基于名义尺度的空间自相关指数测度方法研究*

从空间自相关的基本构成出发,提出了一种测度名义尺度的空间自相关指数——自邻接指数,给出了基于名义尺度下全局空间自相关和局部空间自相关的测度方法,并在ArcView平台下通过Avenue二次编程进行了算法实现。最后通过例子展示了自邻接指数在土地利用格局分析中发挥的重要作用。     

基于名义尺度栅格数据空间自相关测度初探

空间自相关是地理信息科学目前研究的热点问题,作为空间数据挖掘的一种手段,它着重分析了空间实体的聚集程度,阐释了事物之间普遍联系的准则。值得注意的是,目前空间自相关的研究多以空间实体定量化属性为对象,针对定性表达数据测度的空间自相关研究稍显不足。本文以名义尺度下的栅格数据作为研究对象,通过定义空间自邻接指数提出一种空间自相关测度算法,并将其应用到土地利用数据分析中得到了较好的试验结果。     

基于邻接关系的空间数据挖掘

空间邻接关系是空间数据库对象之间的特征联系，其处理过程直接影响着空间数据挖掘算法的实现与效率，基于3种邻接关系，给出了邻接图，邻接路径的概念和几个基本操作，并分析了几种典型的空间数据挖掘算法。     

基于邻接关系的空间数据挖掘技术的研究

随着现代科学技术的迅速发展,复杂多变的空间数据日益膨胀,远远超出人们的解译能力,迫切地需要数据挖掘和知识发现为其提供知识。文中从空间数据挖掘的基本概念出发,详细阐述了空间数据的特点、空间邻接关系及其相关操作,并针对空间邻接关系给出了几种典型的空间数据挖掘方法。     

基于邻接关系的空间数据挖掘技术的研究

随着现代科学技术的迅速发展，复杂多变的空间数据日益膨胀，远远超出人们的解译能力，迫切地需要数据挖掘和知识发现为其提供知识。文中从空间数据挖掘的基本概念出发，详细阐述了空间数据的特点、空间邻接关系及其相关操作，并针对空间邻接关系给出了几种典型的空间数据挖掘方法。     

空间数据尺度的概念与逻辑小议

运用空间数据尺度有时会遇到麻烦的概念和逻辑问题,必须仔细严格划定数据的名义尺度、有序尺度、间隔尺度和比率尺度,才会避免自我矛盾。     

面向影像分割的多尺度快速区域合并方法

影像分割的区域合并技术中,传统的采用区域邻接图的方法存在着数据结构和算法复杂、难以扩充和维护、可考虑的特征因子有限以及空间浪费严重等问题。对此,提出了一种新的区域合并方法,提供了一套采用面向对象技术解决区域合并问题的新框架。在该框架下,区域的相异度指标、属性、邻接关系和行为可根据需要自由定义、扩充和修改,算法的稳定性和可维护性得到提升,合并过程被充分简化。在此基础上提出了多尺度合并区域的改进方法,并对等级队列的构建机制进行了优化。最后通过多尺度的对比实验,证明该方法不但能保证区域合并的精度,而且可以显著提高执行效率。     

基于邻接关系的空间聚类算法研究

聚类指的是把数据库里的对象分组成有意义的子集,使得一个聚类内的成员尽可能相似,而不同聚类间的成员差异尽可能大。空闻对象的主要特性受其邻接对象的影响,并且随着距离的增加或减少,影响作用也相应地增加或减少。论文针对相邻空间对象的特性总是相似或相关联的特点,以邻接关系为基础对空间聚类算法进行了分析与研究。     

基于Voronoi和空间自相关的离群点检测

为了提高空间数据挖掘的效率和准确度,在分析传统的离群点检测算法优、缺点的基础上,提出一种空间离群点检测算法。用Voronoi来确定空间对象间的邻近关系,在空间邻域内利用空间自相关性来计算局部Moran指数,并将其作为离群因子进而判断离群点。实验结果表明,该算法能够高效、准确地检测出空间离群点,具有对用户依赖性少和可伸缩性强等优点。     

基于Voronoi和空间自相关的离群点检测

为了提高空间数据挖掘的效率和准确度,在分析传统的离群点检测算法优、缺点的基础上,提出一种空间离群点检测算法。用Voronoi来确定空间对象间的邻近关系,在空间邻域内利用空间自相关性来计算局部Moran指数,并将其作为离群因子进而判断离群点。实验结果表明,该算法能够高效、准确地检测出空间离群点,具有对用户依赖性少和可伸缩性强等优点。     

Multiscale assessment of green leaf cover in a semi-arid rangeland with a small unmanned aerial vehicle

Spatial variability in green leaf cover of a semi-arid rangeland was studied by comparing field measurements on 50 m crossed transects to aerial and satellite imagery. The normalized difference vegetation index was calculated for 2 cm resolution images collected with a multispectral digital camera mounted on a radio-controlled helicopter, as well as a 30 m resolution Landsat Thematic Mapper image. Variograms of green cover from these two sources show that the range of influence for spatial autocorrelation extended to a distance of approximately 200 m. Field transects that are much smaller than the extent of this spatial autocorrelation are more likely to fall within local deviations from the mean landscape condition. A sampling scheme that exceeds the spatial scale of these localized deviations is shown to reduce sample variance and require fewer sampling locations to reach a given level of measurement uncertainty. The time and cost of more spatially extensive sampling at each location may be less than deploying to a larger number of locations with smaller transects, and unmanned aerial vehicles may be a valuable tool in extending current field sampling strategies for quantifying the health of shrub-dominated rangelands.     

Assessing the spatial sensitivity of a random forest model: Application in gridded population modeling

Gridded human population data provide a spatial denominator to identify populations at risk, quantify burdens, and inform our understanding of human-environment systems. When modeling gridded population, the information used for training the model may differ in spatial resolution than what is produced by the model prediction. This case arises when approaching population modeling from a top-down, dasymetric approach in which one redistributes coarse administrative unit level population data (i.e., source unit) to a finer scale (i.e., target unit). However, often overlooked are issues associated with the differing variance across the scale, spatial autocorrelation and bias in sampling techniques. In this study, we examine the effects of intentionally biasing our sampling from the source to target scale within the context of a weighted, dasymetric mapping approach. The weighted component is based on a Random Forest estimator, which is a non-parametric ensemble-based prediction model. We investigate issues of autocorrelation and heterogeneity in the training data using 18 different types of samples to show the variations in training, census-level (i.e., source) and output, grid-level (i.e., target) predictions. We compare results to simple random sampling and geographically stratified random sampling. Results indicate that the Random Forest model is sensitive to the spatial autocorrelation inherent in the training data, which leads to an increase in the variance of the residuals. Sample training datasets that are at a spatial scale representative of the true population produced the best fitting models. However, the true representative dataset varied in autocorrelation for both scales. More attention is needed with ensemble-based learning and spatially-heterogeneous data as underlying issues of spatial autocorrelation influence results for both the census-level and grid-level estimations.     

尺度分布的Getis统计对遥感图像特征参量空间自相关性的研究

分析了Getis统计中空间尺度设定对于标准Z值的影响，提出用变化的空间尺度设定来进行Gctis空间相关性的计算，直观反映遥感特征参量的空间分布，并用被动微波遥感SSM／I（special sensor microwave／imager）的辐射亮度温度数据进行了验证分析。进一步地，针对Getis标准Z值分布不能评估中值聚类的缺点，提出了多层尺度分布的Getis空间自相关性统计方法，用模拟的图像和主动微波遥感ERS-2SAR（European Remote Sensing-2 Synthetic Aperture Radar）数据进行了验证分析，结果证明多层尺度分布的Getis能够比较全面地反映地表特征参量分布的空间信息。     

Spatial structure,sampling design and scale in remotely-sensed imagery of a California savanna woodland

Abstract

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.     

Modelling small area counts in the presence of overdispersion and spatial autocorrelation

The problems arising when modelling counts of rare events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present or anticipated are considered. Different models are presented for handling inference in this case. The different strategies are implemented using data on offender counts at the enumeration district scale for Sheffield, England and results compared. This example is chosen because previous research suggests that social processes and social composition variables are key to understanding geographical variation in offender counts which will, as a consequence, show evidence of clustering both at the scale of the enumeration district and at larger scales. This in turn leads the analyst to anticipate the presence of overdispersion and spatial autocorrelation. Diagnostic measures are described and different modelling strategies are implemented. The evidence suggests that modelling strategies based on the use of spatial random effects models or models that include spatial filters appear to work well and provide a robust basis for model inference but gaps remain in the methodology that call for further research.     

Identifying an optimal analysis level in multiscalar regionalization: A study case of social distress in Greater Santiago

Assembling spatial units into meaningful clusters is a challenging task, as it must cope with a consequential computational complexity while controlling for the modifiable areal unit problem (MAUP), spatial autocorrelation and attribute multicolinearity. Nevertheless, these effects can reveal significant interactions among diverse spatial phenomena, such as segregation and economic specialization. Various regionalization methods have been developed in order to address these questions, but key fundamental properties of the aggregation of spatial entities are still poorly understood. In particular, due to the lack of an objective stopping rule, the question of determining an optimal number of clusters is yet unresolved. Therefore, we develop a clustering algorithm which is sensitive to scalar variations of multivariate spatial correlations, recalculating PCA scores at several aggregation steps in order to account for differences in the span of autocorrelation effects for diverse variables. With these settings, the scalar evolution of correlation, compactness and isolation measures is compared between empirical and 120 random datasets, using two dissimilarity measures. Remarkably, adjusting several indicators with real and simulated data allows for a clear definition of a stopping rule for spatial hierarchical clustering. Indeed, increasing correlations with scale in random datasets are spurious MAUP effects, so they can be discounted from real data results in order to identify an optimal clustering level, as defined by the maximum of authentic spatial self-organization. This allows singling out the most socially distressed areas in Greater Santiago, thus providing relevant socio-spatial insights from their cartographic and statistical analysis. In sum, we develop a useful methodology to improve the fundamental comprehension of spatial interdependence and multiscalar self-organizing phenomena, while linking these questions to relevant real world issues.     

基于同步实测光谱数据的ASTER影像邻近效应校正研究

2008年6月在甘肃省张掖市开展的天-空-地同步大型遥感实验“黑河综合遥感联合实验”中,用自己设计的装置在兰州大学草地观测站开展了卫星同步邻近效应测量实验,用逆最小二乘算法求算了邻近效应校正系数,并将系数应用于影像真实反射率转换方程,对同步获取的ASTER影像进行了邻近效应校正,校正结果影像质量有所改善。将校正的结果影像与SHDOM方程的邻近效应校正结果影像对比,结果表明,经过邻近效应校正系数校正后影像的反射率、归一化植被指数产生较大差异,像元之间的相关性也明显降低。     

基于随机灰度图的空间自相关指数的计算与分析

空间自相关指数体现了地理元素间自相关的程度。本文基于随机灰度图设计实验,借助计算机技术,编程完 成各类空间自相关指数的计算,并对计算结果进行分析,验证各指数探测聚集性的能力。