尺度空间地图多重表达的面向对象数据模型研究
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摘要
空间数据多尺度表达是GIS领域研究的热点问题之一,它在多尺度空间分析、矢量数据渐进式传输、多源多尺度数据集成以及自适应动态可视化等领域都有贡献作用。目前而言,多尺度数据库的创建主要存在静态多版本和动态综合派生两种策略。前者存在大量数据冗余,数据一致性差,更新困难；后者受地图综合这一国际难题的影响,针对不同的要素类型发展极不平衡,实用性差。针对这一现状,本论文从数据组织的角度,运用面向对象的思想方法实现了对版本和操作的有机集成,创新性地提出了一个面向对象的“生命期模型”。具体研究内容包括：
1.总结了国内外多尺度数据模型的研究进展。着重分析了几个代表性的研究成果：GEODYSSEY、地图立方体模型(Map Cube Model)、抽象胞腔复形(Abstract Cell Complexes)、层次地图空间(Stratified Map Spaces)、MADS模型及VUEL模型,等等。这些模型大致可以分为两类,一类是基于多版本的层次模型(如地图立方体模型、层次地图空间模型等),一类是基于地图综合的动态派生模型(如GEODYSSEY、MADS、VUEL等)。综合来看,这些模型要么重版本,要么重操作,但都没有提供对版本和操作的集成机制。事实上,版本和操作可以看作是空间数据的两个基本特征,一个是静态属性,一个是动态操作,在面向对象的框架下二者是可以集成的。当前的多尺度模型往往偏重概念层次或者逻辑层次或者物理层次等某一具体层次的研究,而没有系统地将这三个不同的层次有机地串接起来,形成一个完备的体系。因此,未来多尺度数据模型的研究应该注重对版本和操作的有机集成,注重概念分析、逻辑设计和物理实现三个层次的串接。
2.系统阐述了GIS中尺度的基本概念,并从认知和表达的角度分析了GIS数据的多尺度特性。尺度是空间数据的重要特征,也是地理信息科学中最模糊、最多意、最难分辨的术语。论文从尺度概念的内涵、外延和类别三个不同的角度阐述了尺度的基本特征；从认知的角度分析了空间数据的尺度效应、尺度依赖性和尺度不变性,这些性质构成了空间数据尺度变换和多尺度表达的理论基础；从表达的角度分析了空间数据的多尺度特征,多尺度表达既是层次化空间认知和尺度变换的结果,又是辅助从粗到细、从整体到局部空间认知的有力工具。
3.从面对对象的角度提出了一个集空间数据表达和尺度变换操作于一体的多尺度数据模型“生命期模型”。该模型基于面向对象的思想,将空间数据的静态表达和动态操作分别建模为对象的属性和方法,通过属性和方法的组合运算,可以动态地导出任意尺度上的数据表达,实现增量式更新传播,避免了传统多版本数据模型所固有的表达不一致问题及更新困难问题。
4.从数据操作的角度总结出了一套完备的矢量数据尺度变换模式,包括地图综合模式、变化累计(LOD)模式、形状内插(Morphing)模式和等价尺度变换模式。LOD模式将实体的表达剖分为一系列结构简单的几何细节单元,每个细节单元对应一定的尺度层次,实体在任意尺度上的表达表现为一系列细节的累积。这种模式改变了传统的基于函数变换的尺度变换模式,具有小数据量、大跨度、多算子集成、操作简单等特性,为GIS数据尺度变换提供了基于数据组织的新视角。Morphing模式以两端尺度控制代替一端尺度控制,以敏感的内插函数实现了较大尺度空间内空间数据的连续光滑变换。在面向对象的框架下,通过继承和封装等技术方法实现了对四种模式的集成,提高了多尺度模型数据操作的效率,增强了模型的动态性和灵活性。
5.从数据组织的角度,提出了基于图结构的多尺度数据组织方式“演化链图”。演化链图以结点表示空间数据的尺度状态,以链边表示空间数据的尺度变换操作。论文基于表达的特征总结了四种结点类型：实结点、虚结点、累积结点和复合结点；基于尺度变换的特征总结了四种链边类型：综合链边、LOD链边、Morphing链边和等价变换链边。不同类型结点和链边的组合可以表达不同的尺度变换模式,一系列结点和链边的有序组合直观地反映了空间数据在其整个表达尺度空间的表达变化过程,从而实现了对大跨度尺度范围内空间数据多重表达过程的有效描述,将传统的面向尺度点的静态表达拓展为面向尺度区间的动态表达。
6.基于Domap软件平台,对多尺度数据模型和尺度变换算法进行了大量程序开发实验,验证了模型算法的可行性。
The issue of multi-scale representations of spatial data is a hot research topic in GIS. Multi-scale representation database (MRDB) contributes to many GIS related fields, such as multi-scale spatial analysis, vector data progressive transmission over internet, data integration, and self-adaptive dynamic visualization and so on. At present, there are two strategies to construct a MRDB:static multiple versions and dynamic deriving. The former has some drawbacks, such as data redundancy, inconsistency and outdated; the latter seriously depends on the development of map generalization, which is a difficult problem. To remedy these shortages we present an object-oriented multi-scale spatial data model "lifespan model" from the viewpoint of data organization, which integrates static versions and dynamic operations together. The followings are the specific research contents of this study.
1. The thesis gives a review on the research progress in multi-scale data model. Several representative research works are analyzed. They are GEODYSSEY, Map Cube Model, Stratified Map Spaces, Abstract Cell Complexes, MADS and VUEL. All of these models can be categorized into two classes:the hierarchy model (eg:the Map Cube Model and the Stratified Map Spaces Model) and the generalization deriving model (eg:GEODYSSEY、MADS. VUEL). Generally speaking, all these models either focus on the hierarchy or on the generalization. No one considers both of the hierarchy and generalization together. In fact, both of the representations and the scale transformations can be integrated under the framework of object-oriented. Meanwhile all of these models usually focus on the level of concept model or logical model or physical model. No one relates all of the three models and integrates them to form a system. Hence we think the future direction of multi-scale spatial data model is to integrate representations and scale transformations together, and join concept model, logical model and physical model.
2. Scale is an important characteristic of spatial data. In this study, we discuss the concept of scale from three aspects:connotation, extension and category. From viewpoint of the cognition, we summarize the scale characteristics of spatial data including:scale effect, scale dependence, and scale invariance, which are the theory basis of scale transformation and multi-scale representation. From the viewpoint of representation, the multi-scale representations are not only the results of hierarchical spatial cognition and scale transformation, but also the tools to assist spatial cognition from coarse to fine.
3. From the viewpoint of object-oriented (00), we present a multi-scale data model "Lifespan Data Model", which integrates the representation and scale transformation together. According to the idea of 00, we model the representation as the attribute of object, and the scale transformation as the method of object respectively. Based on this model, we can dynamically derive any representation from the scaling of basic representation. Then the polymorphism of spatial data can be better represented and the problem of inconsistency and update can be avoided.
4. From the viewpoint of data manipulation, we design a suit of scale transformation modes, including map generalization transformation, LOD transformation, morphing transformation and equal transformation. The basic idea is LOD transformation is to partition the full representation into series of details with different scale hierarchies, and then the representation at any scale can be accumulated with the details of corresponding levels. This mode has some favorable characteristics, such as little data volume, wide scale span, operator integration and so on. The morphing transformation is based on the idea of shape interpolation. It can be used to drive smooth and consecutive representations. Within the framework of 00, all of these modes are integrated by the technologies of aggregation and inheritance. Consequently, the efficiency and flexibility of the lifespan model are greatly improved.
5. From the viewpoint of data organization, we present a graph based multi-scale data structure "evolution graph". For the evolution graph, the vertex denotes one or sets of representation, and the edge denotes the scale transformation operation relating two consecutive representations. Based on the properties of representations, we summarize four types of vertex:solid vertex, fake vertex, detail vertex and complex vertex. Based on the properties of scale transformation we summarize four types of edges:generalization edge, LOD edge, morphing edge and equal edge. The graph with different vertexes and edges can denote different scale transformation modes. A series of ordered vertexes and edges directly reflect the complex scale evolution process of spatial data over a large scale space. Finally the evolution graph extends the traditional scale point oriented static representations to the scale space oriented dynamic representations.
6. Based on the Domap software platform, we develop a lot of experiments to verify the feasibility of the model and algorithms.
1.总结了国内外多尺度数据模型的研究进展。着重分析了几个代表性的研究成果：GEODYSSEY、地图立方体模型(Map Cube Model)、抽象胞腔复形(Abstract Cell Complexes)、层次地图空间(Stratified Map Spaces)、MADS模型及VUEL模型,等等。这些模型大致可以分为两类,一类是基于多版本的层次模型(如地图立方体模型、层次地图空间模型等),一类是基于地图综合的动态派生模型(如GEODYSSEY、MADS、VUEL等)。综合来看,这些模型要么重版本,要么重操作,但都没有提供对版本和操作的集成机制。事实上,版本和操作可以看作是空间数据的两个基本特征,一个是静态属性,一个是动态操作,在面向对象的框架下二者是可以集成的。当前的多尺度模型往往偏重概念层次或者逻辑层次或者物理层次等某一具体层次的研究,而没有系统地将这三个不同的层次有机地串接起来,形成一个完备的体系。因此,未来多尺度数据模型的研究应该注重对版本和操作的有机集成,注重概念分析、逻辑设计和物理实现三个层次的串接。
2.系统阐述了GIS中尺度的基本概念,并从认知和表达的角度分析了GIS数据的多尺度特性。尺度是空间数据的重要特征,也是地理信息科学中最模糊、最多意、最难分辨的术语。论文从尺度概念的内涵、外延和类别三个不同的角度阐述了尺度的基本特征；从认知的角度分析了空间数据的尺度效应、尺度依赖性和尺度不变性,这些性质构成了空间数据尺度变换和多尺度表达的理论基础；从表达的角度分析了空间数据的多尺度特征,多尺度表达既是层次化空间认知和尺度变换的结果,又是辅助从粗到细、从整体到局部空间认知的有力工具。
3.从面对对象的角度提出了一个集空间数据表达和尺度变换操作于一体的多尺度数据模型“生命期模型”。该模型基于面向对象的思想,将空间数据的静态表达和动态操作分别建模为对象的属性和方法,通过属性和方法的组合运算,可以动态地导出任意尺度上的数据表达,实现增量式更新传播,避免了传统多版本数据模型所固有的表达不一致问题及更新困难问题。
4.从数据操作的角度总结出了一套完备的矢量数据尺度变换模式,包括地图综合模式、变化累计(LOD)模式、形状内插(Morphing)模式和等价尺度变换模式。LOD模式将实体的表达剖分为一系列结构简单的几何细节单元,每个细节单元对应一定的尺度层次,实体在任意尺度上的表达表现为一系列细节的累积。这种模式改变了传统的基于函数变换的尺度变换模式,具有小数据量、大跨度、多算子集成、操作简单等特性,为GIS数据尺度变换提供了基于数据组织的新视角。Morphing模式以两端尺度控制代替一端尺度控制,以敏感的内插函数实现了较大尺度空间内空间数据的连续光滑变换。在面向对象的框架下,通过继承和封装等技术方法实现了对四种模式的集成,提高了多尺度模型数据操作的效率,增强了模型的动态性和灵活性。
5.从数据组织的角度,提出了基于图结构的多尺度数据组织方式“演化链图”。演化链图以结点表示空间数据的尺度状态,以链边表示空间数据的尺度变换操作。论文基于表达的特征总结了四种结点类型：实结点、虚结点、累积结点和复合结点；基于尺度变换的特征总结了四种链边类型：综合链边、LOD链边、Morphing链边和等价变换链边。不同类型结点和链边的组合可以表达不同的尺度变换模式,一系列结点和链边的有序组合直观地反映了空间数据在其整个表达尺度空间的表达变化过程,从而实现了对大跨度尺度范围内空间数据多重表达过程的有效描述,将传统的面向尺度点的静态表达拓展为面向尺度区间的动态表达。
6.基于Domap软件平台,对多尺度数据模型和尺度变换算法进行了大量程序开发实验,验证了模型算法的可行性。
The issue of multi-scale representations of spatial data is a hot research topic in GIS. Multi-scale representation database (MRDB) contributes to many GIS related fields, such as multi-scale spatial analysis, vector data progressive transmission over internet, data integration, and self-adaptive dynamic visualization and so on. At present, there are two strategies to construct a MRDB:static multiple versions and dynamic deriving. The former has some drawbacks, such as data redundancy, inconsistency and outdated; the latter seriously depends on the development of map generalization, which is a difficult problem. To remedy these shortages we present an object-oriented multi-scale spatial data model "lifespan model" from the viewpoint of data organization, which integrates static versions and dynamic operations together. The followings are the specific research contents of this study.
1. The thesis gives a review on the research progress in multi-scale data model. Several representative research works are analyzed. They are GEODYSSEY, Map Cube Model, Stratified Map Spaces, Abstract Cell Complexes, MADS and VUEL. All of these models can be categorized into two classes:the hierarchy model (eg:the Map Cube Model and the Stratified Map Spaces Model) and the generalization deriving model (eg:GEODYSSEY、MADS. VUEL). Generally speaking, all these models either focus on the hierarchy or on the generalization. No one considers both of the hierarchy and generalization together. In fact, both of the representations and the scale transformations can be integrated under the framework of object-oriented. Meanwhile all of these models usually focus on the level of concept model or logical model or physical model. No one relates all of the three models and integrates them to form a system. Hence we think the future direction of multi-scale spatial data model is to integrate representations and scale transformations together, and join concept model, logical model and physical model.
2. Scale is an important characteristic of spatial data. In this study, we discuss the concept of scale from three aspects:connotation, extension and category. From viewpoint of the cognition, we summarize the scale characteristics of spatial data including:scale effect, scale dependence, and scale invariance, which are the theory basis of scale transformation and multi-scale representation. From the viewpoint of representation, the multi-scale representations are not only the results of hierarchical spatial cognition and scale transformation, but also the tools to assist spatial cognition from coarse to fine.
3. From the viewpoint of object-oriented (00), we present a multi-scale data model "Lifespan Data Model", which integrates the representation and scale transformation together. According to the idea of 00, we model the representation as the attribute of object, and the scale transformation as the method of object respectively. Based on this model, we can dynamically derive any representation from the scaling of basic representation. Then the polymorphism of spatial data can be better represented and the problem of inconsistency and update can be avoided.
4. From the viewpoint of data manipulation, we design a suit of scale transformation modes, including map generalization transformation, LOD transformation, morphing transformation and equal transformation. The basic idea is LOD transformation is to partition the full representation into series of details with different scale hierarchies, and then the representation at any scale can be accumulated with the details of corresponding levels. This mode has some favorable characteristics, such as little data volume, wide scale span, operator integration and so on. The morphing transformation is based on the idea of shape interpolation. It can be used to drive smooth and consecutive representations. Within the framework of 00, all of these modes are integrated by the technologies of aggregation and inheritance. Consequently, the efficiency and flexibility of the lifespan model are greatly improved.
5. From the viewpoint of data organization, we present a graph based multi-scale data structure "evolution graph". For the evolution graph, the vertex denotes one or sets of representation, and the edge denotes the scale transformation operation relating two consecutive representations. Based on the properties of representations, we summarize four types of vertex:solid vertex, fake vertex, detail vertex and complex vertex. Based on the properties of scale transformation we summarize four types of edges:generalization edge, LOD edge, morphing edge and equal edge. The graph with different vertexes and edges can denote different scale transformation modes. A series of ordered vertexes and edges directly reflect the complex scale evolution process of spatial data over a large scale space. Finally the evolution graph extends the traditional scale point oriented static representations to the scale space oriented dynamic representations.
6. Based on the Domap software platform, we develop a lot of experiments to verify the feasibility of the model and algorithms.
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