香港附近海区台风波浪的波群统计分析

根据香港附近海区１９８５，１９８６年两次台风大浪的实测资料，以有效波高为相应水平，对波群作统计分析，得出可供海洋工程参考的波群连长、波群重复长度、波群出现频率以及波群中最大波高与有效波高的关系等。并将统计值与理论值作比较，结果表明：统计值大于“连”的理论值，而与包线理论中采用合田修正公式的汁算值相近。     

Optimum Sample Size to Detect Perturbation Effects: The Importance of Statistical Power Analysis – A Critique

Abstract. The current article describes statistical power analysis as an efficient strategy for the estimation of the optimum sample size. The principle aim is constructively to criticise and enrich the results presented by Mouillot et al. (1999) , who estimate the optimum sample size for evaluating possible perturbations. The authors did not make any reference to statistical power analysis, even though their objective clearly went beyond a simple stock evaluation to assess management strategies in a particular marine ecosystem. Surprisingly, they proposed (a priori) an ANOVA design to test a hypothesis considering both space and temporal scales. However, the authors did not cover important topics related with power analysis and the precautionary principle, both used into environment impact assessment programmes for marine ecosystems. Based on their results and on statistical power analysis, it is demonstrated that the variability (dispersion statistics), a key factor they used to estimate the sample size, is less relevant than the magnitude of perturbation (effect size). Therefore, a greater effort must be devoted to estimate the effect size of a particular phenomenon rather than a desired variability.     

Heavy metals in marine sediments of Taranto Gulf (Ionian Sea, Southern Italy)

Al, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Ti and Zn concentrations have been determined in surface sediment samples collected in the Taranto Gulf (Ionian Sea, Southern Italy) in order to evaluate their levels and spatial distribution in this important area of the Mediterranean Sea. For various metals, the geoaccumulation index has been calculated as a criterion to assess if their concentrations represent contamination levels or can be considered as background levels. The results show that metals concentrations in sediments can be considered near the background levels found in the Mediterranean Sea.Analytical results have been elaborated by using a Geographical Information System (GIS) software to show metals accumulation areas. Using multivariate statistical analysis, we evaluate the possibility to distinguish the sampling stations, in relation to their geographical position. Results have showed metals distribution in the Taranto Gulf is principally influenced by industrial and urban wastes, located mostly in the northern coastal area of the Ionian Sea. Rivers in the Basilicata region and prevailing anticlockwise marine currents are further factors influencing metal accumulation in sediments.     

基于GIS空间统计分析的广电收视率评估研究——以北京市广电收视覆盖分析系统为例

本文将GIS空间信息统计分析手段应用于广播电视收视率的评估,以层次分析法为基础进行改进,采用抽样调查的方法确定权重,提出较为合理的基于空间信息统计方法的广电人口覆盖分布模型并加以软件实现。以北京市为研究区域,经导入多个版本人口数据实验,发现这种评估方式较好地修正了传统的仅仅按照面积评估覆盖率的评估模型,对于提高广电行业的信息化管理水平具有一定意义。     

基于GIS的区域经济统计分析系统的设计与实现

随着经济的发展,区域间经济相互作用和影响的关系越来越密切,忽略空间相互作用的传统区域经济分析手段和方法开始显现出一些明显的不足。而GIS在处理和分析区域地理数据上具有强大的空间分析能力,为此我们引入GIS工具对区域经济进行分析,相较于传统的分析手法有了很大改善。本文以江西省区域经济数据为例,设计了一个基于GIS的区域经济统计分析系统,并利用Arcob-jects二次开发控件结合VB可视化编程语言,实现了大部分区域经济统计分析功能,最后并将结果进行网络发布。研究结果表明将GIS引入到区域经济统计分析中,更能正确、有效的揭示区域经济发展在空间上的联系,为区域经济研究决策提供服务。     

TOWARD ESTABLISHING THE CONCEPT OF PHYSICAL URBAN AREA IN CHINA

I.IntroduchollWiulegeOgraphystudiestheterritoria1systemsabouttherelationsbforeenhumanbeingSandthenature[I4],urbanmpaphyfocusesspecificallyonthespatialoopltizationandprocessofurbanterritory,anditsinteraCtionwiti1Otherterritorialsystems.Usually,wedoresearch…     

Analysis of the effect of geology, soil properties, and land use on groundwater quality using multivariate statistical and GIS methods

The regional survey of groundwater used as a small water supply system was performed to know the effect of geology, soil properties and land use on groundwater quality at Nonsan City, Korea. A total of 126 groundwater samples were collected and analyzed at the study area. The multivariate statistical methods, principal components analysis and discriminant analysis, and GIS technique were used for the quantitative interpretation of groundwater quality. The study area is mainly composed of Precambrian gneiss, Jurassic granite, and Cretaceous volcanics, and metasedimentary rocks of the Ogcheon zone. The land use was grouped as paddy, upland, grassland, resident, point source, industrial area, and water system. The soil properties were classified as 4 major groups, Entisols, Alfisols, Inceptisols, and Ultisols, by the degree of development, and reclassified as 11 subgroups. The modified and simplified geologic map, soil map, and land use map were made by using ARCGIS soft-ware. The area of geology, soil property, and land use affecting the groundwater quality for each well were also calculated by ARCGIS soft-ware to acquire the quantitative parameters for multivariate statistical analysis. The monitoring results of groundwater in the study area showed that 13%-21% of the groundwater samples exceeded the portable water guideline and the main causes were turbidity, bacteria, arsenic and nitrate-N. The spatial distribution of each component showed the close relationship between groundwater quality and geology reflecting the topography, land use.     

Bayesian Spatial Modelling of Gamma Ray Count Data

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.