澜沧江流域(云南段)人口密度空间自相关分析

本文介绍了空间自相关的理论方法,利用澜沧江流域(云南段)人口分布数据计算了MoranI和Local Moran I.详细论述了自相关分析的技术路线,对人口分布的空间特征进行研究探讨,把GIS技术的空间拓扑关系及可视化制图功能等很好地融合于地理现象的空间模式中,分析了研究区域人口分布的时空特征,反映了该区域人口地理分布的...     

基于空间自相关的乡镇经济差异分析

应用GIS和空间自相关分析技术,对2006年瓦房店市27个乡镇的GDP数据的空间差异进行研究,揭示了瓦房店市乡镇经济的空间自相关和集聚特征。研究表明,瓦房店市各乡镇GDP数据存在空间相关性,有明显的空间集聚;Moran散点图多位于第三、四象限,高低集聚、低低集聚较多,显示出经济发展的不平衡;市中心及沿海地区经济较发达,而北部内陆地区经济发展缓慢,应加大对北部乡镇的经济扶持力度。     

广东省县域经济实力差异空间自相关分析

以2005年广东省87个县（市、区）级区域为研究对象,选取总人口、国民生产总值等8个指标作为评价因子,通过因子分析和主成分分析法获得各县级单元经济实力综合得分。对综合得分为变量进行空间自相关分析,计算得出全局相关Moran＇s值和LISA值。结果显示：广东省的县域经济具有明显的空间集聚特征,86.21%的县市经济表现为空间正相关,但仍有少部分县市的经济发展存在异质性。     

地球化学的空间自相关异常信息提取方法

针对地球化学数据存在的空间分布相关性特征,该文提出了一种基于空间自相关统计的地球化学异常提取方法。以内蒙古浩布高矿床外围的土壤地球化学数据为例,通过对Sn、Cu元素地球化学数据在不同空间间隔上的全局自相关计算,测算其空间聚集的程度,选取聚集程度最高时的间隔距离作为局部空间自相关的参数,通过局部Moran’s I值研究元素的空间分布特征,分析空间聚类和异常值,从而提取地球化学异常。结果表明,局部空间自相关分析可以揭示Sn、Cu地球化学数据的空间分布特征,能够更好地提取地球化学弱缓异常,说明空间自相关是一种有效的地球化学异常识别方法。     

DEM误差的空间自相关特征分析

采用空间自相关分析方法,从空间角度对数字高程数据误差的空间分布特征进行了研究。实验表明,利用双线性曲面表示地形表面时,产生的数字高程数据误差的全局Moran’sI指数趋近于0,在整个区域单元上的分布不存在显著的全局空间自相关,邻近区域单元上高程数据误差之间的关系在整体上既不综合表现为趋同,也不综合表现为趋异,高程数据误差的整体空间格局为随机格局;而且数字高程数据误差在空间上的分布与地形坡度和地表粗糙度有一定的联系,一般情况下,平均坡度、地表粗糙度越大,高程数据的全局Moran’sI指数偏离0稍远一些;否则,距离0近一些,但全局空间自相关仍不显著,在整体上表现为随机格局。     

基于空间自相关的河南省城镇化水平空间格局研究

城镇化水平是衡量一个地区经济发展状况的重要指标。本文基于空间自相关分析方法,利用2007年河南省各市城镇化数据,分析了城镇化水平的空间分布规律。结果表明,河南省城镇化水平全局Moran’sI指数为0.299,达到显著正相关,呈现明显的全局空间集聚现象;其次,河南省城镇化水平分布的局部空间集聚现象亦十分显著。总体上,城镇化水平分布呈正关联的地市数量明显高于呈负关联的数量。根据计算结果,提出了促进河南省城镇化水平快速全面发展的对策和建议。     

区域城镇基准地价水平的空间自相关格局分析

研究了区域城镇基准地价水平的空间自相关分析方法。采用Moran’s I系数、局域Moran’s I系数、Moran’s I系数曲线分析了区域内城镇基准地价的空间自相关程度、局域聚集特征和多阶邻域空间自相关性变化特征;提出了一种基于路径间隔的半方差统计方法进行基准地价水平的空间变异尺度分析;构造了基于空间属性复合距离统计量的空间聚类方法,并利用该方法进一步揭示了城镇地价水平的空间聚集分布规律。以湖北省为例进行了实例分析。     

利用影像相关分析的遥感变化检测

针对利用单一光谱特征进行影像相关分析在遥感变化检测应用中效果较差的问题,提出了一种将纹理特征与光谱特征相结合的相关系数计算模型。应用中在对多时相影像进行相同尺度棋盘分割的基础上,先计算各对应分割窗口内的相关系数,再以其中心点的坐标和相关系数值作为一个特征点,在三维空间中进行插值处理得到整个区域的相关系数空间分布图;进一步通过密度分割处理提取变化信息。本文以两期GF-1影像数据进行了变化信息提取试验,结果表明,采用组合特征相关系数的变化检测结果明显优于单一光谱相关系数的变化检测结果。组合相关系数的应用研究为利用影像相关分析方法从高分影像中提取变化信息提供了一种新的思路。     

基于ArcGIS的空间自相关分析模块的开发与应用

针对目前GIS的空间分析能力,利用ArcObjects开发了空间自相关分析模块。模块包括空间权值矩阵建立、全局空间自相关分析、局部空间自相关分析三方面的功能,并可以嵌入到ArcGIS系统中。论文介绍了空间自相关分析的基本概念、空间自相关分析模块的设计与开发,并演示了模块的应用。     

空间权重矩阵对空间自相关的影响分析——以我国肾综合征出血热疾病为例

应用空间自相关统计方法,分析了2008年我国肾综合征出血热（HFRS）发病率的空间分布。采用多种权重度量计算了全局和局部两种相关性指数,分析了自相关数值对空间权重矩阵的依赖性。分析结果表明：①空间距离矩阵比空间邻接矩阵能更好地度量HFRS的空间分布;②使用空间距离矩阵时,当距离阈值500km〈δ〈800km时,全国发病率数据显示出显著的空间自相关性;③从局域上看,吉林省高值显著聚集,新疆、西藏、青海、广西和海南省自身低值被高值包围聚集显著。     

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.     

基于局部空间统计分析的SPOT 5影像分类

受同物异谱和异物同谱现象的影响,对遥感影像进行分类时若仅利用光谱信息则分类精度的提高将会受到限制,而局部空间统计特征可以通过对地物空间聚集度的描述与分析在一定程度上减轻这种影响。本文研究了局部空间统计在不同指数(Moran’s I, Getis-Ord G_i, Geary’s C)、邻域规则和间隔距离下,对高空间分辨率的SPOT 5影像分类精度的影响规律。首先,对波段1进行局部空间统计分析,运算结果作为纹理波段添加到原始的光谱波段中;然后,综合利用光谱波段和纹理波段进行监督分类;最后,选取测试样本进行分类的精度评价,并比较分析不同条件下的分类精度,得到地物分类精度同参数之间的关系与规律。通过分析可以得出Getis-Ord G_i指数对于总体分类精度的提高最理想,总体分类精度从 87.74%提高到95.12%。     

Global and local spatial autocorrelation in bounded regular tessellations

This paper systematically investigates spatially autocorrelated patterns and the behaviour of their associated test statistic Moran's I in three bounded regular tessellations. These regular tessellations consist of triangles, squares, and hexagons, each of increasing size (n=64; 256; 1024). These tesselations can be downloaded at http://geo-www.sbs.ohio-state.edu/faculty/tiefelsdorf/regspastruc/ in several GIS formats. The selection of squares is particularly motivated by their use in raster based GIS and remote sensing. In contrast, because of topological correspondences, the hexagons serve as excellent proxy tessellations for empirical maps in vector based GIS. For all three tessellations, the distributional characteristics and the feasibility of the normal approximation are examined for global Moran's I, Moran's I ^(k) associated with higher order spatial lags, and local Moran's I _i. A set of eigenvectors can be generated for each tessellation and their spatial patterns can be mapped. These eigenvectors can be used as proxy variables to overcome spatial autocorrelation in regression models. The particularities and similarities in the spatial patterns of these eigenvectors are discussed. The results indicate that [i] the normal approximation for Moran's I is not always feasible; [ii] the three tessellations induce different distributional characteristics of Moran's I, and [iii] different spatial patterns of eigenvectors are associated with the three tessellations. Received: 2 July 1999 / Accepted: 9 November 1999     

Hidden negative spatial autocorrelation

Mostly lip service treatments of negative spatial autocorrelation (NSA) appear in the literature, although spatial scientists confront it in practice. NSA was detected serendipitously in recalcitrant empirical analyses containing a sizeable amount of global positive spatial autocorrelation (PSA) unaccounted for by standard spatial statistical models, and labeled hidden because conventional spatial statistical tools detected only PSA while giving absolutely not hint of NSA existing. The meaning of this phenomenon is explored empirically, with findings including: a better understanding of NSA, spatial filter model construction guidelines, effective illustrations of NSA, and how hidden NSA furnishes a diagnostic for model misspecification.

Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis

Traditional methods of recording fire burned areas and fire severity involve expensive and time-consuming field surveys. Available remote sensing technologies may allow us to develop standardized burn-severity maps for evaluating fire effects and addressing post fire management activities. This paper focuses on multiscale characterization of fire severity using multisensor satellite data. To this aim, both MODIS (Moderate Resolution Imaging Spectroradiometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been processed using geo-statistic analyses to capture pattern features of burned areas.Even if in last decades different authors tried to integrate geo-statistics and remote sensing image processing, methods used since now are only variograms, semivariograms and kriging. In this paper, we propose an approach based on the use of spatial indicators of global and local autocorrelation. Spatial autocorrelation statistics, such as Moran's I and Getis–Ord Local Gi index, were used to measure and analyze dependency degree among spectral features of burned areas. This approach enables the characterization of pattern features of a burned area and improves the estimation of fire severity.     

A linear regression solution to the spatial autocorrelation problem

The Moran Coefficient spatial autocorrelation index can be decomposed into orthogonal map pattern components. This decomposition relates it directly to standard linear regression, in which corresponding eigenvectors can be used as predictors. This paper reports comparative results between these linear regressions and their auto-Gaussian counterparts for the following georeferenced data sets: Columbus (Ohio) crime, Ottawa-Hull median family income, Toronto population density, southwest Ohio unemployment, Syracuse pediatric lead poisoning, and Glasgow standard mortality rates, and a small remotely sensed image of the High Peak district. This methodology is extended to auto-logistic and auto-Poisson situations, with selected data analyses including percentage of urban population across Puerto Rico, and the frequency of SIDs cases across North Carolina. These data analytic results suggest that this approach to georeferenced data analysis offers considerable promise. Received: 18 February 1999/Accepted: 17 September 1999     

无障碍设施空间布局核密度热点探测和空间自相关分析——以北京市核心区室外公共空间为例

城市无障碍设施在区域空间中往往呈现聚集分布的特征,通常利用核密度估计方法分析总体空间分布形态,研究区域空间分布的数量差异,探测分布热点;同时通过分析无障碍设施空间自相关性特征,反映无障碍设施服务的聚集特点。将空间分析方法引入到无障碍环境评估当中,可以优化无障碍环境发展空间。结果表明,北京市核心区无障碍设施总体呈现出"多核分布"的态势。无障碍设施的总体分布存在空间差异性,局部无障碍设施空间分布存在聚集特性。