A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and non-spatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

Spatial aggregations for spatial based decision 

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of nonspatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by nonspatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.     

Spatial aggregations for spatial based decision making in spatial data warehouses

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of non-spatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by non-spatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.     

三维城市模型辅助空间决策支持的研究思路

传统的空间分析方法由于只考虑目标本身的几何特征及相互间存在的简单拓扑关系,缺乏与专业应用模型的有机结合,在辅助空间决策方面远没有发挥应有的作用。随着三维城市模型在城市规划设计管理和城市信息化中的广泛应用,三维城市模型的辅助空间决策支持的研究已经提到议事日程。论述了三维城市模型辅助空间决策支持的典型数学模型,如统计模型、时间序列模型、空间动力学模型等,讨论了3DCM的一些典型应用,比如通视分析、噪声污染、日照分析、电磁波覆盖等。这对进一步推进三维城市模型数据的深层次应用和增值服务具有指导意义。图6,参8。     

影响MCSs移动的环境物理量场提取

已有研究表明,暴雨的形成常常与中尺度对流系统(MCSs)的移动息息相关,而引起中尺度对流系统移动的成因十分复杂.该文利用日本地球静止气象卫星(GMS)记录的红外辐射亮温资料对1998年6-8月青藏高原上的MCSs进行了自动追踪。在此基础上,运用空间数据挖掘中的决策树方法建立了移出高原的MCSs与其环境物理量场之间的关系,结果表明,利用该法来预测MCSs未来的移动路径是切实可行的。     

基于Voronoi图的复杂对象空间方位关系的推理计算

在分析总结前人研究成果的基础上,研究了复杂对象之间的空间方位关系,提出了基于Voronoi图的空间方位关系的推理模型,并详细论述了该模型的算法实现过程及步骤.实例计算表明该模型是正确和有效的.模型无需区分源目标与参考目标,具有自反性,不受对象的形状、大小、位置和距离等的影响,容易与基于Voronoi图的空间拓扑关系的相关理论结合,形成统一的推理模型.     

Research of ERP model system of spatial data warehouse

The broad sharing of spatial information is demanded in the infrastructure construction of spatial data in our country. And the spatial data warehouse realizes the effective management and sharing of spatial information serving as an efficient tool. This article proposes ERP model system that of general-decision-oriented for constructing spatial data warehouse from the aspect of decision application. In the end of article, the construction process of spatial data warehouse based on ERP model system is discussed.     

Research of ERP model system of spatial data warehouse

The broad sharing of spatial information is demanded in the infrastructure construction of spatial data in our country. And the spatial data warehouse realizes the effective management and sharing of spatial information serving as an efficient tool. This article proposes ERP model system that of general-decision-oriented for constructing spatial data warehouse from the aspect of decision application. In the end of article, the construction process of spatial data warehouse based on ERP model system is discussed.     

A Voronoi-based spatial algebra for spatial relations

Spatial relation between spatial objects is a very important topic for spatial reasoning, query and analysis in geographical information systems (GIS). The most popular models in current use have fundamental deficiencies in theory. In this paper, a generic algebra for spatial relations is presented, in which (i) appropriate operators from set operators (i.e. union, intersection, difference, difference by, symmetric difference, etc.) are utilized to distinguish the spatial relations between neighboring spatial objects; (ii) three types of values are used for the computational results of set operations-content, dimension and number of connected components; and (iii) a spatial object is treated as a whole but the Voronoi region of an object is employed to enhance its interaction with its neighbours. This algebra overcomes the shortcomings of the existing models and it can effectively describe the relations of spatial objects.     

基于几何元素的具有影像纹理的三维空间数据模型

基于对城市三维地理信息系统中复杂对象进行描述 ,以及三维空间查询和影像纹理真实化表达的需要 ,将空间现象抽象为点对象、线对象、面对象、体对象、数字地面模型和栅格影像 6类。基于点、直线段和面片 3类几何元素 ,建立了具有真实影像纹理的三维地理信息系统的空间数据模型 ,并给出了其数据存储结构。理论分析表明 ,该模型不仅可以表达规则对象 ,也可以表达非规则对象 ,同时还可以依据模型中隐含定义的几何元素之间的拓扑关系 ,方便地推求空间对象之间的空间关系。相对于矢量栅格集成的数据模型而言 ,该模型把三维几何建模矢量数据和影像纹理栅格数据分开存储 ,可以方便地在关系数据库中予以实现 ,同时 ,采用基于面片几何元素的形式很容易实现空间面对象或体对象的不同侧面的影像纹理贴加。     

一种可用于分类型属性数据的多变量决策树算法

针对绝大部分多变量决策树只能联合数值型属性,而不能直接为带有分类型属性数据集进行分类的问题,提出一种可联合多种类型属性的多变量决策树算法(CMDT).该算法通过统计各个分类型属性的属性值在各个类别或各个簇中的频率分布,来定义样本集合在分类型属性上的中心,以及样本到中心的距离.然后,使用加权k-means算法划分决策树中的非终端结点.使用这种结点划分方法构建的决策树可用于数值型数据、分类型数据以及混合型数据.实验结果表明,该算法建立的分类模型在各种类型的数据集上均获得比经典决策树算法更好的泛化正确率和更简洁的树结构.     

Research on spatial association rules mining in two direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and twodirection association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During twodirection spatial association rules mining, an algorithm is proposed to get nonspatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into nonspatial associations and the nonspatial itemsets were gotten. Based on the nonspatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.     

Research on spatial association rules mining in two-direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and two-direction association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During two-direction spatial association rules mining, an algorithm is proposed to get non-spatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into non-spatial associations and the non-spatial itemsets were gotten. Based on the non-spatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.     

一种改进的自映射空间判定树算法

判定树在基于知识的专家系统中非常有用,同时在数据挖掘中也是一种重要的方法.但是目前的判定树判定方法并不能准确、清晰地处理与人类思想和感觉的知识.通过自映射空间模型作为知识表达和处理不确定性的方法以达到改进目前方法的目的.与传统的分类方法相比,自映射空间方法更好地集成了模糊性和随机性.提出了基于自映射空间模型的判定树方法,该方法处理人类思维更加自然.在实际的分类问题过程中,自映射空间方法更加有效、灵活.     

制图综合中空间关系相似度的集成表达

基于空间关系的概念邻域,研究了空间实体多尺度表达中空间关系变化程度的比较和计算问题.首先从数字景观多尺度表达的角度对空间关系包含的拓扑关系、方位关系、距离关系分别建立了具有适当空间关系分辨率的概念邻域结构,其次,基于已建立的空间关系概念邻域给出了多尺度对象空间关系差异程度的集成表达和度量方法,从而得出了空间关系相似度的计算公式,最后结合实验数据的分析和计算,证明了该空间关系相似度集成表达模型和计算方法的合理性及适用性.     

Scale-based spatial data model for GIS

Being the primary media of geographical information and the elementary objects manipulated, almost all of maps adopt the layer-based model to represent geographic information in the existent GIS. However, it is difficult to extend the map represented in layer-based model. Furthermore, in Web-Based GIS, It is slow to transmit the spatial data for map viewing. In this paper, for solving the questions above, we have proposed a new method for representing the spatial data. That is scale-based model. In this model we represent maps in three levels: scale-view, block, and spatial object, and organize the maps in a set of map layers, named Scale-View, which associates some given scales. Lastly, a prototype Web-Based GIS using the proposed spatial data representation is described briefly.     

基于PCA和决策树模型的农用地(耕地)质量评价研究

主要利用ArcGIS、PCA和See 5决策树模型,以原始数据为基础,对福建省将乐县农用地(耕地)质量进行评价.首先根据将乐县的自然、社会和经济等各方面情况,初选排溉条件等9个主要指标作为评价因子;然后用ArcGIS矢量化提取相关图层并进行相应的空间分析,经过多图层叠加确定评价单元及其属性数据;再利用PCA方法最后确定耕地质量评价综合因子;接着利用数据挖掘工具See 5,自动构建决策树模型,进而用构建的模型来获取耕地质量分等规则的方法;最后划分耕地质量级别,并进行抽样检验结果的准确性.研究结果表明:该县耕地质量中等,一至四等地分别占22%,44%,15%,19%,经实地检验评价结果基本与实际情况相符.     

一种基于模糊集的混合空间推理方法

基于模糊集合理论, 提出一种将定性空间推理与定量空间推理相结合进行混合空间推理的方法. 将对象间的空间关系解释为表达有关对象空间信息语言变量的限制, 引入语言变量表达定性空间关系; 把语言变量与模糊集合关联起来, 获得定量空间信息; 通过将语言值映射为模糊集合, 以及将模糊集合的元素映射为语言限制词, 实现了定性空间信息和定量空间信息间的转换. 此外, 还给出了与Allen提出的二元基 本关系关联的模糊关系, 并得到了基于模糊关系的Allen推理算法, 从而实现了空间关系间 的推理.     

环境调控空间决策支持系统的设计与实现--福建海岸带环境调控决策支持系统

以福建省海岸带环境调控决策支持系统为例 ,对环境模拟建模、地理信息系统、遥感技术、空间数据仓库与数据挖掘融合而成的环境空间决策支持系统的建设与应用进行了探讨 .首先提出了空间决策支持系统可扩展的体系结构 ,并结合应用需求着重介绍环境空间数据仓库的设计、数据挖掘的过程以及若干环境模拟与评价可扩展模块的实现方法 ,最后给出应用系统的功能框架和系统界面 .     

Scale-based spatial data model for GIS

Being the primary media of geographical information and the elementary objects manipulated, almost all of maps adopt the layer-based model to represent geographic information in the existent GIS. However, it is difficult to extend the map represented in layer-based model. Furthermore, in Web-Based GIS, It is slow to transmit the spatial data for map viewing. In this paper, for solving the questions above, we have proposed a new method for representing the spatial data. That is scale-based model. In this model we represent maps in three levels: scale-view, block, and spatial object, and organize the maps in a set of map layers, named Scale-View, which associates some given scales.Lastly, a prototype Web-Based GIS using the proposed spatial data representation is described briefly.