基于GIS空间数据挖掘技术的应用研究

介绍了空间数据挖掘的概念,分析了基于GIS的空间数据挖掘的流程,详细介绍了空间数据挖掘在GIS中的应用和可视化方法,最后提出GIS空间数据挖掘存在的问题,阐述了技术的发展前景。     

可视化空间数据挖掘研究综述

空间数据挖掘针对的是更具有可视化要求的地理空间数据的知识发现过程,可视化能提供同用户对空间目标心理认知过程相适应的信息表现和分析环境,可视化与空间数据挖掘的结合是该领域研究发展的必然,并已成为一个研究热点。论文综述了空间数据挖掘和可视化的研究现状,重点阐述了空间数据挖掘中的可视化化技术及其应用,并对可视化空间数据挖掘的发展趋势进行了阐述。     

GIS和DEM技术在贵州省县级农用地分等定级数据库中的应用探讨

以贵州省县级农用地数据库的建立方法为例,概述在GIS和DEM技术支持下的农用地分等定级的技术路线,详述GIS环境下图库的建立、评价单元的划分、数字高程模型的应用和成果的应用等几项关键步骤,探讨如何以GIS技术建立县级农用地的分等定级体系.     

空间数据挖掘与GIS集成及应用研究

阐明空间数据挖掘与GIS集成的优越性,分析空间数据挖掘与关系数据库系统的区别,介绍面向对象技术对空间数据挖掘和空间数据挖掘的常用算法．在此基础上介绍地理信息系统与空间数据挖掘工具及应用。     

基于聚类的空间数据挖掘技术在中药资源分析中的应用

空间数据挖掘技术是从空间数据库中提取隐含的、用户感兴趣的知识.主要阐述空间数据挖掘技术在中药资源分析中的应用及方法,并用实例证明其结果的可行性.     

基于空间数据挖掘的合肥市应急避难场所选址

城市应急避难场所的规划选址需要依据城市空间的总体布局、人口、现有避难场所、土地利用现状等文本和空间数据。以合肥市部分地区为例,利用ArcGIS作为构建模型的基础平台,搭建一个地理空间数据挖掘应用模型,运用空间分析方法对空间数据进行处理,选取符合模型条件的应急避难场所并实现可视化,为防震减灾应急决策和救援提供参考。     

GIS在农用地分等中的应用

农用地等别是衡量农用地质量的基本指标,是确定土地使用税、土地征用补偿及租赁费等的重要依据。GIS强大的数据输入、管理、查询、分析和输出功能,为农用地分等提供了全新的方法和技术手段。本文以广东省四会市农用地分等工作为例,探讨了GIS在农用地分等中的应用。     

县级农用地分等成果更新方法的研究

以福建省某县农用地分等成果更新项目为例,借助第二次全国土地调查成果,在上轮分等成果的基础上,对分等成果更新方法进行研究,可为今后的县级农用地分等成果更新工作提供借鉴和参考.     

基于GIS的山区农用地定级方法研究

为探索提高山区县级农用地定级结果精度,以柞水县为例,基于GIS技术,运用特尔菲法及直线法、缓冲区法和最短路径法,建立定级因素因子体系,根据各因子的不同类型对其进行赋分和量化,在此基础上求算定级指数,初步划分农用地级别,通过级差收益检验、相关性检验及相关专家论证确定级别划分结果。研究结果表明,该方法不但可快速划分出农用地级别,且可对划分级别进行检验,从而确保定级结果的准确性。研究结果可为山区农用地定级工作的开展提供思路和参考方法,也可为全面掌握和科学量化农用地质量,促进其合理利用提供科学依据。     

空间数据挖掘技术及其应用

介绍了空间数据挖掘的概念，体系结构，常用的方法，可获取的知识类型及其应用。     

Development of a data mining method for land control

Land resources are facing crises of being misused, especially for an intersection area between town and country, and land control has to be enforced. This paper presents a development of data mining method for land control. A vector-match method for the prerequisite of data mining i. e., data cleaning is proposed, which deals with both character and numeric data via vectorizing character-string and matching number. A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse. In order to monitor land use dynamically and accurately, it is suggested to set up a real-time land control system based on GPS, digital photogrammetry and online data mining. Finally, the means is applied in the intersection area between town and country of Wuhan city, and a set of knowledge about land control is discovered.     

DEVELOPMENT OF A DATA MINING METHOD FOR LAND CONTROL

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矿区土地动态变化时空数据模型设计

在地下开采活动过程中,矿区地表地理实体具有明显的时空变化特征,本文是在分析现有时空数据模型的基础上,针对矿区采动受损地表实体的损害及修复,按照面向对象的思想,将矿区地面实体分为点、线、面三类,分别对各种受损对象设计了复垦区时空数据模型,在此基础上可以进一步构建矿区土地复垦时空数据库。     

可视化交互空间数据挖掘技术的探讨

随着地理信息获取技术飞速发展,使得当前存储在空间数据库中的空间数据的深度和广度得到了前所未有的发展,传统的空间统计方法和空间分析方法已经难以有效而迅速地处理和分析它们,如何有效而及时地分析和处理空间数据变得越来越迫切。空间数据挖掘作为上个世纪90年代逐步发展起来的新兴技术,逐渐在研究和实践中显示出它的优势。与此同时,地理可视化技术也逐步走向成熟,二者的结合催生出新型空间数据分析技术———可视化交互空间数据挖掘。本文就该技术的相关问题进行了一些研究探讨。     

基于空间数据挖掘的煤矿安全管理的研究

煤炭企业属于传统的资源开采型企业,煤矿安全对煤炭企业影响巨大,安全工作在煤矿生产中占有重要地位。其管理好坏直接关系到煤炭企业的生存和发展。煤矿安全管理任务十分艰巨,是因为影响煤矿安全的因素非常复杂,而且含有大量的不确定性因素。本论文利用空间数据挖掘技术来研究采煤方式的选择,并选用焦作矿务局某矿一工作面的实例进行分析,结果表明用粗集来确定采煤方式是比较客观的、科学的和全面的,对保证煤矿的安全提供了一定的保障。     

Behavior mining of spatial objects with data field

The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.     

基于数据场的空间目标行为挖掘

The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining. Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804), the National Natural Science Fundation of China (No.60743001), the Best National Thesis Fundation (No.2005047), the National New Century Excellent Talent Fundation (No.NCET-06-0618).     

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.     

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 results 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.