Analysis of the relations among spatial tessellations

A spatial tessellation is a set of regions that are collectively exhaustive and mutually exclusive except for the boundaries. In geographical analysis, it may represent such administrative units as census tracts, postal zones, and electoral or school districts. Spatial tessellations of a certain area are often closely related to each other. Areas of local communities are related to school districts, market areas of retail stores, and administrative units. Postal zones and census tracts are determined by collecting or dividing administrative units. Analysis of such relations among tessellations often reveals their underlying spatial phenomena. To this end, this paper proposes a new exploratory method for analyzing the relations among spatial tessellations. It aims to detect spatial patterns, especially those with a hierarchical structure, and to provide a tessellation classification scheme. Topological relations and similarity measures are introduced to evaluate the relations between tessellation pair. For more tessellations, tree representations are proposed. These not only visualize relations, but also provide a means of classifying tessellations. The method is applied to the analysis of two sets of spatial tessellations: one with five hypothetical tessellations, and another with 34 candidate plans for the new Doshusei administrative system in Japan. The application reveals the properties of the method and quantitative measures used in analysis.     

A progressive line simplification algorithm

An application of line simplification considering spatial knowledge is described. A method for identifying potential conflict regions, in order to avoid the self-intersection of generalized, lines, is also discussed. Further-more, a new progressive line simplification algorithm is presented. From the view point of spatial cognition, a spatial hierarchical structure is proposed, and its application to construction of spatial knowledge related to a line is explained.     

Spatial Object Aggregation Based on Data Structure,Local Triangulation and Hierarchical Analyzing Method

This paper focuses on the methods and process of spatial aggregation based on semantic and geometric characteristics of spatial objects and relations among the objects with the help of spatial data structure (Formal Data Structure),the Local Constrained Delaunay Triangulations and semantic hierarchy.The adjacent relation among connected objects and unconnected objects has been studied through constrained triangle as elementary processing unit in aggregation operation.The hierarchical semantic analytical matrix is given for analyzing the similarity between objects types and between objects.Several different cases of aggregation have been presented in this paper.     

Spatial object aggregation based on data structure,local triangulation and hierarchical analyzing method

This paper focuses on the methods and process of spatial aggregation based on semantic and geometric characteristics of spatial objects and relations among the objects with the help of spatial data structure (Formal Data Structure), the Local Constrained Delaunay Triangulations and semantic hierarchy. The adjacent relation among connected objects and unconnected objects has been studied through constrained triangle as elementary processing unit in aggregation operation. The hierarchical semantic analytical matrix is given for analyzing the similarity between objects types and between objects. Several different cases of aggregation have been presented in this paper.     

A progressive line simplification algorithm

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Exploiting Illusory Grid Lines for Object-Location Memory Performance in Urban Topographic Maps

In order to be successful in spatial orientation tasks, people need to recall locations and configurations of spatial objects from their memory. This understanding of geographic space often arises from experience with cartographic media representing topographic and topological information by graphic symbols. Learning spatial information from graphic media is influenced by different perception-based grouping effects distorting the accuracy of spatial object-positions and their relations. Such geometric inaccuracies can be softened by adding a grid layer, which regionalizes the map and can be used as an additional orientation pattern. This grid layer usually consists of solid lines and overlays semantic information. The present paper reports the results of two empirical studies on object-location memory (OLM) performance. In these studies, the amount of visual detail of the grid layer was reduced. By positioning the grid layer below specific urban topographic objects (study 1), the grid pattern was graphically interrupted. These interrupted grid lines were completed by cognitive completion mechanisms (illusory grid lines) described in the Gestalt principles of closure and continuation. The second experiment examined the maximum grid line gap that is closed by cognitive line completion and keeps an advantage for OLM (study 2).     

基于拓扑约束的方向关系定性描述模型

针对原有方向关系矩阵模型对于参考目标MBR区域的方向描述缺陷问题,本文将拓扑约束引入方向关系定性描述,构建基于拓扑参考的方向关系定性描述模型,实现了MBR区域方向关系的有效表达。新模型首先将参考目标的MBR区域划分为不同的拓扑区域,提出方向关系拓扑参考定义;基于拓扑参考,分别对不同拓扑区域定义相应的方向关系矩阵;最后,根据参考目标与源目标间的不同拓扑关系,提出不同情况下方向关系分层定性描述策略。实验结果表明,新模型充分反映了拓扑关系对方向关系描述的约束关系,能有效提高方向关系表达的准确性和精确性。     

基于超图的数据结构及其在GIS中的应用

空间数据是空间分析的基础,随着海量空间数据的增大,其之间关系的日益复杂,空间数据的组织就显得愈发重要。基于超图的数据结构以超图和集合理论为基础,可以表达空间数据之间的层次关系和横向关系,使GIS空间数据结构在空间分析中的完备性和适应性问题得到解决。本论文在对超图的层次数据结构和非层次结构进行详细研究的基础上,初步探讨利用面向对象的关系数据库系统实现该模型,并以海洋数据为例,利用HBDS组织海洋原始数据、等深线数据、特征线数据、地貌数据等。论文的最后对HBDS在GIS中的应用做简单举例,并期将HBDS和OLAP相结合的方法进行数据挖掘。     

带有区域分裂自适应细化过程的SAR海冰图像分割

受海冰自身特性、成像系统特性和环境因素的影响,合成孔径雷达SAR海冰图像具有非平稳、尺度依赖的空间结构,现有的单马尔可夫随机场MRF模型分割方法只能较好地适应非平稳性,对海冰场景的多尺度结构考虑仍然是全局的。为此,本文提出了一种区域分裂过程与二叉树分层结构自适应更新相结合的单MRF图像分割方法。首先利用单MRF模型的全局迭代权值完成初始区域合并,同时以二叉树形式保护合并过程的记录。所设计的分层合并算法可保证二叉树结构的节点数与场景中的对象尺度具有正相关性。随后的细化分裂并不产生新的区域,只是返回到初始配置。依据场景中不同区域对象的尺度,自适应地调整空间语境模型中的尺度权值,实现区域更新。实验表明,该方法有效提高了带有多尺度结构SAR海冰场景的分割精度。     

基于邻近图的点群层次聚类方法的研究

空间聚类是点状空间目标群在地图综合中必须解决的问题。分析点群的几种常用邻近图的特征及其层次关系,并基于原始的点集合生成的DT构建相应的GG,UG,MST和NNG,然后在所选择的密度适应性约束、距离适应性约束和偏差适应性约束这三种条件下,利用所生成的邻近图进行了点群的层次聚类。研究并改进现有的点状空间目标群的无监督层次聚类方法,并通过实例验证该算法的可行性。     

基于V9I的空间关系映射与操作

利用V9I模型中目标的边界、内部和Voronoi区域均可量测及易于操作的特点 ,研究建立底层数据结构与空间关系语义层之间的V9I映射机制 ,在Voronoi的动态栅格生成算法的基础上 ,构建空间关系的基本操作。最后 ,以VC 为开发工具并采用面向对象的技术 ,设计了基于V9I的空间关系操作工具原型VTKit ,并给出部分操作实例     

全球多分辨率六边形网格剖分及地址编码规则

首先介绍全球网格系统的应用需求,讨论采用正多面体剖分的基本要素。在此基础上,提出一种新型的便于存储,具有多分辨率层次索引、管理能力的六边形剖分全球网格系统的设计思路、产生步骤和生成算法。分析现有六边形索引机制的缺陷,给出一种新的球面六边形网格层次结构,在该结构的基础上设计网格的层次化编码方式,成功建立网格的编码空间,详细探讨经纬度坐标与网格地址编码的转换问题,给出并实现基于递归层次离散网格坐标系统的互换算法,严格保证计算的准确性和可靠性。该结构能够无缝地表达和处理不同分辨率的全球网格空间数据。     

Mining knowledge from result comparison between spatial clustering themes

This paper introduces some definitions and defines a set of calculating indexes to facilitate the research, and then presents an algorithm to complete the spatial clustering result comparison between different clustering themes. The research shows that some valuable spatial correlation patterns can be further found from the clustering result comparison with multi-themes, based on traditional spatial clustering as the first step. Those patterns can tell us what relations those themes have, and thus will help us have a deeper understanding of the studied spatial entities. An example is also given to demonstrate the principle and process of the method.     

一种顾及空间关系的遥感影像阴影检测算法

以城市区域内高大建筑阴影为研究对象,针对现有的阴影检测算法在复杂地物环境下检测精度和可靠性不高的问题,提出了一种结合颜色空间特征和空间关系的遥感影像阴影检测方法。首先,采用SLIC超像素算法对影像进行分割;然后基于Lab和HSI颜色空间构建初步检测条件,将阴影划分为阴影主体区域和待检测区域;最后,借助Canny边缘检测信息合并待判别区域内的超像素块,并利用阴影区域与造成干扰区域间的空间位置关系构建的检测条件进行判别。实验结果表明,该方法可以有效提高复杂地物环境下遥感影像阴影的检测精度和算法可靠性。     

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.     

关于线状地理特征空间关系的自然语言描述的形式化表达

目前的地理信息系统都使用定量的方法存储和查询空间信息,不能反映人们的定性思维方式,因此它表达地理空间信息的能力受到限制。在关于空间关系的自然语言描述的调查的基础上,本文定义了一组能反映线状物体空间关系的度量指标,结合反映拓扑关系的定量指标,使用决策树的数据挖掘算法,对调查结果进行分析,形式化了描述空间关系的自然语言词汇,建立了模糊的自然语言表达和图形的几何特征之间的联系。自然语言形式化后得到的规则可用于空间关系的自然语言查询系统。     

A spatial contextual immune genetic algorithm to building cartographic displacement

In map generalization, displacement is the most frequently used operator to reduce the proximity conflicts caused by reducing scales or other generalization operations. Building displacement can be formalized as a combinatorial optimization problem, and a heuristic or intelligent search algorithm can be borrowed to obtain the solution. In this way, we can explicitly resolve minimum distance conflicts and control positional accuracy during the displacement. However, maintaining spatial relations and patterns of buildings can be challenging. To address spatial conflicts as well as preserve the significant spatial relations and patterns of buildings, we propose a new spatial contextual displacement algorithm based on an immune genetic algorithm. To preserve important spatial relations and global patterns of map objects and avoid topology errors, displacement safety zones are constructed by overlapping the Voronoi tessellation and buffer areas of the buildings. Additionally, a strategy to shift the buildings in a building group synchronously is used to maintain local building patterns. To demonstrate the effectiveness of our algorithm, two data sets with different building densities were tested. The results indicate that the new algorithm has obvious advantages in preventing topology errors and preserving spatial relations and patterns.     

Modelling relational contexts in GEOBIA framework for improving urban land-cover mapping

In human cognition, both visual features (i.e., spectrum, geometry and texture) and relational contexts (i.e. spatial relations) are used to interpret very-high-resolution (VHR) images. However, most existing classification methods only consider visual features, thus classification performances are susceptible to the confusion of visual features and the complexity of geographic objects in VHR images. On the contrary, relational contexts between geographic objects are some kinds of spatial knowledge, thus they can help to correct initial classification errors in a classification post-processing. This study presents the models for formalizing relational contexts, including relative relations (like alongness, betweeness, among, and surrounding), direction relation (azimuth) and their combination. The formalized relational contexts were further used to define locally contextual regions to identify those objects that should be reclassified in a post-classification process and to improve the results of an initial classification. The experimental results demonstrate that the relational contexts can significantly improve the accuracies of buildings, water, trees, roads, other surfaces and shadows. The relational contexts as well as their combinations can be regarded as a contribution to post-processing classification techniques in GEOBIA framework, and help to recognize image objects that cannot be distinguished in an initial classification.     

Homological relations: A methodology for the certification of irregular tessellations

In GIS, spatial analysis is based on the use of spatial operations such as testing the spatial relations between features. Often, such tests are invalidated by errors in datasets. It is a very common experience that two bordering regions which should obey the topological relation “meet” fall instead in the “overlap” category. The situation is exacerbated when applying topological operators to regions that come from different datasets, where resolution and error sources are different. Despite the problem being quite common, up to now no standard approach has been defined to deal with spatial relations affected by errors of various origins. Referring to topological relations, we define a model to extend the eight Egenhofer relations between two simple regions: we call them homological relations (H‐relations). We discuss how exact topological relations can be extracted from observed relations and discuss the case of irregular tessellations, where errors have the most impact on vector data. In the proposed case study within the domain of geographic crowdsourced data, we propose algorithms for identifying homological regions and obtaining a corrected tessellation. This methodology can be considered as a step for quality control and the certification of irregular tessellations.     

An integrated data model in three dimensional GIS

The current GIS can only deal with 2-D or 2.5-D information on the earth surface. A new 3-D data structure and data model need to be designed for the 3-D GIS. This paper analyzes diverse 3-D spatial phenomena from mine to geology and their complicated relations, and proposes several new kinds of spatial objects including cross-section, column body and digital surface model to represent some special spatial phenomena like tunnels and irregular surfaces of an ore body. An integrated data structure including vector, raster and object-oriented data models is used to represent various 3-D spatial objects and their relations. The integrated data structure and object-oriented data model can be used as bases to design and realize a 3-D geographic information system.