转型期上海社会空间分异研究

采用2000年第五次全国人口普查数据库中居民委员会尺度的数据,对转型期上海城市空间重构与分异展开研究。存在6类社会区：计划经济时代建设的工人居住区、外来人口集中居住区、白领集中居住区、农民居住区、新建普通住宅居住区、离退休人员集中居住区。通过计算分异指数,发现当前上海存在严重的住房分异;但并不存在明显的以社会经济属性为基础的社会空间分异。造成这一现象的原因在于计划经济时代的历史以及仍然存在的大型企事业单位对住房的影响。中国城市目前的社会空间分异在程度上与西方城市还有根本的差异。     

基于GIS的近海剩余环境容量计算模型

探讨一种基于GIS 克里格插值方法的剩余环境容量计算方法, 通过GIS 地统计分析插值模型将水质模拟问题转化为数学统计问题, 充分利用水质水体、沉积物监测数据, 采用克里格插值法对污染物浓度和水深在局部海域内进行插值, 将研究区域离散为一个个的均匀栅格单元, 比较每个单元污染物浓度与水质目标之间的差值, 从而估算得到研究海域的剩余环境容量。通过2009 年8 月锦州湾剩余环境容量的计算实例表明, 基于GIS 的剩余环境容量初步计算方法在资料缺乏地区是十分简便、有效的, 这为海域开发利用区环境管理与规划提供了参考。     

利用克里金技术标定新疆及附近地区P/S震相幅值比及其在地震事件识别中的应用

针对中国的乌鲁木齐台（WMQ）和哈萨克斯坦的马坎奇台（MAK）,采用贝叶斯克里金技术建立了新疆及附近地区地震事件的震相幅值比的校正曲面,并分析了校正曲面与地震波传播路径的关系,以及校正曲面对于改善震相幅值比判据识别效果的有效性.分析结果表明,建立的校正曲面基本合理,结合传播路径差异和地震波传播规律,能够较好地解释校正曲面的起伏特征. 而且,在传播路径差异较大,使得震相幅值比经震中距校正后离散度仍然较高的情况下,利用校正曲面修正震相幅值比,可以进一步降低其离散度,从而改善判据的识别效果.经校正曲面修正后,对天然地震的误识率由16.3%下降为5.2％.      

Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application

Abstract

The present research study investigates the application of nonlinear normalizing data transformations in conjunction with ordinary kriging (OK) for the accurate prediction of groundwater level spatial variability in a sparsely-gauged basin. We investigate three established normalizing methods, Gaussian anamorphosis, trans-Gaussian kriging and the Box-Cox method to improve the estimation accuracy. The first two are applied for the first time to groundwater level data. All three methods improve the mean absolute prediction error compared to the application of OK to the non-transformed data. In addition, a modified Box-Cox transformation is proposed and applied to normalize the hydraulic heads. The modified Box-Cox transformation in conjunction with OK is found to be the optimal spatial model based on leave-one-out cross-validation. The recently established Spartan semivariogram family provides the optimal model fit to the transformed data. Finally, we present maps of the groundwater level and the kriging variance based on the optimal spatial model.

Editor D. Koutsoyiannis; Associate editor A. Montanari

Citation Varouchakis, E.A., Hristopoulos, D.T., and Karatzas, G.P., 2012. Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application. Hydrological Sciences Journal, 57 (7), 1404–1419.     

When do we need a trend model in kriging?

Under usual estimation practice with local search windows for data and for interpolation situations, universal kriging and ordinary kriging yield the same estimates, using a data set with apparent trend, for both the unknown attribute and its trend component. Modeling the trend matters only in extrapolation situations. Because conditions of the case study presented arise most frequently in practice, the simpler ordinary kriging is the preferred option.     

Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data

The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.     

Fuzzy factorial analysis of snow avalanches

The mutual influence of 21 factors pertaining to terrain, weather, forest and snowpack have been discussed by 10 experts. The semantic (nil, weak, medium, high) evaluations are translated as membership degrees of fuzzy sets, and averaged between the experts by taking their fuzzy expectation value, yielding a 21 × 21 fuzzy matrix for direct interactions. Fuzzy successive multiplications and additions of the matrix give the indirect interactions. The activity and the passivity of the factors with respect to avalanches is represented by directed weighted graphs, and the average semantic values by a 4 × 4 matrix: the activity (passivity) is weak (strong) for tree damage; medium (nil) for altitude; medium (weak) for ground shape; medium (medium) for vegetation, soil, wind, microclimate, tree type, vertical distribution and mechanics of trees, snowpack distribution and snow gliding; strong (nil) for ground inclination and exposition, weather; strong (medium) for heat, precipitation, horizontal distribution of trees, snowpack constitution and stability.     

Effects of search parameters on kriged reserve estimates

Summary Reliable ore reserve estimates for deposits with highly skewed grade distributions are difficult tasks to perform. Although some recent geostatistical techniques are available to handle problems with these estimations, ordinary kriging or conventional interpolation methods are still widely used to estimate the ore reserves for such deposits. The estimation results can be very sensitive to the search parameters used during the interpolation of grades with these methods.This paper compares the ore reserve estimates from ordinary kriging using several cases in which certain search parameters are varied. The comparisons are extended to different mineralizations to show the changing effects of these parameters.     

Evaluation and comparison of spatial interpolators II

The performance of several variations on ordinary kriging and inverse distance estimators is evaluated. Mean squared errors (MSE) were calculated for estimates made on multiple resamplings from five exhaustive data bases representing two distinctly different types of estimation problem. Ordinary kriging, when performed with variograms estimated from the sample data, was more robust than inverse-distance methods to the type of estimation problem, and to the choice of estimation parameters such as number of neighbors.Notice: Although the research described in this article has been funded in part by the United States Environmental Protection Agency through Cooperative Agreement CR818526 to the Harry Reid Center for Environmental Studies, University of Nevada-Las Vegas, it has not been subjected to Agency review. Therefore it does not necessarily reflect the views of the Agency. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.     

Fitting variogram models by weighted least squares

The method of weighted least squares is shown to be an appropriate way of fitting variogram models. The weighting scheme automatically gives most weight to early lags and down-weights those lags with a small number of pairs. Although weights are derived assuming the data are Gaussian (normal), they are shown to be still appropriate in the setting where data are a (smooth) transform of the Gaussian case. The method of (iterated) generalized least squares, which takes into account correlation between variogram estimators at different lags, offer more statistical efficiency at the price of more complexity. Weighted least squares for the robust estimator, based on square root differences, is less of a compromise.