空间数据仓库元数据的结构体系

给出了空间数据仓库元数据的基本概念，说明了空间数据仓库元数据的相关技术．重点论述了空间数据仓库元数据的结构体系是由11类信息组成，它们分别是空间数据标识信息、区域范围信息、邻接信息、表示信息、数学基础信息、数据集内容信息、质量信息、分发信息、安全信息、联系信息和时间信息，并设计出了空间数据仓库元数据的组织结构。     

地球空间数据集成多尺度问题基础研究

多尺度数据集成是地球空间数据集成中最难处理的问题。将多尺度数据的集成分解为空间和时间多尺度数据集成,在分析应用项目对数据尺度需求的基础上,就两种多尺度数据集成的传统和数据意义上的集成方法进行了详细的探讨。     

空间数据：性质、影响和分析方法

20世纪90年代以来,以信息技术为主要标志的科学进步日新月异,正深刻地改变着人类社会的生产和生活方式。人类活动所涉及的信息80％以上属空间信息,可由地理坐标确定其空间区位,美国已将发展空间信息科学视为提升其国家竞争力水平的重要途径之一。2000年美国国家地理信息与分析中心（Nat ionalCenterforGeo graphicInformat ionandAnalysis,NCGIA）在其跨世纪的研究规划中提出了发展地理信息科学的Varenius计划。Varenius计划要求研究空间信息的不确定性理论,并开发处理这种不确定性的空间数据分析技术。鉴于空间数据的两维多方向性,空间数据所包含的误差远比单一方向性的时间序列数据来得复杂。最近发展起来的空间统计学和空间计量经济学不仅解决了标准统计方法在处理空间数据时的失误问题,更重要的是为测量这种空间联系及其性质、并在建模时明确地引入空间联系变量以估算与检验其贡献提供了全新的手段。基于美国地理信息科学的发展动态,重点阐释空间统计学和空间计量经济学的理论基础,包括空间数据的性质、空间信息的误差理论、空间数据分析的类型和内容。     

空间关系及其应用

GIS中的空间关系主要描述空间对象之间的各种关系,为GIS的分析提供基本的理论和方法支持。空间关系主要包括距离关系、方向关系、拓扑关系及空间关系相似性等。从空间关系的内涵来讲,空间关系模型应该能够反映目标尺度、人类认知、目标层次、现象的不确定以及随时间变化等特性对空间关系的影响;从数学角度来看,空间关系模型必须是可形式化和可推理的,以方便操作和实现;空间关系从人们认知的角度对空间现象和目标间的关系进行建模,因而在空间数据查询、检索、空间数据挖掘、空间场景相似性评价以及图像理解等应用领域得到了广泛应用。现有GIS模型主要针对二维平面的简单对象、确定的现象进行建模,因而大多数空间关系模型只描述和推理二维平面对象和确定现象的空间关系,三维空间关系、不确定空间关系、复杂目标间的多层次空间关系推理、遥感图像空间关系的抽取、描述与推理将是未来空间关系研究和发展的重要方向。     

中国SARS疫情的探索性空间数据分析

运用空间统计学手段对中国省级SARS疫情的空间分布格局做了探索性的空间数据分析(exploratory spatial data analysis)。Moran I统计分析表明SARS疫情的分布具有很强的空间自相关，Moran Scatterplot进一步揭示了这一空间关系的地域差异及其各区位SARS疫情分布对总体趋势的偏移，[WTBX]Gi*[WTBZ] 统计则确定了疫情分布的空间集聚模式及其随时空转移轨迹。分析表明中国省级SARS疫情的发展并非一个相互独立的过程,而是存在着统计学意义上可测度的空间关系，且这种关系在空间相邻时最为显著。     

空间数据挖掘系统及其应用

近年来，空间数据挖掘系统的开发及应用已取得了极大的进展。文章在介绍已有空间数据挖掘系统的基础上，简述了空间数据挖掘与空间数据仓库、空间数据挖掘与地理信息系统的关系，阐述了空间数据挖掘的智能分析及其应用，探讨了目前空间数据挖掘系统开发及应用中存在的主要问题及未来的主要研究方向。     

Geostatistical methods to identify and map spatial variations of soil salinity

The problem of estimating and predicting spatial distribution of a spatial stochastic process, observed at irregular locations in space, is considered in this paper. Environmental variables usually show spatial dependencies among observations, with lead one to use geostatistical methods to model the spatial distributions of those observations. This is particularly important in the study of soil properties and their spatial variability. In this study geostatistical techniques were used to describe the spatial dependence and to quantify the scale and intensity of spatial variations of soil properties, which provide the essential spatial information for local estimation. In this contribution, we propose a spatial Gaussian linear mixed model that involves (a) a non-parametric term for accounting deterministic trend due to exogenous variables and (b) a parametric component for defining the purely spatial random variation due possibly to latent spatial processes. We focus here on the analysis of the relationship between soil electrical conductivity and Na content to identify spatial variations of soil salinity. This analysis can be useful for agricultural and environmental land management.     

不同方法下土壤重金属元素空问最大变异距离研究——以浙江上虞为例

从地质统计学和全域型空间自相关分析两种途径探讨浙江上虞市土壤重金属的空间变异情况。研究结果表明，空间分析法得到的上虞市重金属空间变程比地质统计学法得到的空间变程更为直观和稳定；但空间分析法的准确程度依赖于空间间隔的大小，不同的空间间隔计算量有很大的不同，随着间隔的逐渐减小，计算量成倍增加。地质统计学法能快速得到空间变程，还能推测空间变异程度，用优化的拟合模型得到的结果更为准确。     

High-order Statistics of Spatial Random Fields: Exploring Spatial Cumulants for Modeling Complex Non-Gaussian and Non-linear Phenomena

The spatial distributions of earth science and engineering phenomena under study are currently predicted from finite measurements and second-order geostatistical models. The latter models can be limiting, as geological systems are highly complex, non-Gaussian, and exhibit non-linear patterns of spatial connectivity. Non-linear and non-Gaussian high-order geostatistics based on spatial connectivity measures, namely spatial cumulants, are proposed as a new alternative modeling framework for spatial data. This framework has two parts. The first part is the definition, properties, and inference of spatial cumulants—including understanding the interrelation of cumulant characteristics with the in-situ behavior of geological entities or processes, as examined in this paper. The second part is the research on a random field model for simulation based on its high-order spatial cumulants. Mathematical definitions of non-Gaussian spatial random functions and their high-order spatial statistics are presented herein, stressing the notion of spatial cumulants. The calculation of spatial cumulants with spatial templates follows, including anisotropic experimental cumulants. Several examples of two- and three-dimensional images, including a diamond bearing kimberlite pipe from the Ekati Mine in Canada, are analyzed to assess the relations between cumulants and the spatial behavior of geological processes. Spatial cumulants of orders three to five are shown to capture directional multiple-point periodicity, connectivity including connectivity of extreme values, and spatial architecture. In addition, they provide substantial information on geometric characteristics and anisotropy of geological patterns. It is further shown that effects of complex spatial patterns are seen even if only subsets of all cumulant templates are computed. Compared to second-order statistics, cumulant maps are found to include a wealth of additional information from underlying geological patterns. Further work seeks to integrate this information in the predictive capabilities of a random field model.     

基于空间统计分析与GIS研究江西省县域经济

利用空间统计分析方法能够扩展和加强GIS的空间分析功能,从而更深入地探索、分析、处理和解释经济发展的空间模式与空间关联。将空间统计分析与GIS相结合,探索了江西省各县域经济的空间分布格局与规律。结果表明,江西省各县域经济存在显著的空间自相关和聚集模式,经济发展在空间上具有明显的规律。     

空间数据挖掘技术

近年来，伴随着空间信息获取技术的快速发展，不同类型的空间数据库以及数据库中的数据量正在不断增长，如何从这些海量数据中提取出隐含的信息和知识已成为人们研究的热点。空间数据挖掘就是为了解决这一科学问题而提出的。该文在回顾已有空间数据挖掘规则的基础上。指出了空间数据挖掘过程中存在的主要问题，进而探讨了未来空间数据挖掘的主要研究方向。     

基于GDW的空间数据融合

空间数据融合是目前地学信息领域面临的难题。通过介绍Oracle数据仓库系统架构和其在地学信息存储和分析方面的优点,深入分析ArcInfo中的Geodatabase的空间数据组织模型,重点研究了Oracle Spatial的空间数据存储模型、空间数据和空间索引组织模式,并给出了空间数据融合的具体实现过程。针对具体项目,结合GIS二次开发,能非常高效地实现空间数据的融合。     

基于流溪河模型的新丰江水库入库洪水预报模型研究

基于流溪河模型构建了不同空间分辨率的新丰江水库入库洪水预报模型,采用粒子群算法优选模型参数进行洪水模拟,对比实测资料验证了模型性能。结果表明,建立的新丰江水库入库洪水预报流溪河模型模拟效果优良,符合新丰江水库入库洪水预报的精度要求,可用于新丰江水库入库洪水预报：90 m空间分辨率的模型模拟效果最好,200 m空间分辨率的模型也具有良好的模拟性能,500 m空间分辨率的模型模拟性能有明显降低。模型计算工作量随分辨率的提高呈指数增加,90 m空间分辨率的参数优选时间是200 m空间分辨率参数优选时间的7.04倍,而200 m空间分辨率的参数优选时间是500 m空间分辨率参数优选时间的3.53倍。     

别拉洪河流域湿地变化的多尺度空间自相关分析

利用空间自相关模型,结合GIS技术,对别拉洪河流域不同尺度上湿地空间自相关的动态变化进行了研究。结果表明,自20世纪50年代至2000年,别拉洪河流域湿地面积逐渐减少,湿地全局空间自相关呈先增加后减小再增加的"N"字型趋势;别拉洪河流域湿地高-高自相关类型有逐渐减少并向流域下游扩张的趋势,而低-低自相关类型有逐渐增加并向流域上游扩张的趋势;湿地空间自相关表现出明显的尺度效应,随着研究尺度的增大湿地空间自相关性逐渐增强;别拉洪河流域湿地空间自相关的动态变化主要是由人类对湿地的开发造成的。     

基于MAPGIS的地学空间数据库中数据质量的研究

MAPGIS作为基础平台目前广泛应用于我国地学空间数据库的建设之中,其空间数据的质量直接影响地学空间数据库的优劣.空间数据的精度,属性数据的正确性与完整性,空间数据拓扑关系的正确性是影响空间数据库质量的主要因素.通过实践总结,阐述了控制上述3方面数据质量的具体手段和方法,从而达到确保数据质量,满足建立地学空间数据库的要求,建成质量优良的地学空间数据库.     

岩土力学参数空间变异性的集合卡尔曼滤波估值

岩土参数具有结构性和随机性的空间变异特征,该特征导致岩土参数具有不确定性。以地质统计学作为岩土参数空间变异性分析的理论基础,将分布于研究区的岩土参数视为区域化变量,变异函数既描述了岩土参数整体的空间结构性变化,又描述了其局部的随机性变化,用变异函数理论模型作为描述岩土参数空间变异规律的数学模型。引入集合卡尔曼滤波（EnKF）分析方法,利用时空分布的观测数据,对岩土参数空间变异性进行估值。数值算例表明,EnKF能够有效地融合观测数据,较好地提供岩土参数空间变异性的估值。     

High-order Stochastic Simulation of Complex Spatially Distributed Natural Phenomena

Spatially distributed and varying natural phenomena encountered in geoscience and engineering problem solving are typically incompatible with Gaussian models, exhibiting nonlinear spatial patterns and complex, multiple-point connectivity of extreme values. Stochastic simulation of such phenomena is historically founded on second-order spatial statistical approaches, which are limited in their capacity to model complex spatial uncertainty. The newer multiple-point (MP) simulation framework addresses past limits by establishing the concept of a training image, and, arguably, has its own drawbacks. An alternative to current MP approaches is founded upon new high-order measures of spatial complexity, termed “high-order spatial cumulants.” These are combinations of moments of statistical parameters that characterize non-Gaussian random fields and can describe complex spatial information. Stochastic simulation of complex spatial processes is developed based on high-order spatial cumulants in the high-dimensional space of Legendre polynomials. Starting with discrete Legendre polynomials, a set of discrete orthogonal cumulants is introduced as a tool to characterize spatial shapes. Weighted orthonormal Legendre polynomials define the so-called Legendre cumulants that are high-order conditional spatial cumulants inferred from training images and are combined with available sparse data sets. Advantages of the high-order sequential simulation approach developed herein include the absence of any distribution-related assumptions and pre- or post-processing steps. The method is shown to generate realizations of complex spatial patterns, reproduce bimodal data distributions, data variograms, and high-order spatial cumulants of the data. In addition, it is shown that the available hard data dominate the simulation process and have a definitive effect on the simulated realizations, whereas the training images are only used to fill in high-order relations that cannot be inferred from data. Compared to the MP framework, the proposed approach is data-driven and consistently reconstructs the lower-order spatial complexity in the data used, in addition to high order.     

岩土力学参数空间变异性的集合卡尔曼滤波佶值

岩土参数具有结构性和随机性的空间变异特征，该特征导致岩土参数具有不确定性。以地质统计学作为岩土参数空间变异性分析的理论基础，将分布于研究区的岩土参数视为区域化变量，变异函数既描述了岩土参数整体的空间结构性变化，又描述了其局部的随机性变化，用变异函数理论模型作为描述岩土参数空间变异规律的数学模型。引入集合卡尔曼滤波（EnKF）分析方法，利用时空分布的观测数据，对岩土参数空间变异性进行估值。数值算例表明，EnKF能够有效地融合观测数据，较好地提供岩土参数空间变异性的估值。     

Spatial statistics for urban analysis: A review of techniques with examples

Traditionally, urban analysis has been quick to adopt and benefit from developments in technology (e.g., microcomputer, GIS) and techniques (e.g., statistics, mathematical programming). This has not been the case, however, with newer methods of spatial analysis — in particular, spatial statistics. Only recently has this situation started to change. This paper documents the confluence of spatial statistics and urban analysis by first reviewing developments in spatial statistics, and then presenting examples of recent applications in urban analysis. The developments reviewed fall under the rubric of global and local forms of spatial analysis, and cover three major technical issues: spatial association, spatial heterogeneity and the modifiable areal unit problem. The examples highlight the relevance and usefulness of the techniques reviewed for urban transportation and land-use applications. The paper concludes with conjectures concerning future developments at the intersection of spatial statistics and urban analysis.     

Spatial statistics for urban analysis: A review of techniques with examples

Traditionally, urban analysis has been quick to adopt and benefit from developments in technology (e.g., microcomputer, GIS) and techniques (e.g., statistics, mathematical programming). This has not been the case, however, with newer methods of spatial analysis — in particular, spatial statistics. Only recently has this situation started to change. This paper documents the confluence of spatial statistics and urban analysis by first reviewing developments in spatial statistics, and then presenting examples of recent applications in urban analysis. The developments reviewed fall under the rubric of global and local forms of spatial analysis, and cover three major technical issues: spatial association, spatial heterogeneity and the modifiable areal unit problem. The examples highlight the relevance and usefulness of the techniques reviewed for urban transportation and land-use applications. The paper concludes with conjectures concerning future developments at the intersection of spatial statistics and urban analysis.