地统计学用于空间不确定性的描述

Most geospatial phenomena can be interpreted probabilistically because we are ignorant of the biophysical processes and mechanisms that have jointly created and observed events. This philosophy is important because we are certain about the phenomenon under study at sampled locations, except for measurement errors, but, in between the sampled, we become uncertain about how the phenomenon behaves. Geostatistical uncertainty characterization is to generate random numbers in such a way that they simulate the outcomes of the random processes that created the existing sample data. This set of existing sample is viewed as a partially sampled realization of that random function model. The random function’s spatial variability is described by a variogram or covariance model. The realized surfaces need to honour sample data at their locations, and reflect the spatial structure quantified by the variogram models. They should each reproduce the sample histogram representative of the whole sampling area. This paper will review the fundamentals in stochastic simulation by covering univariate and indicator techniques in the hope that their applications in geospatial information science will be wide-spread and fruitful. Supported by the National 973 Program of China (No. 2006CB701302).     

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.     

基于地统计学空间插值法的作物单产估算

针对第三次全国农业普查农产量抽样调查的样本调查方法改革后,现行的传统农作物单产抽样调查估算方法无法有效利用调查样本所包含的空间差异性来估算抽样设计中子总体的作物单产等问题,提出了一种基于地统计学的空间插值估算方法,对调查队取得的样本数据进行深度挖掘。以河南省辉县为研究区,以冬小麦单产为研究对象,进行实验和结果分析。结果表明,利用地统计学克里格插值法取得的村级冬小麦平均单产估算精度在90%以上的村达到研究区村总量的91%,且其中83%的村估算精度优于95%;估算精度在90%以下的所有村的冬小麦种植总面积仅占全县的2.26%,对全县产量的影响微乎其微。基于地统计学的空间插值法很好地分析和利用了样本属性中的冬小麦单产信息表现出的空间相关性和异质性,不仅能较高精度地估算出现行的传统农作物单产推算方法无法给出的抽样设计中子总体(全县各村)的单产信息,而且能较好地给出总体(全县)的单产信息。利用该方法得到的全县冬小麦平均单产估算精度达到97.75%,高于现行的传统农作物单产抽样调查估算精度,估算效果良好,方法可行性高,相对传统方法还可起到费省效宏的作用。     

地统计学方法研究与应用——以甘肃省为例

以甘肃省53个气象台站多年平均降水量和蒸发量为研究对象,运用地统计学分析方法,通过比较分析,分别采用指数模型和球状模型对降水量和蒸发量的半变异函数进行了拟合,并且应用克立格插值生成了年降水量与蒸发量的空间分布图,直观分析了研究对象的空间变异规律。     

地统计学在地理信息系统教学中的地位与作用

地统计分析是空间分析的重要技术手段之一,当前不少地理信息系统(GIS)专业的学生对地统计分析课程不够了解和重视.为促进GIS专业学生对地统计分析课程的认识,推动地统计学的发展,作者根据多年从事地统计分析教学与科研的经验,并通过查阅大量国内外文献,从地统计分析与传统统计学,地理信息系统的区别与联系出发,详细地分析了地统计...     

模糊类别制图的空间统计学方法

类别地图是地理信息系统（ＧＩＳ）应用中所利用的重要数据类别。这类数据可以从摄影测量和遥感技术得到。用摄影测量方法（影像判读）制作的类别地图常以点、线和多边形的离散目标形式描述,而遥感图像分类方法输出的类别地图以连通光栅块形式表达。不论哪一种情况,在每一个多边形或者光栅块（即制图单元）中仅允许单一类别,边界内部非均匀性和模糊形已经被“过滤”了。这样的类别地图沿用了古曲脆集合论,因为每个制图单元只允许     

基于地统计学的江西省年降水量插值研究

本文选取江西省86个气象站点2008年的年降水量数据,基于地统计学理论对数据进行探索性空间数据分析,得出该组气象数据插值的理论最优模型。采用不同全局趋势及变差函数模型进行插值,通过交叉性检验证实了该理论模型的有效性。最终结果显示,采用3次函数剔除全局趋势并以球状模型作为变差函数模型的考虑高程的Co-Kriging方法插值结果最优。     

地统计学在测量中的应用与研究

地统计学是数学地质理论中的新兴边缘学科,它研究的是区域物理现象的空间变异性。地统计学的方法应用到测量中,是一种新的方法,可以用它的理论、方法和模拟技术去研究地理空间信息的分布特征、分布规律,模拟它们的离散性和波动性,很好为决策服务。该文系统地介绍地统计学的基本理论、方法及对地统计学应用到测量当中进行了研究,并证实了地统计学应用到测量中的可行性。     

基于GIS和地统计学的地理国情监测成果制图应用研究

在全国范围开展的地理国情监测是对国家基础地理信息的动态更新,对监测数据的科学分析及成果展示有利于提升数据的使用价值。本文论述了利用GIS空间分析功能和地统计学原理,对国情监测数据的变化信息进行综合制图,为地理国情监测专项成果的可视化表达提供了新的方法。     

基于GIS的城市空间扩展预测

高速的城市扩展给社会发展带来了无比的活力。但是,也带来了一系列影响社会经济可持续发展的问题。因此,建立城市扩展预测模型对城市空间扩展预测有着实际的意义。本文主要是根据射线预测法的相关理论,使用Map Basic编程和Map Info软件进行相关操作,对济南市进行城市空间扩展预测并对预测进行分析,验证射线预测法的准确性。     

西安市住宅价格空间结构和分异规律分析

利用ESDA方法对西安市城区的291个普通住宅项目均价数据进行研究,通过计算Moran指数和半变异函数分析了其空间自相关性和变异性,并进行了趋势分析。应用Kriging空间插值方法对西安市普通住宅价格空间分布进行了模拟。研究结果表明:西安市房价存在显著的空间自相关性,大部分住宅价格呈空间集聚格局,少部分因存在空间异质性而呈离散分布;房价变异函数表现出各向异性,不同方向有不同结构特征,空间自相关尺度为14.2km;西安市房价空间分异规律明显,房价分布格局受城市功能区划和交通影响较大。     

基于地统计学和克里金法的水下趋势面分析

介绍了地统计学和克里金法对水深数据进行分析的方法和原理,结合工程实践,分析了水深值在沿垂直河道方向具有的空间自相关性,根据半变异函数模型,建立了水下趋势面模型,预测了未采样区域的水深值,并对结果进行了检核。实例分析结果表明:地统计学和克里金法在研究水深数据的空间格局和预测未知采样数据方面具有较强的优势,并能建立满足后续专业应用需求的水下趋势面模型。     

空间水汽场重建算法对比研究

GPS水汽探测可精确获取某点处的水汽数据,但实际应用中所需多是空间水汽场数据,因此有必要研究如何利用点位水汽数据来重建空间水汽场.文章探讨了反距离权重法、样条函数法、克里格法等常见空间插值算法在空间水汽场重建中的应用,并用交叉验证法对结果进行比较分析,试验结果表明克里格法最优.本文还利用DEM辅助重建空间水汽场,探讨了协同克里格法、泛克里格法等算法,并用Onn模型和高度归算法对克里格模型进行改进.交叉验证结果表明利用DEM辅助重建空间水汽场效果明显,基于Onn模型改进的泛克里格算法最优,其次是基于高度归算法的克里格模型.     

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。     

Kriging空间分析法及其在地价评估中的应用

介绍了Kriging空间分析法在地价评估中的应用,并以武汉市住宅地价评估为例,证明了该方法的可靠性。     

Increased accuracy of geostatistical prediction of nitrogen dioxide in the United Kingdom with secondary data

Five techniques were used to map nitrogen dioxide (NO₂) concentrations in the United Kingdom. The methods used to predict from point data, collected as part of the UK NO₂ diffusion tube network, were local linear regression (LR), inverse distance weighting (IDW), ordinary kriging (OK), simple kriging with a locally varying mean (SKlm) and kriging with an external drift (KED). These techniques may be divided into two groups: (i) those that use only a single variable in the prediction process (IDW, OK) and (ii) those that make use of additional variables as a part of prediction (LR, SKlm and KED). Nitrous oxides emission data were used as secondary data with LR, SKlm and KED. It was concluded that SKlm provided the most accurate predictions based on the summary statistics of prediction errors from cross-validation.     

The framework of a geospatial semantic web-based spatial decision support system for Digital Earth

Abstract

While significant progress has been made to implement the Digital Earth vision, current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to integrate data and models for simulating social and physical processes. To achieve effectiveness of decision-making using Digital Earth for understanding the Earth and its systems, new infrastructures that provide capabilities of computational simulation are needed. This paper proposed a framework of geospatial semantic web-based interoperable spatial decision support systems (SDSSs) to expand capabilities of the currently implemented infrastructure of Digital Earth. Main technologies applied in the framework such as heterogeneous ontology integration, ontology-based catalog service, and web service composition were introduced. We proposed a partition-refinement algorithm for ontology matching and integration, and an algorithm for web service discovery and composition. The proposed interoperable SDSS enables decision-makers to reuse and integrate geospatial data and geoprocessing resources from heterogeneous sources across the Internet. Based on the proposed framework, a prototype to assist in protective boundary delimitation for Lunan Stone Forest conservation was implemented to demonstrate how ontology-based web services and the services-oriented architecture can contribute to the development of interoperable SDSSs in support of Digital Earth for decision-making.     

统计遥感空间基础框架的研究与应用

研究通过建立多尺度、多分辨率,多种类的空间基础框架数据库体系,根据地理空间位置实现统计信息资源的整合与共享.针对统计遥感业务对空间基础框架的需求,提出了统计遥感空间基础框架建设的目标与任务,研究了框架建设中框架构成,数据体系、标准体系、技术体系和组织运行体系等关键技术,并展示了统计遥感空间基础框架在国家统计遥感粮食作物...     

A comprehensive framework for exploratory spatial data analysis: Moran location and variance scatterplots

Abstract

A significant Geographic Information Science (GIS) issue is closely related to spatial autocorrelation, a burning question in the phase of information extraction from the statistical analysis of georeferenced data. At present, spatial autocorrelation presents two types of measures: continuous and discrete. Is it possible to use Moran's I and the Moran scatterplot with continuous data? Is it possible to use the same methodology with discrete data? A particular and cumbersome problem is the choice of the spatial-neighborhood matrix (W) for points data. This paper addresses these issues by introducing the concept of covariogram contiguity, where each weight is based on the variogram model for that particular dataset: (1) the variogram, whose range equals the distance with the highest Moran I value, defines the weights for points separated by less than the estimated range and (2) weights equal zero for points widely separated from the variogram range considered. After the W matrix is computed, the Moran location scatterplot is created in an iterative process. In accordance with various lag distances, Moran's I is presented as a good search factor for the optimal neighborhood area. Uncertainty/transition regions are also emphasized. At the same time, a new Exploratory Spatial Data Analysis (ESDA) tool is developed, the Moran variance scatterplot, since the conventional Moran scatterplot is not sensitive to neighbor variance. This computer-mapping framework allows the study of spatial patterns, outliers, changeover areas, and trends in an ESDA process. All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb© (or, in the near future, myGeooffice.org).