空间数据研究的发展及对策

随着地理信息科学和地理信息系统的发展,空间数据先后经历数据生产、数据质量、数据不确定性和可用性4个研究阶段。在分析各阶段研究内容和方法的基础上,深入分析空间数据现有研究间的联系与区别,并给出未来的研究对策。     

空间数据融合的基本框架

本文就空间数据融合的基本理论和问题进行了阐述。空间数据分为矢量数据和栅格数据,由于矢量数据的融合比较简单,本文就矢量数据的图形数据和属性数据的融合加以简单论述;主要研究了栅格数据,目前对于栅格空间数据而言主要是影像数据,本文对影像数据的融合的层次,步骤和方式等问题进行了系统研究;而且对空间数据融合存在的问题和解决的途径进行了论述。     

空间数据重复记录的清理方法研究

在空间数据的更新过程中,需要利用多种数据源对现有数据进行更新,这样可能会导致在获得的空间数据中出现重复记录。利用计算机自动对这些重复记录进行检查处理,将会极大地降低数据生产者的工作量。本文根据在数字制图系统研制过程中所遇到的此类问题,提出了对重复空间数据进行检查与处理的方法,并在所研制的数字制图系统中得到了应用。     

基于MicroStation的空间数据提取方法研究

目前MicroStation在测绘中的应用越来越为广泛,由于MicroStation不是专门为测绘行业设计的平台,对于测绘中的某些具体作业,还需要结合实际的需要加以改造,才能满足要求。本文主要针对空间数据提取在Mi-croStation系统中的实现进行分析,并介绍了具体的实现方法。     

基于空间数据引擎的综合地下管线数据组织

在武汉市的综合地下管线建库工作中,数据组织是最为核心和关键的工作。我们结合实际情况,基于空间数据引擎制定了特有的、兼容性强的数据结构,并针对数据的特性进行了分层管理,建立了数据安全保障机制,充分发挥空间数据中间件ArcSDE的高性能,为数据库的安全、高效运行提供了坚实的保障。     

多源异构空间数据集成模型的研究

本文就目前空间数据集成的两种主流模式,在论证了各模式的优缺点的基础上,得出了用GML作为地理空间数据转换及传输的标准是切实可行、具有应用前景的方案。从而提出了基于GML的多源异构空间数据集成的模型,并探讨了基于这一模型实现空间数据共享的若干问题。     

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).     

Big spatial data for urban and environmental sustainability

ABSTRACT

Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.     

Developing local measures of spatial association for categorical data

This paper describes a procedure for extending local statistics to categorical spatial data. The approach is based on the notion that there are two fundamental characteristics of categorical spatial data; composition and configuration. Further, it is argued that, when considered locally, the latter should be measured conditionally with respect to the former. These ideas are developed for binary, gridded data. Local composition is measured by counting the numbers of cells of a particular type, while local configuration is measured by join counts. The approach is illustrated using a small, empirical data set and an ad hoc procedure is developed to deal with the impact of global spatial autocorrelation on the local statistics.The author gratefully acknowledges financial support from the GEOIDE Network of Centres of Excellence (ENV #4) and the helpful comments of three anonymous reviewers.     

空间数据仓库元数据的认知过程

空间数据仓库的认知过程主要就是十一个层次的世界通过十个算子转换的过程,如何将某两个世界进行转换,上述十个算子起着至关重要的作用。实际上,这些转换算子主要是依靠元数据来实现的,要实现两个世界的转换,通晓这两个世界的元数据是转换的前提。因此,这十一个层次的世界模型及转换算子就可构成空间数据仓库元数据的认知过程理论。     

GIS空间数据转换为DXF格式的方法研究

GIS空间数据具有多源性的特征,为数据综合利用和数据共享带来不便,解决多格式数据源集成一直是近年来GIS应用系统开发中需要解决的重要问题[5]。DXF格式作为一种空间数据转换的事实上的标准,对于GIS软件来说,实现两者的数据转换具有重要意义。本文分析了GIS空间数据与AutoCAD数据模型的组织特征,在比较分析的基础上设计了转换方案,并就实现过程中的关键技术进行了论述。     

An Improved Hilbert Curve for Parallel Spatial Data Partitioning

A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data i...     

Identifying local spatial association in flow data

In this paper we develop a spatial association statistic for flow data by generalizing the statistic of Getis-Ord, G _i (and G _i ^*). This local measure of spatial association, G _ij, is associated with each origin-destination pair. We define spatial weight matrices with different metrics in flow space. These spatial weight matrices focus on different aspects of local spatial association. We also define measures which control for generation or attraction nonstationarity. The measures are implemented to examine the spatial association of residuals from two different models. Using the permutation approach, significance bounds are computed for each statistic. In contrast to the G _i statistic, the normal approximation is often appropriate, but the statistics are still correlated. Small sample properties are also briefly discussed. Received: 18 February 1998/Accepted: 29 September 1998     

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。     

基于空间行为的面向对象空间数据模型

本文根据面向对象空间数据模型的相关概念和特点,将地理空间中的空间行为进行建模后纳入面向对象空间数据模型的框架中。面向对象空间数据模型由数据模型和空间行为模型两个部分组成:数据模型包括空间特征数据和属性特征数据;同时将空间行为模型分为两大类,即静态空间行为模型(地理目标间关系)和动态行为模型(地理目标行为)两部分。在此框架提出的基础上,本文利用二进制、XML流等格式文件对该空间数据模型进行了整合和组织。     

空间数据模糊聚类的有效性(英文)

The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.     

森林资源空间数据更新的时空数据模型研究

地理信息系统(GIS)技术可以实现空间数据和属性数据统一存贮、管理和数据更新,但往往缺失时间数据,较难实现历史查询。本文以森林小班数据为例,分析林木自然生长以外数据更新的空间形态变化,使用图论对森林资源小班数据更新的时空信息进行描述,在此基础上构建森林资源更新的时空数据模型,此模型能够方便地进行历史查询分析。     

一种分布式空间数据的服务聚合模型

数据源的异构、GIS服务规范的自定义以及分布式存放等问题制约了空间信息的广泛共享和应用。本文基于空间信息共享平台建设,围绕服务动态聚合的思想,结合OGC的标准规范和技术思想,在具有REST风格的GIS服务的基础之上,提出了一个服务聚合模型,通过该模型实现了分布式存放的异构瓦片数据的无缝集成显示以及网络要素服务的功能共享。     

基于规则的空间数据组织模式研究

本文提出一种基于"规则"的空间数据组织模式。要素规则从几何和属性两个方面控制空间数据的质量和逻辑;拓扑规则将地理要素与要素之间的拓扑关系独立出来,增加了空间数据使用的灵活性;应用规则主要针对用户访问空间数据时的透明度和安全性等问题。