顾及几何形状相似性的简单多边形最近距离计算方法

提出简单多边形距离计算方法,在计算简单多边形最近距离的同时,可以求得多边形之间彼此面对的线段链.针对多边形之间不同位置关系,给出不同的最近距离和多边形之间彼此面对线段链的计算算法.     

利用地标空间关系约束的点实体匹配方法

针对传统的点实体匹配方法的不足,提出了利用地标空间关系约束的点实体匹配方法。该方法以参考点实体、候选匹配点实体与其邻域内的同名地标之间的空间关系为基础构造距离特征向量和方向特征向量,基于距离特征向量和方向特征向量计算点实体的相似度,进而确定点同名实体。实验结果表明,该方法在点同名实体空间位置偏差较大的情况下依然能够取得很好的效果,验证了该方法的有效性。     

利用泰森多边形的点实体匹配算法

针对众源地理数据中的同名点实体之间存在距离、方向等非一致性偏差,导致匹配困难的问题,该文提出了基于泰森多边形的点实体匹配算法。利用相匹配的点实体数据集其对应的泰森多边形具有较高的对应关系这一特点,将不确定的点与点之间的匹配转化为匹配度更高的对应泰森多边形的匹配。首先统计出被彼此泰森多边形包含的点对,根据点对的距离概率分布,计算出距离阈值作为确认同名实体的条件之一;然后将泰森多边形的位置及形状相似性作为匹配条件二;最后将相似度最高的实体确认为同名实体。通过实验与现有的几种点实体匹配算法进行了比较,结果表明,该算法具有较高的查全率和查准率,且普适性强。     

任意多边形间方向关系的计算及其可确定问题

提出了一种符合空间认知规律的栅格化处理的任意多边形间方向关系的计算方法，该方法计算结果与人眼的判断结果非常接近，在此基础上讨论了多边形间方向关系的可确定问题，并提出了一种基于8方向隶属零值个数的两个多边形方向关系可确定程度的判定方法。     

地籍空间对象拓扑关系一致性研究

空间数据库是通过建立并执行空间完整性约束来维护目标及其间关系的精确性、正确性、有效性。空间完整性约束包括对几何结构冲突、属性冲突及空间关系冲突约束,其中,空间关系冲突是指目标间的拓扑、方向及度量关系应符合空间完整性约束。在地籍数据库中,空间对象拓扑关系约束是为防止任意对象及其目标间的拓扑关系冲突,避免造成数据库对实体及其之间关系的表达错误,导致自身几何结构和对象间拓扑关系错误等,从而破坏了空间数据库的完整性。     

点状地名信息的加权泰森多边形检索法

鉴于地名库中多数地名仅以中心点坐标形式记录其空间位置信息,缺乏其空间范围的具体描述,造成地名检索中的片面性和局限性,本文在深入分析地名及其空间属性、空间关系的基础上,考虑同类型地名的不同性质在检索中的重要作用,利用泰森多边形在地名边界近似中的优势,提出了针对点状地名信息的加权泰森多边形检索法,依据同类型地名的面积属性作为权重指标构建泰森多边形,以近似表达地名的空间范围,进而描述地名间的各类空间关系;并从拓扑关系、方位关系与距离关系3个方面给出了具体公式以计算地名间的空间相似性;最后以行政区划边界近似与检索为例验证该方法。试验结果表明,该方法可较好地近似表达地名空间范围及空间关系,增强了检索词与地理信息资源在空间位置上的相似性度量,检索结果优于传统方法。     

克服双重约束的面目标位置聚类方法

面目标的聚集模式识别是空间聚类研究的重要方向之一,但因多边形几何信息和空间障碍阻隔的双重约束,目标的位置相似性难以快速而准确地计算。扩展点目标多尺度聚类方法,通过构建面目标的强度函数计算目标与邻近目标的位置聚集程度,提出了有效作用于双重约束下的面目标位置聚类法,并以判断相邻尺度下同一面目标类的强度函数阈值相等作为算法的收敛条件。经试验分析与比较发现,算法无须自定义参数,能够识别密度不均、任意形状分布,以及"桥"链接的面目标集群,同时能够准确判断障碍约束对面目标簇的阻隔和划分。     

大地坐标系中多边形面积计算方法研究

面状地理实体的面积是重要地理信息之一,多边形常用来表示面状分布的地理要素。研究从CGCS 2000数字地形图数据计算多边形面积的方法。首先,基于辛普森积分公式,针对复杂多边形要素椭球面积计算改进了多边形椭球面积计算方法;其次,顾及地形起伏,提出了任意多边形要素表面积计算方法;最后,根据本文提出的方法进行试验。     

一种空间关系相似性约束的居民地匹配算法

从分析人在寻找陌生地物时的思维习惯入手,在居民地匹配过程中引入了空间关系相似性约束。对两个居民地之间的拓扑关系、距离关系和方向关系的相似性进行了分析,并提出了符合人认知习惯的离散化计算方法。在匹配过程中,以突出居民地作为起始对象,以已匹配居民地作为参照,对未匹配居民地按空间邻近原则进行广度优先搜索,利用空间关系相似性约束来缩小匹配目标备选集,实现了空间关系有序的精确匹配。最后,对已匹配对象实施了基于邻近对象空间关系相似性校验的匹配质量检查。实验结果表明,该算法在待匹配数据位移较大、居民地对象形状同质化较高的情况下优势明显,能够有效提升匹配精度。     

简单多边形间最大距离的求解算法

求解任意两个简单多边形间的最大距离,在几何图形计算中,一直是一个基本问题。在对多边形自身的特性以及两多边形间关系进行深入分析的基础上,提出了一个基于折线凸包的单调性的简单多边形间最大距离的求解算法。根据封闭折线内部所具有的特性,把封闭折线拆分成两个断开的折线,使一条折线在另一条折线左边。两个多边形分别被拆分成四条折线,两个分为一组。分别求出每组中两条折线的凸包,利用凸包的单调性可以快速地找出两个距离最远的顶点,其中较大的是两个简单多边形间的最大距离。算法的时间复杂度是线性的。     

多边形形状简化及其质量评价

在保证多边形之间拓扑关系完整的基础上,分别使用垂距限值法和Douglas-Peucker算法对多边形形状进行了简化,同时从多边形常规参数以及相似度等方面对简化后多边形的质量进行了评价。实验结果表明Douglas-Peucker算法的简化效果较好。     

支持地图综合的面状目标约束Delaunay三角网剖分

针对多边形面状目标的综合问题,建立了二维空间中约束Ｄｅｌａｕｎａｙ三角网剖分结构,融入多边形的环、岛屿、边界、顶点的描述,通过形式化条件检索,在该结构上提取二维空间中各种感兴趣的由剖分三角形组成的区域,用于支持地图综合中邻近多边形的搜索、多边形弯曲部位的识别、冲突关系探测、多边形合并等操作。并对基于骨架线的图结构建立、分枝宽度计算等几何问题进行了详细讨论,指出了其在诸如双线河中轴化、街道中轴线网络模型建立、多边形合并中的邻近关系分析、面状目标注记自动定位领域的支持作用。     

An efficient solution for building polygonal objects

Polygonal object is a fundamental type of geometric data in vector GIS. The key step cleaning topological relationship after data collection of polygonal layer is to build polygonal objects from digital arcs. The raw digital arcs may intersect with each other. The algorithm for building polygonal objects after the raw arcs have been split at all intersections is presented. The build-up of polygonal objects in this paper is designed to be implemented by two steps. The first step is to extract all the polygons needed for build-up of polygonal objects from arcs. The second step is to organize polygonal objects from these polygons. For the first step, a tracing algorithm is proposed. The algorithm merely extracts the polygons needed for the build-up of polygonal objects, which is a subset of all the possible polygons that can be induced from the arcs. For the second step, an algorithm based on a specially designed order of polygons is advanced. All the topological relationships among the polygons are shown in a single scan. Experiments show that the two algorithms together offer a robust and efficient solution for building polygonal objects from intersected arcs.     

An efficient solution for building polygonal objects

Polygonal object is a fundamental type of geometric data in vector GIS. The key step of cleaning topological relationship after data collection of polygonal layer is to build polygonal objects from digital arcs. The raw digital arcs may intersect with each other. The algorithm for building polygonal objects after the raw arcs have been split at all intersections is presented. The build-up of polygonal objects in this paper is designed to be implemented by two steps. The first step is to extract all the polygons needed for build-up of polygonal objects from arcs. The second step is to organize polygonal objects from these polygons. For the first step, a tracing algorithm is proposed. The algorithm merely extracts the polygons needed for the build-up of polygonal objects, which is a subset of all the possible polygons that can be induced from the arcs. For the second step, an algorithm based on a specially designed order of polygons is advanced. All the topological relationships among the polygons are sho     

基于ArcGIS9.0土地利用数据中的图斑合并方法

运用ArcGIS软件和内置VBA二次开发技术,提出一种土地利用数据中图斑的合并方法。首先按地类等级合并属于同一行政区且相邻的图斑,然后根据本文提出的合并规则合并小图斑到相邻图斑中。实验表明:利用此方法合并图斑,符合数据制作规范、不易出错、速度快等优点。     

邻接凸多边形方向关系计算及其推理

在对拓拟邻接凸多边形方向关系计算特殊性分析的基础上，提出了一个邻接凸多边形方向关系计算法和二维空间方向关系推理方法。与Voronoi图所表达的邻近拓扑关系相结合，该算法可以用于计算离散二维空间目标之间的方向关系。     

Assessing polygon edge integrity

Geographic entities are represented in vector GIS as geometric objects with definite position and sharp, zero or one-dimensional, boundaries. This representation ignores, and may in fact misrepresent, the underlying properties of many geographic features. This paper considers a binary classification scheme for ill-defined edges recently suggested by Couclelis (1996). Three initial perspectives of edge indeterminacy are considered for woodland polygons, namely functionally defined, using Hamming distances of the indeterminacy properties, and using the weighted trace of the connectivity matrix that defines the simplicial complex created from the indeterminacy relationships among the woodland polygons and their adjacent polygons. Finally, an analysis of the relationships created by the binary coding is carried out by examining more closely the connectivity structure of the simplicial complexes. The usefulness of the approach is discussed in the context of woodland habitat. Received: 20 March 1999/Accepted: 7 September 1999     

Polygonal Clustering Analysis Using Multilevel Graph‐Partition

Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non‐overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and multilevel graph partition. Based on spatial cognition on spatial similarity of polygons, four new similarity criteria (i.e. the distance, connectivity, size and shape) are developed to measure the similarity between polygons, and used to visually distinguish those polygons belonging to the same clusters from those to different clusters. The clustering method with multilevel graph‐partition first coarsens the graph of polygons at multiple levels, using the four defined similarities to find clusters with maximum similarity among polygons in the same clusters, then refines the obtained clusters by keeping minimum similarity between different clusters. The presented method is a general algorithm for discovering clustering patterns of polygons and can satisfy various demands by changing the weights of distance, connectivity, size and shape in spatial similarity. The presented method is tested by clustering residential areas and buildings, and the results demonstrate its usefulness and universality.