空间拓扑关系定量描述与抽象方法研究

具有复杂形状的空间区域之间拓扑关系的表示与抽象是空间信息多尺度表达和可视化中必须解决的问题.本文讨论区域之间拓扑关系边界交集成分的定性描述方法,并在此基础上提出度量化边界交集成分描述方法,运用这种详细的描述方法,探讨空间区域之间拓扑关系抽象的规律,可为地图自动综合中空间关系的维护提供有关的理论基础.     

GIS图上地理区域空间不确定性的分析

区域是GIS空间分析的基本单元。它是环境科学、土壤科学等领域中广泛存在的一类地理实体。这类实体抽象概括为GIS中空间目标时不可避免地带有模糊性。利用模糊集这个强有力的数学工具，首先基于场模型提出了表达GIS中模糊区域目标的方法，分析了其空间不确定性。继而，建立了模糊区域的形态描述模式，即模糊区域边界、内部和外部，并指出了Cohn给出的定义中的不足。在此基础上，对Egenhofer提出的九元组进行了扩展，构建了适合于描述确定和模糊区域目标间拓扑空间关系的模型。     

空间点面拓扑关系不确定性的模糊描述研究

阐述空间数据和空间关系不确定性的概念、来源及建模,并研究利用模糊理论进行空间区域的划分,以及利用模糊九交模型对空间对象拓扑关系进行描述分析.     

离散空间的拓扑关系模型

在分析现有拓扑关系表达模型对离散空间目标间拓扑关系表达不足的基础上,从两个方面对现有RCC5模型进行了改进研究:基于模糊集合的分解定理,通过利用空间实体的k阶邻域构造了空间实体的模糊区域,实现了离散空间的连续化;以部分关系作为基本操作算子构建了基于三元组(P(X,Y),P(X,┐Y),P(Y,X))的拓扑关系表达模型,实现了离散空间实体间拓扑关系的表达,集成了空间拓扑和距离关系的定性表达和推理.     

宽边界区域拓扑关系量化分析与抽象

对Cohn等人提出的卵一黄模型所确定的宽边界区域的46种拓扑关系及其聚类方法进行了分析，提出了一种定量化方法对这些拓扑关系进行描述，并对这46种拓扑关系进行了抽象。     

在图形简化中面状目标间拓扑关系渐进式转换的研究

拓扑关系是空间关系中最为重要的关系,在空间抽象中,拓扑关系会发生变化,如何控制这种变化?如何在更抽象的层次上维护空间拓扑关系的一致性,就是一个非常重要的问题。本文以拓扑关系成分抽象的转换方法为基础,研究了面之间拓扑关系的抽象规则,并详细探讨了这些拓扑关系的渐进式转换方法,绘制了相应的面之间基本拓扑关系渐进式转换图。     

多尺度表达中空间拓扑关系等价性评价模型

本文主要讨论不同尺度下地理目标之间拓扑关系的等价性问题。根据人类认知习惯,得出从大比例尺地理空间中拓扑关系抽象为小比例尺空间中拓扑关系应当遵循的基本规则,并利用模糊函数将这些规则定量化;在此基础上,得到评价地理空间场景中多个空间目标之间拓扑关系在不同尺度下是否等价的综合评价模型。该模型可以用于检验和维护空间数据质量、为地图自动综合提供空间关系处理和评价方法的参考。     

一种三维空间两模糊对象拓扑关系的描述方法

拓扑定义了空间要素如何共享空间的规则,它是一个规则和关系的集合,可以用来控制要素间的地理关系,同时保持它们各自的集合完整性,是重建地理实物的有效手段。针对三维空间中拓扑关系的研究现状,本文采用了粗糙集理论,对三维空间中模糊对象间的拓扑关系进行了研究,得出了两个模糊对象间的拓扑关系模型。为进一步研究分析空间拓扑关系提供了理论基础。     

图形简化过程中空间拓扑关系抽象的规律

以局域基本拓扑关系的抽象为基础,着重阐述了线与线之间局域拓扑关系抽象的规则,并研究了相邻两类局域基本空间拓扑关系合并的规律,提出了在空间抽象过程中渐进式删除局域拓扑关系的方法.     

栅格索引的结点匹配算法

拓扑空间关系是地理信息系统(GIS)进行空间分析的基础,也是GIS区别于传统制图系统的主要因素.对于拓扑空间关系自动构建技术的研究由来已久,但其速度及效率一直是瓶颈.在分析拓扑空间关系自动构建算法的基础上,指出结点匹配算法是拓扑空闻关系构建的瓶颈问题,并提出了一种基于栅格索引的结点匹配算法.实践表明:新算法对于拓扑空间关系自动构建的效率有大幅度的提高.     

Developing local measures of spatial association for categorical data

This paper describes a procedure for extending local statistics to categorical spatial data. The approach is based on the notion that there are two fundamental characteristics of categorical spatial data; composition and configuration. Further, it is argued that, when considered locally, the latter should be measured conditionally with respect to the former. These ideas are developed for binary, gridded data. Local composition is measured by counting the numbers of cells of a particular type, while local configuration is measured by join counts. The approach is illustrated using a small, empirical data set and an ad hoc procedure is developed to deal with the impact of global spatial autocorrelation on the local statistics.The author gratefully acknowledges financial support from the GEOIDE Network of Centres of Excellence (ENV #4) and the helpful comments of three anonymous reviewers.     

基于GIS的种植业结构调整及空间匹配技术的实现——以河北省正定县为例

本文依据河北正定县原始农业数据,综合考虑各种因素建立模型,对各种作物种植结构进行调整和效益分析,并利用MAPOBJETS控件和VB使种植前后的面积值显示在相应的地块上,实现了决策结果与空间位置的空间匹配。     

SPATIAL OBJECTS AND SPATIAL RELATIONSHIPS

Spatial object and spatial relationship are two basic concepts of GIS.Spatial object is the digital representation of geographical entity or phenomenon,which forms the basis for data management and analysis;spatial relationship is theconnexion between spatial objects when geometric properties are considered.Thecommonly used classification of spatial objects as points,lines and areas is mathe-matically strict,and suitable for data management,but a bit too generalized forrepresenting real entities and extracting spatial relationships.A good classificationmodel should not only be good for representing real entities,but also good for re-vealing spatial relationships,therefore good for formalizing spatial analyses.     

一种顾及障碍约束的空间聚类方法

为了使得空间聚类分析更加适应实际情况,发展了一种同时顾及空间障碍约束与空间位置邻近的空间聚类方法。该方法采用Delaunay三角网描述实体间的邻近关系,并且不依赖用户指定参数。实验验证了本方法的有效性与优越性。     

Modeling spatial dependence in high spatial resolution hyperspectral data sets

 As either the spatial resolution or the spatial scale for a geographic landscape increases, both latent spatial dependence and spatial heterogeneity also will tend to increase. In addition, the amount of georeferenced data that results becomes massively large. These features of high spatial resolution hyperspectral data present several impediments to conducting a spatial statistical analysis of such data. Foremost is the requirement of popular spatial autoregressive models to compute eigenvalues for a row-standardized geographic weights matrix that depicts the geographic configuration of an image's pixels. A second drawback arises from a need to account for increased spatial heterogeneity. And a third concern stems from the usefulness of marrying geostatistical and spatial autoregressive models in order to employ their combined power in a spatial analysis. Research reported in this paper addresses all three of these topics, proposing successful ways to prevent them from hindering a spatial statistical analysis. For illustrative purposes, the proposed techniques are employed in a spatial analysis of a high spatial resolution hyperspectral image collected during research on riparian habitats in the Yellowstone ecosystem. Received: 25 February 2001 / Accepted: 2 August 2001     

Global and local spatial autocorrelation in bounded regular tessellations

This paper systematically investigates spatially autocorrelated patterns and the behaviour of their associated test statistic Moran's I in three bounded regular tessellations. These regular tessellations consist of triangles, squares, and hexagons, each of increasing size (n=64; 256; 1024). These tesselations can be downloaded at http://geo-www.sbs.ohio-state.edu/faculty/tiefelsdorf/regspastruc/ in several GIS formats. The selection of squares is particularly motivated by their use in raster based GIS and remote sensing. In contrast, because of topological correspondences, the hexagons serve as excellent proxy tessellations for empirical maps in vector based GIS. For all three tessellations, the distributional characteristics and the feasibility of the normal approximation are examined for global Moran's I, Moran's I ^(k) associated with higher order spatial lags, and local Moran's I _i. A set of eigenvectors can be generated for each tessellation and their spatial patterns can be mapped. These eigenvectors can be used as proxy variables to overcome spatial autocorrelation in regression models. The particularities and similarities in the spatial patterns of these eigenvectors are discussed. The results indicate that [i] the normal approximation for Moran's I is not always feasible; [ii] the three tessellations induce different distributional characteristics of Moran's I, and [iii] different spatial patterns of eigenvectors are associated with the three tessellations. Received: 2 July 1999 / Accepted: 9 November 1999     

空间数据仓库的概略设计

基于数据仓库和 GIS技术 ,设计出了空间数据仓库的体系结构 ,在该结构中重点描述了空间数据仓库与测绘数据库和应用系统的区别与联系。在此基础上提出了空间数据仓库应有多数据源的抽取、数据仓库管理、元数据管理、数字产品融合、数字产品网上服务、多维分析和二次开发这七大部分组成 ,并对这七大组成部分的内容进行了详细描述     

空间数据仓库研究综述

论述了空间数据仓库的定义、基本特征；着重叙述了目前国内外空间数据仓库在学术研究、产品研制和实际应用方面的发展现状；设计出了空间数据仓库的体系结构，说明了空间数据仓库和应用系统的区别与联系；最后得出结论，为支持我国空间数据基础设施建设，研制空间数据仓库十分必要。     

Spatial analysis in epidemiology: Nascent science or a failure of GIS?

This paper summarizes contributions of GIS in epidemiology, and identifies needs required to support spatial epidemiology as science. The objective of spatial epidemiology is to identify disease causes and correlates by relating spatial disease patterns to geographic variation in health risks. GIS supports disease mapping, location analysis, the characterization of populations, and spatial statistics and modeling. Although laudable, these accomplishments are not sufficient to fully identify disease causes and correlates. One reason is the failure of present-day GIS to provide tools appropriate for epidemiology. Two needs are most pressing. First, we must reject the static view: meaningful inference about the causes of disease is impossible without both spatial and temporal information. Second, we need models that translate space-time data on health outcomes and putative exposures into epidemiologically meaningful measures. The first need will be met by the design and implementation of space-time information systems for epidemiology; the second by process-based disease models.