaces

Quantitative relations between spatial similarity degree and map scale change in multi-scale map spaces play important roles in map generalization and construction of spatial data infrastructure. Nevertheless, no achievements have been made regarding this issue. To fill the gap, this paper firstly proposes a model for calculating spatial similarity degrees between an individual linear object at one scale and its generalized counterpart at the other scale. Then psychological experiments are designed to validate the new model, taking four different individual linear objects at five different scales as test samples. The experiments have shown that spatial similarity degrees calculated by the new model can be accepted by a majority of the subjects. After this, it constructs a formula that can calculate spatial similarity degree using map scale change (and vice versa) for individual linear objects in multi-scale map spaces by the curve fitting method using the point data from the psychological experiments. Both the formula and the model can calculate quantitative relations between spatial similarity degree and map scale change of individual linear objects in multi-scale map spaces, which facilitates automation of map generalization algorithms for linear features.     

基于要素空间关系的1∶10000 DLG数据自动整合

文中通过分析DLG数据整合过程中遇到的居民地地名层缺少地名编码、植被注记混乱、面域水系与线形水系的HYDC码不统一等问题,提出一种基于要素空间关系的自动整合方法,以云南省1∶10 000DLG数据整合为例,借助ArcGIS的二次开发实例,验证该方法的可行性。     

空间关系的研究进展

空间关系是GIS的重要理论问题之一,在GIS数据建模、空间查询、空间分析、空间推理、制图综合、地图理解等过程中起着重要作用。目前国际上对空间关系的研究主要集中在空间关系的语义问题、空间关系描述、空间关系表达、基于空间关系的查询分析等方面。本文主要讲述了这几个方面的研究进展和成果。     

土地利用现状图斑的自动综合

本文针对土地利用现状数据中图斑的自动综合,采用语义与空间相结合的方法,分析了影响图斑综合的因素,根据这些因素的影响对土地利用多边形进行合并、融合及聚合操作。并以长沙市宁乡县的土地利用数据为例进行实验,结果表明,该方法较好地保持了宁乡县综合前后各地类的结构布局,能够较好地反映土地利用现状布局,节省了人机交互合并的工作量。