An improved direction relation detection model for spatial objects

Direction is a common spatial concept that is used in our daily life. It is frequently used as a selection condition in spatial queries. As a result, it is important for spatial databases to provide a mechanism for modeling and processing direction queries and reasoning. Depending on the direction relation matrix, an inverted direction relation matrix and the concept of direction pre- dominance are proposed to improve the detection of direction relation between objects. Direction predicates of spatial systems are also extended. These techniques can improve the veracity of direction queries and reasoning. Experiments show excellent efficiency and performance in view of direction queries.     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is spatial object granularity,the other is pixel granularity.We also present an approach to combine inductive learning with conventional image classification methods,which selects class probability of Bayes classification as learning attributes.A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data.Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Comparing with the result produced only by Bayes classification,the overall accuracy increased by 11% and the accuracy of some classes,such as garden and forest,increased by about 30%.The results indicate that inductive learning can resolve spectral confusion to a great extent.Combining Bayes method with inductive learning not only improves classification accuracy greatly,but also extends the classification by subdividing some classes with the discovered knowledge.     

基于指示和多元地质统计学的空间预测方法

There are various occasions where simple, ordinary, and universal kriging techniques may find themselves incapa- ble of performing spatial prediction directly or efficiently. One type of application concerns quantification of cumulative distribution function （CDF） or probability of occurrences of categorical variables over space. The other is related to optimal use of co-variation inherent to multiple regionalized variables as well as spatial correlation in spatial prediction. This paper extends geostatistics from the realm of kriging with uni-variate and continuous regionalized variables to the territory of indicator and multivariate kriging, where it is of ultimate importance to perform non-parametric estimation of probability distributions and spatial prediction based on co-regionalization and multiple data sources, respectively.     

基于剖面拓扑推理的三维地质体重建方法

In order to solve the dynamic reconstruction and local updating problem of three-dimensional geological solids, topology reasoning is used for three-dimensional geological modeling. This can advance the level of the corresponding section automation in implementing the 3D geological solid dynamical reconstruction by the construction of and reasoning on topology on the 3D curved surface. This method has been successfully used in the Nanjing city geological modeling and the Zijin gold mine modeling. The results prove that this method adapts to coplanar section and noncoplanar section data, and improves the efficiency of 3D geological modeling.     

Land-use spatial optimization based on PSO algorithm

The optimization of land-use spatio-structure is one of the most important areas of land use management;constructing a spatial optimization model that is based on the micro spatial unit in a bottom-up mode plays an important role in coupling the quan-tity structure and spatial structure effectively.The objective of this research is to develop a land use spatial optimization model based on particle swarm optimization to make spatial decision in land use management.The model is implemented using real data-sets to emulate the process of spatial structure optimization in order to get the best landscape pattern under the control of decision environments.Simulation results revealed that the particle swarm optimization model has the ability to utilize the quantity and spa-tial structure.Furthermore,the result demonstrated that it can be used to stimulate the landscape pattern in designing the appropriate optimization environment,which could land quantity target to the basic spatial units effectively and provide appropriate spa-tio-structure for regional land use space layout decision making.     

Measurement of similarity for spatial directions between areal objects

Similarity for spatial directions plays an important role in GIS. In this paper, the conventional approaches are analyzed. Based on raster data areal objects, the authors propose two new methods for measuring similarity among spatial directions. One is to measure the similarity among spatial directions based on the features of raster data and the changes of distances between spatial objects, the other is to measure the similarity among spatial directions according to the variation of each raster cell centroid angle. The two methods overcome the complexity of measuring similarity among spatial directions with direction matrix model and solve the limitation of small changes in direction. The two methods are simple and have broader applicability.     

Application of integration of spatial statistical analysis with GIS to regional economic analysis

This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.     

Method and application of comprehensive knowledge discovery

This paper proposes the principle oi comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the com-prehensive knowledge discovery considers both the spatial relations and attributes of spatial entities or objects. We intro-duce the theory of spatial knowledge ex-pression system and some concepts inclu-ding comprehensive knowledge discovery and spatial union information table (SUIT). In theory, SUIT records all in-formation contained in the studied ob-jects, but in reality, because of the com-plexity and varieties of spatial relations,only those factors of interest to us are se-lected. In order to find out the compre-hensive knowledge from spatial databas-es, an efficient comprehensive knowledge discovery algorithm called recycled algo-rithm (RAR) is suggested.     

An optimum vehicular path algorithm for traffic network based on hierarchical spatial reasoning

Human beings' intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers' choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large-scale traffic networks.     

层次空间推理的机制及其在路径寻找方面的应用

人类对所处的客观世界的认识,具有很明显的空间层次特征,每一层次包含了解决特定问题的必要信息,而层次空间推理是人类在解决具有空间特征问题时常采用的一种方法。本文在探讨了层次空间推理的定义、一般性框架的基础上,重点描述了它在道路网络的路径寻找中的应用。     

基于规则知识的空间推理研究

在一般空间推理的基础上 ,提出了空间推理扩展 ,即基于规则知识的空间推理。该方法结合了人工智能的基本原理 ,继承并发展了空间推理的方法学 ,其推理过程建立在空间知识与模型集成的基础上 ,以规则知识为推理控制 ,因而更符合实际 ,具有更大的灵活性。实例验证了本方法的正确性。     

Computational complexity of spatial reasoning with directional relationship

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Computational complexity of spatial reasoning with directional relationship

The property of NP-completeness of topologic spatial reasoning problem has been proved. According to the similarity of uncertainty with topologic spatial reasoning, the problem of directional spatial reasoning should be also an NP-complete problem. The proof for the property of NP-completeness in directional spatial reasoning problem is based on two important transformations. After these transformations, a spatial configuration has been constructed based on directional constraints, and the property of NP-completeness in directional spatial reasoning has been proved with the help of the consistency of the constraints in the configuration.     

空间目标的方向关系定性推理

由于空间问题固有的复杂性和不确定性,空间关系的描述和推理普遍采用定性的方法来符合人们的认知。方向关系是一类重要的空间关系,以往对方向关系的定性推理大多在2个参照系下进行,将目标对象抽象为点或者认为目标的大小近似相等。为了更细致地推理方向关系,提出四种不同情况下基于1个参照系的定性方向关系推理方法,并就本方法存在的问题和今后的研究方向提出了一些看法和思路。     

不确定拓扑关系模糊推理

针对定性空间推理难以处理不确定空间拓扑关系推理的缺憾,将确定拓扑关系复合表推理方法扩展到模糊拓扑空间,建立了基于拓扑关系复合表的不确定拓扑关系模糊推理模型,设计了基于RCC5的拓扑关系复合表的模糊推理规则库和推理的方法流程,并结合具体计算实例对模糊推理模型进行了实验研究,推理结果验证了模型的认知合理性和可行性。     

三维体目标间拓扑关系与方向关系的混合推理

重点研究了三维空间中拓扑关系和方向关系间的混合空间关系推理。用Allen区间关系对描述基于投影的空间划分方法得到的方向区域和用九交矩阵描述的拓扑关系,用定义法研究混合空间关系推理,推理结果用组合推理表表示。     

GIS中拓扑和方向关系推理模型

当今GIS在空间分析、建模和推理方面的能力极其有限,究其原因,现有GIS空间数据表达不直接支持推理运算,空间关系推理模型相互独立且运算复杂,准确性不高。本文在深入研究现有空间拓扑关系和方向关系推理模型的基础上,基于Cobb的空间拓扑关系模型的基本思想,建立了拓扑关系矩阵TopX2×2、TopY2×2和方向关系矩阵Dir3×3并将拓扑和方向关系推理计算统一到空间目标的MBRs(MinimumBoundingRectangles)的比较运算上来,达到了简化推理运算和统一建模实现拓扑和方向关系的联合推理,丰富了空间关系推理的语义,提高了推理的准确性。     

线与线之间的空间拓扑关系组合推理

在空间拓扑关系组合描述的基础上，进一步完善了利用基本空间拓扑关系进行组合推理的方法。并建立了组合表。详细绘制了两条线之间的空间拓扑关系图。