Categorical database generalization in GIS
Categorical database generalization in GIS
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发表时间：
2002
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通讯作者：
Y. Liu
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作者：
Y. Liu
关键词：分类数据库，分类数据库泛化，形式数据结构，约束，转换单元，分类层次，聚合层次，语义相似性，数据模型，Delaunay三角网。语义相似度评估模型。分类数据库广泛应用于GIS中，用于各种应用、分析、规划、评估和管理。从单一数据库衍生出更详细的不同分辨率数据库的数据库泛化是GIS和制图领域的关键研究问题和研究热点之一。本论文提出了 GIS 中分类数据库泛化的框架。它包括定义当前分类数据库泛化变换的概念方面和泛化变换的约束、支持数据结构和变换单元的阐述、辅助分析方法的开发以及一些应用示例的演示。数据库泛化被视为一个转换过程。根据分类数据库的特点和分类数据库的泛化定义了三种变换。它们是地理空间模型变换、对象变换和关系变换。每个转换都有一定的功能并涉及数据库的某些方面。地理空间变换主要用于定义新数据库的内容框架，决定新数据库的主题。对象转换和关系转换处理对象的主题和几何方面以及对象之间的关系从现有数据库到新数据库的转换。数据库泛化（转换）需要一种能够有力支持数据库中的数据组织、空间分析和决策的数据结构。数据结构的设计应考虑两个功能。它为描述和组织空间对象及其之间的关系提供了基础。另一个是用于分析和支持对空间对象的操作。本文介绍了IEFDS，它是FDS的集成和扩展版本，作为支持自动化数据库泛化转换的数据模型。 FDS 的补充是三角形。基于IEFDS中三角形的构成属性，提出了三角形及其分类，IEFDS在扩展相邻关系、包含关系以及提取骨架线方面发挥着重要作用。本文还提供了一些利用扩展相邻关系和语义三角形的空间查询操作的例子。在分类数据库中，对象类型之间的相似性可以通过相似性度量来描述。相似性取决于应用程序。从某种意义上说，相似性将控制和指导数据库转换操作。本研究基于集合论、分类和聚合层次结构，提出了相似度评估模型和相似度矩阵来分析和表示对象和对象类型之间的相似度。转换条件等约束在数据库泛化过程中起着关键作用。约束可用于识别冲突区域、指导操作选择和触发操作以及管理数据库泛化。泛化过程应该在约束的控制下通过一系列操作来执行。三种类型的约束，数据模型。在土地利用数据库泛化中提出了基于面向对象数据库的对象和关系。这些约束可以由用户交互地指定，并且可以改变以反映不同的目标或目的。这些类型的约束取决于应用程序。这将使数据库泛化过程非常灵活/适应性强，并且决策可以基于地理意义而不仅仅是基于对象的几何形状。本研究提出的一个重要元素是转换单元。它是一个重要的处理单元，因为许多泛化问题需要通过将相关对象的子集视为一个整体来解决，而不是单独处理它们。从某种意义上说，变换单元是一个基本的分析。处理、决策单元和聚合操作过程的触发器，在数据库转换中起着重要作用。冲突的对象及其（它们的）相关对象被组织成一个转换单元。在对象的主题和/或几何方面或对象之间的空间关系或整合它们时，“将对象的子集聚集在一起”的转换单元。创建转换单元的主要目的是为聚合操作做准备。它限制了聚合操作中一组相关对象的面积和数量。不同的冲突类型将创建不同类型的转换单元。在本研究中，根据所讨论的约束条件考虑了四种类型的转换单元。每种转换单元都有相应的聚合操作。辅助分析方法实际执行空间分析和变换所需要的最基本的任务是确定在哪里进行概括、如何进行概括以及何时进行概括。本文介绍了一些已经开发出来的辅助分析方法，这些方法可以解决数据库转换中的许多重要的几何和主题问题，这些辅助分析方法包括语义相似度矩阵、计算相似度模型、变换单元的检测和创建、区域对象聚合分析以及基于变换单元、多邻域、对象聚类的过程。论文中的应用实例包括对象聚类、土地利用聚合和流域分级自动组织等，这些应用实例展示了IEFDS和相似性评估模型的适用性和优势，这些支撑模型在数据库泛化中组织专题和几何数据、空间分析和空间查询方面发挥了关键作用，也证明了数据库泛化中的许多关键几何和专题问题可以得到解决，或者可以更有效地解决。模型。
Key words: Categorical database, categorical database generalization, Formal data structure, constraints, transformation unit, classification hierarchy, aggregation hierarchy, semantic similarity, data model, Delaunay triangulation network. semantic similarity evaluation model. Categorical databases are widely used in GIS for different kinds of application, analysis, planning, evaluation and management. Database generalization that derives different resolution databases from a single database with more detail is one of the key research problems and a hot research point in the GIS and Cartography field. This dissertation presents a framework for categorical database generalization in GIS. It includes defining conceptual aspects of current categorical database generalization transformation and constraints for generalization transformation, elaboration on supporting data structure and transformation units, development of auxiliary analysis methods, and demonstration of some application examples. Database generalization is considered as a transformation process. Three kinds of transformation are defined based on the characteristics of categorical database and categorical database generalization. They are geo-spatial model transformation, object transformation and relation transformation. Each transformation has a certain function and deals with some aspects of database. Geo-spatial transformation is mainly used to define the content framework of a new database and decide the theme of a new database. Object transformation and relation transformation deal with transformations of thematic and geometric aspects of objects and relationship between objects from an existing database to a new database. Database generalization (transformation) requires a data structure that strongly supports data organization, spatial analysis and decision-making in a database. The design of a data structure should take two functions into account. One provides the basis for describing and organizing spatial objects and the relationships between them. and the other is for analyzing and supporting operations on spatial objects. This thesis introduces the IEFDS, an integrated and extended version of FDS, as a data model to support automated database generalization transformation. The addition to FDS is triangles. The triangles and their classification are proposed based on constituent properties of triangles in IEFDS which plays an important role in the extended adjacent and inclusion relations and extracting the skeleton line. Some examples of spatial query operations that make use of the extended adjacent relation and semantic triangles are also provided in this thesis. In a categorical database, similarity between object types can be described by a similarity measure. The similarity is application-dependent. In a sense, the similarity will control and guide database transformation operations. The similarity evaluation model and similarity matrix are proposed for analyzing and representing similarity between objects and object types in this study which is based on Set-theory, classification and aggregation hierarchy. The constraints such as transformation conditions play a key role in the process of database generalization. Constraints can be used to identify conflicting areas, guide choices of operations and trigger operations as well as govern the database generalization. The processes of generalization should be performed by a series of operations under the control of constraints. Three types of constraints, data model. object and relationships based on an object-oriented database are proposed in landuse database generalization. These constraints can be specified interactively by users and varied to reflect different objectives or purposes. These types of constraints are applicationdependent. This will make the database generalization process very flexible/adaptive, and the decisionmaking can be based on geographic meaning and not simply on the geometry of an object. An important element proposed in this study is the transformation unit. It is an important process unit as many generalization problems need to be solved by considering a subset of related objects as a whole, rather than treating them individually. In a sense, the transformation unit is a basic analysis. processing, decision-making unit and a trigger to aggregation operation processes and it plays an important role in database transformation. The conflicted objects and its (their) related objects are organized into a transformation unit. A transformation unit that "brings together- a subset of objects can he created by conflict,; in thematic and /or geometric aspects of objects or spatial relation among objects or integrating them. The main purpose of creating a transformation unit is for the preparation of an aggregation operation. It limits the area and number of a set of related objects in an aggregation operation. The different conflict types will create different types of transformation units. For this study, four types of transformation units are considered based on the constraints discussed. Each of which has a corresponding aggregation operation. The auxiliary analysis methods (algorithms) are needed to actually perform spatial analysis and transformations. The most fundamental tasks are to identify where to generalize, how to generalize, and when to generalize. The thesis introduces a number of auxiliary analysis methods that have been developed to solve a number of important geometric and thematic problems in database trans, form ati on. These auxiliary analysis methods include semantic similarity rnatrix, computing a model of similarity, detection and creation of transformation units, area object aggregation analysis and the process based on transformation units, multineighborhood, object cluster and creation of catchments hierarchy etc. Such examples of the application are included in the thesis as object cluster, land use aggregation and automated organization of hierarchical catchments. The application examples demonstrate the applicability and benefits of the IEFDS and similarity evaluation model. These supporting models play a key role in organizing thematic and geometric data, spatial analysis and spatial query in database generalization. It also proved that a lot of critical geometric and thematic problems in database generalization can be solved, or can be solved in a more efficient way, with the support of an adequate data model.