日本囊对虾(Marsupenaeus japonicus)3个野生种群和1个养殖种群的形态差异与判别分析

采用3种多元分析方法,对3个野生种群和1个养殖种群的13个形态比例参数进行比较研究。聚类分析结果表明,广东湛江和海南临高的种群形态最接近,福建厦门种群的趋异程度最大。主成分分析构建了6个主成分,其贡献率分别为:第1主成分23.084%,第2主成分17.976%,第3主成分15.321%,第4主成分13.84%,第5主成分12.707%,第6主成分7.770%,累积贡献率为90.699%。逐步判别选入6个贡献率较大的性状进行判别分析,F检验结果显示,4个种群形态差异显著(P0.01);同时建立了4个种群的判别函数,其判别准确率P1、P2均为100%。综合判别率为100%。3种多元分析结果均认为,4个种群日本囊对虾在形态上已产生一定程度的差异,且集中表现在体重、头胸甲的性状上。     

Mapping the farming-pastoral ecotones in China

Conventional farming-pastoral ecotones methods of delineating were not quantitative and could not fully show their spatial distribution. The present paper attempts to develop quantitative methods for mapping farming-pastoral ecotones in China. Nine indicators, related to temperature, precipitation and altitude aspects, were selected to quantify ecological susceptibility of vegetation （crops and forage）. Methods of analytic hierarchy process （AHP） and expert score ranking combined with fuzzy set theory were applied to assign the weight for each indicator and to define the membership functions. The geographic information system （GIS） was used to manage the spatial database and conduct the spatial analysis. According to the spatial calculation of evaluation model integrated with GIS, the ecological susceptibility of vegetation （crops and forage） was mapped. Three different zones, pastoral area, farming-pastoral ecotones and farming area, were classified by spatial cluster analysis and the maximum likelihood classification for the numeric map of vegetation ecological susceptibility by GIS. This map was validated by the economic statistical result based on the ratio of the output value from animal husbandry in total output value of agriculture by the National Bureau of Statistics in China, indicating that the mapping of the farming-pastoral ecotones may be accepted.     

Multivariate analysis of fluorescence and source identification of dissolved organic matter in Jiaozhou Bay, China

Hierarchical clustering analysis and principal component analysis (PCA) methods were used to assess the similarities and dissimilarities of the entire Excitation-emission matrix spectroscopy (EEMs) data sets of samples collected from Jiaozhou Bay, China. The results demonstrate that multivariate analysis facilitates the complex data treatment and spectral sorting processes, and also enhances the probability to reveal otherwise hidden information concerning the chemical characteristics of the dissolved organic matter (DOM). The distribution of different water samples as revealed by multivariate results has been used to track the movement of DOM material in the study area, and the interpretation is supported by the results obtained from the numerical simulation model of substance tracing technique, which show that the substance discharged by Haibo River can be distributed in Jiaozhou Bay.     

Alternative Fuzzy Cluster segmentation of remote sensing images based on Adaptive Genetic Algorithm

Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.     

青州市表层土壤元素地球化学组合特征研究

聚类分析和因子分析可以获得土壤元素地球化学组合特征及其差异性。对青州市表层土壤样品数据进行分析研究,通过聚类分析,绘制表层土壤元素聚类谱系图,将23种元素或指标分为5个元素组合簇群及2个单元素簇,研究各元素间的组合特征,探讨其相关性、聚集性及其指示意义;通过因子分析,找出有代表性的因子,用其代表变量,绘制典型因子得分等值线图,并从中分析不同元素组合的区域分布基于何种因素,用11个代表性因子的分布特征就基本可以代表青州市表层土壤23项原始变量的分布特征,并对F1,F2,F3主因子进行了地质解释。聚类分析与因子分析相结合,利于表层土壤中元素的共生组合特征及其差异性研究,利于对研究区表层土壤异常进行归纳总结。     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

GeoSurveillance: a GIS-based system for the detection and monitoring of spatial clusters

This article introduces a software package named GeoSurveillance that combines spatial statistical techniques and GIS routines to perform tests for the detection and monitoring of spatial clustering. GeoSurveillance provides both retrospective and prospective tests. While retrospective tests are applied to spatial data collected for a particular point in time, prospective tests attempt to incorporate the dynamic nature of spatial patterns via analyzing time-series data to detect emergent clusters as quickly as possible. This article will outline the structure of GeoSurveillance as well as describe the statistical cluster detection methods implemented in the software. It concludes with an illustration of the use of the software to analyze the spatial pattern of low birth weights in Los Angeles County, California.      

全球地理信息系统(G2IS)架构体系初探

随着人们对全球GIS的需求不断增加,现有的地理信息系统在技术上已不能适应,因此,建设新一代全球GIS成为当前GIS发展的客观要求。本文基于地球剖分理论,以计算机集群技术、时空一体化技术和虚拟现实技术为辅助技术,提出了关于全球地理信息系统(G2IS)的架构体系的初步设想,认为新一代全球地理信息系统将成为GIS发展的一个重要方向。     

输水管网模糊地震可靠性的 Monte Carlo 法分析程序

本文详细介绍了笔者所建议的运用模糊图理论和方法进行城市输水管网的地震可靠性分析途径和计算程序。对于输水管网中每段管道的震害预测,建立了相应的专家系统。在此基础上,定义了管网系统的模糊可靠性,更好地反映了人们对于管道损坏状态估计的模糊性和对可靠性要求上的模糊性。以模糊矩阵来描述输水管网的状态是十分方便的。通过计算模糊矩阵的传递闭包,极易找出最短路、关键段等。由于地震作用和管道抗震能力都是随机的,因此整个管网系统的可靠性分析是藉助Monle Carlo法进行的。以Monle Carlo法对模糊图进行分析,从而得到管网的地震可靠性,这还是首次。用PASCAL和PROLOG编制了在微机上实用的计算机程序,特别是以智能语言PROLOG编制的程序,更有许多特色。由于采用了一些技巧,使在微机上可以分析相当大的系统,为城市抗震防灾工作提供了一个有力的工具。     

建议一个弥补震害资料不足的方法

本文用模糊贴近度原则作类比,提供了一种弥补震害预测时历史地震资料不足的方法,途径是将一个烈度之下的震害经验推广到其他烈度上去用。实践证明,用这种建立在近似推理基础之上的方法推导出来的结果,在总体趋势上和真实情况完全一致。