空间自相关在区域经济统计分析中的应用

利用了空间自相关分析方法,分析了海南省区域经济的空间分布模式研究,结果表明其能很好地检测区域经济之间的空间关联模式。     

顾及空间自相关的统计数据分级质量评价

详细分析了统计地图数据分级质量的评价指标，研究了数据分级中应当考虑的数据空间分布规律，并用实例证明了分级数与空间自相关系数之间的变化规律。     

REVIEWS OF GEOGRAPHIC SOFTWARE

CHAOS CLlM 3.77 LP88 and TPR088 MAPEDIT PRODESIGN II RANDMAP SPATIAL AUTOCORRELATION SIMULATION GAME . Daniel A. Griffith STATA THINKTANK NOTEBOOK II     

Variograms and their role in interpretation of soft iron ore types

Difficulties are encountered in the interpretation of variograms from mixed populations. A case history study of a soft iron ore body capped by laterite demonstrates that the variations observed in variograms for different elements are caused mainly by intermixing of different lithologic units which are interstratified. In the case of soft iron ore, occasional bands of clay and blue dust affect significantly the variations observed for iron, silicon, and aluminum. In the future, it is recommended that the variograms produced reflect a single population.     

Analytical studies on some statistical properties of mean values and their applications to sampling in one dimension

This paper documents the results of investigations on the variance and covariance properties of mean values within the theory of stationary random functions under a variety of conditions. Analytical expressions have been developed in each case, to facilitate direct and ready applications of the results, using the exponential function as a working model for the covariance function. These properties, in turn, have been utilized in developing mathematical expressions for estimation accuracy (error variance) of the mean estimates for one-dimensional sampling plans. Two distinctly different sampling plans have been discussed: punctual sampling with no significant linear extensions of the samples and linear sampling with significant linear extensions of samples in the direction of the section being sampled.     

Standard cumulative semivariograms of stationary stochastic processes and regional correlation

The standard cumulative semivariograms (SCS), obtained analytically from the currently employed stationary stochastic processes, provide a basis for the model identification and its parameter as well as regional correlation estimations. The analytical solutions for different stationary stochastic processes such as independent (IP), moving average (MA), autoregressive (AR), and autoregressive integrated moving average ARIMA (1,0,1) processes give rise to different types of SCSs which can be expressed in terms of the autocorrelation structure parameters only. The SCSs of independent and MA processes appear as linear trends whereas other type of processes have SCSs which are nonlinear for short distances but become linear at large distances. Irrespective of the stationary stochastic process type the linear portions of SCSs have unit slopes. The vertical distance between these linear portions and that of the IP cumulative semivariogram (CS), provide an indicator for measuring the regional correlation. In the case of stationary processes, the straight line portions of any CS are parallel to each other. Hence, it is possible to identify the model from the sample CS. Finally, necessary procedures are provided for the model parameters estimation. The methodology developed, herein, is applied to some hydrochemical ions in the groundwater of the Wasia aquifer in central part of Kingdom of Saudi Arabia.     

基于ESDA-GIS的城镇群体空间结构

基于ESDA-GIS的空间分析框架,利用江苏省1346个小城镇的统计数据,对其城镇群体空间结构进行了研究。首先对统计数据进行了概括性因子分析,抽取的规模与经济因子值的频率分布都是偏态的,且两因子之间不存在规模-效益的正相关性。随后,对小城镇群体的空间结构进行了探索性分析,由密度图显示:小城镇空间分布不均衡,其密度由东南部的沿长江、环太湖地区向西和向北递减。通过空间自相关测度分析得出:小城镇的经济因子的空间分布具有正相关性,呈现出空间集聚的特征;规模因子的空间相关特征不明显。最后,将小城镇的局域空间自相关系数按县(市、区)行政单元进行聚类,利用方差图的结论修正聚类结果,得出江苏省小城镇经济发展类型分区:三区、一环(带)、一片,即苏北中部地区、苏中及宁镇区、苏锡常地区、苏北边缘环带、睢宁片区,对应的经济发展类型分别为:弥漫发展型、极核发展型、集群发展型、过渡发展型、持续贫困型。     

An efficient optimum interpolation scheme for objective analysis over Indian region

In the optimum interpolation scheme, the weights for the observations are computed by solving a set of linear equations for every grid point. As the number of observations increases particularly over data-rich regions, the matrix dimension increases and the computer time required to solve these equations to determine weights increases considerably. In order to reduce the computer time for computing the weights, Tanguay and Robert suggested schemes in which the gaussian function representing the autocorrelation function has been approximated by a second-order and also by a fourth-order Taylor series expansion. This resulted in the solution of matrices of order 4 or 9 respectively to obtain weighting functions irrespective of the number of observations used in the analysis. In the present study, the analyses of mean sea level pressure and geopotential height at 700 mbar level have been carried out for five days using the above two schemes and the regular OI scheme. The analyses are found to be similar in all the three cases suggesting that a lot of computer time could be saved without sacrificing the analysis accuracy by using the modified scheme in which the second-order approximation is utilized.