摘要
随着城市地下空间工程的发展，大量的空间和非空间的数据得到采集和存储。如何更有效的利用这些数据，从中发现符合一定规律的、隐含的有用信息并服务于城市地下空间工程的超前地质预报和分析，是城市地下空间数据分析和综合利用的重要研究方向。为此，本论文将数据挖掘与可视化技术相结合，以提高整个数据挖掘过程的灵活性、有效性与交互性。
对于GIS可视化空间数据挖掘技术以及评测方法已有部分研究，然而应用于数据挖掘的可视化技术一般只作为数据对象的表达工具，在分析方法及过程本身中并没有进行有效的可视化，现有的可视化数据挖掘系统其可视化与数据挖掘技术之间的关系是松散的。此外，在城市超前地质预报方面，已有将GIS技术应用于地质工程、岩土工程领域的研究及一些软件系统，但这些系统有些只能实现对地表地形地貌的三维模拟，在几何建模、分析功能和交互功能上并不能很好满足用户的要求。
因此，本文系统讨论基于可视化数据挖掘技术的城市地下空间GIS系统的关键技术和构建方法，改进机器学习算法、空间和非空间的聚类算法，研究结合挖掘算法的相关可视化技术，进而研制一套支持可视化数据挖掘的城市地下空间GIS原型系统。主要研究工作如下：
（1）可视化空间数据挖掘技术的研究。从数据挖掘技术特点、海量数据特征以及多维、多源数据集成的角度进行综合分析，采用可视化数据挖掘和GIS技术的集成应用。在空间数据挖掘技术上，主要采用基于空间关联规则、基于支持向量机和基于聚类分析等空间数据挖掘方法。在空间数据挖掘的可视化技术上，提出了一种基于平行坐标理论的多维多时相空间数据可视化方法，能较好的处理海量空间数据可视化问题，使用Java3D技术可以实现复杂地质体的建模显示，以及空间插值结果的三维展示功能。
（2）支持向量机算法的研究。结合空间关联规则和基于案例推理（CBR）学习思想，对基于支持向量机的空间数据挖掘方法进行了深入分析，以GIS技术以及空间数据模型为切入点，提出了进一步提高分类精度和缩短训练时间的两种改进方法，即CBR初选训练子集和基于空间区域划分的SVM算法。与常规方法进行对比实验，结果表明两种改进算法能够缩短训练时间，在大数据量情况下提高进行空间数据挖掘的效率；其中基于空间区域划分的SVM算法还可以在一定程度上缩短训练时间。此外，对于空间数据挖掘中基于距离测度的空间分类方法做了改进，即以统计距离代替欧氏距离可以消除数据自身相关性带来的错误分类影响。
（3）城市地下空间GIS分类技术分析与数据质量控制。针对城市地下空间点、线、面数据，可以采用基于距离、数学形态、拓扑关系和空间关联规则的空间聚类分析方法来进行分类；对于文本分类，可以经过文本预处理、特征选择、特征项权重确定和具体分类等过程来实现。另外，针对空间分析过程中的抽样布点问题，采用基于三明治空间抽样模型的空间抽样方法对城市地下空间数据采集过程中的抽样布点问题进行模拟和改进，最终达到在不损失可信度和精度的前提下降低地质数据采集成本的目的。
（4）可视化空间数据挖掘系统的研制。在数据库设计、集成方法设计和数据流程设计的基础上，完成了城市地下空间GIS系统功能详细设计，并研制了基于插件形式进行城市地下空间数据挖掘GIS原型系统，可以应用于天津市城市地下空间超前地质预报。其中，采用插件式软件架构设计模式不仅可以较好地实现软件的松散耦合性能，达到弹性系统的目标；而且显著地降低了系统开发成本，提高了开发效率。
With the development of urban underground space engineering, a large amount of spatialand non-spatial data is acquired and stored. How to make more effective use of these data, andto find the useful information which is hidden and complied with certain rules, so as to servicegeological prediction in advance and analysis of urban underground space engineering, is animportant research area for data analysis and comprehensive utilization of urban undergroundspace. For this reason, this paper combines data mining and visualization technology to increasethe flexibility, efficiency and interactivity in entire data mining process.
There are partial researches on GIS visualization and evaluation methods of spatial datamining technology, but the visualization technology used in data mining is only as anexpression tool for data objects and is no effective visualization in analytic methods and processitself, The relationship between visualization and data mining technology in currentvisualization data mining system is loose. In addition, in the prediction of urban geology inadvance, there are partial researches and some software systems to apply GIS technology in thefield of geological engineering, geotechnical engineering, but some of these systems are onlyused in3D simulation of surface topography, and not very well meet the requirements of usersin geometric modeling, analysis function and interactive function.
This paper systematically discusses the key technologies for GIS system of urbanunderground space and construction methods based on visualization data mining technologies,improves machine learning algorithms, clustering algorithm of space and non-space, studies therelative visualization technologies combined with data mining algorithms, and develops a set ofGIS prototype system of urban underground space to support visualization data mining.Themain works are summarized as follows:
(1)Study on the technology of visualization spatial data mining. Through comprehensiveanalysis from the angle of the technical characteristics of data mining, mass data characteristicsand data integration with multi-dimension and multi-source, the integration application betweenvisualization data mining and GIS technology is adopted in geological prediction in advance ofurban underground space. In the spatial data mining technology, the spatial data miningmethods based on association rules, support vector machine (SVM) and clustering analysis aremainly used. In the visualization of spatial data mining technology, a visualization method ofmultidimensional multi-temporal spatial data based on parallel coordinates theory is presented,which can well deal with the visualization of mass spatial data, realize the modeling display ofcomplex geologic body by use of Java3D technology, as well as3D display of spatialinterpolation results.
(2)Study on support vector machine algorithm. To combine spatial association rule andcase-based reasoning (CBR), the spatial data mining method based on support vector machineis analyzed in-depth, as a starting point by use of GIS technology and spatial data models, twoimproved algorithms are presented to further improve classification accuracy and reducetraining time, such as the SVM with CBR to initial select training subset and the SVM withspatial region partition. Comparison with conventional methods, experimental results show thatthe two algorithms can shorten training time and improve efficiency of spatial data mining inthe case of large amount of data. The SVM algorithm based on space partition can also shortentraining time in a certain extent. In addition, the space classification method based on distancemeasurement is improved for spatial data mining, that is, statistical distance instead ofEuclidean distance, so as to eliminate the misclassification effect from data self correlation.
(3) Analysis on classification technology of urban underground space GIS and data qualitycontrol. For the data of point, line and surface from urban underground space, the spatialclustering analysis method based on distance, or mathematical morphology, or topology andspatial association rule is used to classification; and the text classification is realized by theprocess of text pre-processing, feature selection, determining feature-weight and specificclassification. In addition, for the problem of sampling distribution in spatial analysis process,the spatial sampling method based on Sandwich space sampling model is used to simulate and improve that of urban underground space in the data acquisition process, and the purpose ofreducing the cost of geological data acquisition is achieved under the premise of no loss inreliability and accuracy.
(4) Development of visualization spatial data mining system. On the basis of design ofdatabase, integration method and data process, the detailed function design on urbanunderground space GIS is completed, and a GIS spatial data mining prototype system based onplug-in form is developed to apply in geological prediction of urban underground space inTianjin. Among them, the use of plug-in software architecture design patterns not only canachieve good performance in software loose coupling and achieve the goal of elastic systems,but also can reduce the cost of systems development remarkably and increase developmentefficiency.
引文
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