民间信仰载体的地域格局及其影响因素——以兰州市榆中县祠庙文化为例

以祠庙祭祀为主体且祠庙保存良好的民间信仰是甘肃陇中地区民间文化的重要组成。甘肃陇中地区的民间信仰具有很强的趋同性,本文以陇中地区的榆中县为例,以各村的祠庙为民间文化载体,通过核密度分析、Logistic回归等方法探讨榆中县民间文化载体的空间分布及其影响因素对于探讨陇中地区人地关系,保护和弘扬民俗文化有着重要意义。研究结果表明：（1）根据已有研究将榆中县民间信仰归纳为山神信仰、水神信仰、天地信仰、女性信仰、英雄崇拜和祖先崇拜,祖先崇拜的祠庙数量占有重要地位;（2）榆中县祠庙主要分布在西北黄河南岸、中部陇海铁路沿线及南部风景名胜集聚区,村级层面空间差异不显著、乡镇空间差异相对较大。通过核密度分析,不同类别民间信仰空间分布热点各有不同;（3）榆中县民间信仰空间分布受到区位条件限制,民间信仰的祠庙多位于海拔高度较低、人口密度较大、交通可达性较好的地区,坡度和到水源的距离成为山神信仰和水神信仰祠庙空间分布显著的影响因子,榆中县祠庙呈现出山神信仰类祠庙“依山”,水神信仰类祠庙“傍水”的空间特点。     

Statistical Downscaling Based on Dynamically DownscaledPredictors： Application to Monthly Precipitation in Sweden

A prerequisite of a successful statistical downscaling is that large-scale predictors simulated by the General Circulation Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical downscaling. The method uses predictors that are upscaled from a dynamical downscaling instead of predictors taken directly from a GCM simulation. The method is applied to downscaling of monthly precipitation in Sweden. The statistical model used is a multiple regression model that uses indices of large-scale atmospheric circulation and 850-hPa specific humidity as predictors. Data from two GCMs (HadCM2 and ECHAM4) and two RCM experiments of the Rossby Centre model (RCA1) driven by the GCMs are used. It is found that upscaled RCA1 predictors capture the seasonal cycle better than those from the GCMs, and hence increase the reliability of the downscaled precipitation. However, there are only slight improvements in the simulation of the seasonal cycle of downscaled precipitation. Due to the cost of the method and the limited improvements in the downscaling results, the three-step method is not justified to replace the one-step method for downscaling of Swedish precipitation.     

Hybrid Estimation of Semivariogram Parameters

Two widely used methods of semivariogram estimation are weighted least squares estimation and maximum likelihood estimation. The former have certain computational advantages, whereas the latter are more statistically efficient. We introduce and study a “hybrid” semivariogram estimation procedure that combines weighted least squares estimation of the range parameter with maximum likelihood estimation of the sill (and nugget) assuming known range, in such a way that the sill-to-range ratio in an exponential semivariogram is estimated consistently under an infill asymptotic regime. We show empirically that such a procedure is nearly as efficient computationally, and more efficient statistically for some parameters, than weighted least squares estimation of all of the semivariogram’s parameters. Furthermore, we demonstrate that standard plug-in (or empirical) spatial predictors and prediction error variances, obtained by replacing the unknown semivariogram parameters with estimates in expressions for the ordinary kriging predictor and kriging variance, respectively, perform better when hybrid estimates are plugged in than when weighted least squares estimates are plugged in. In view of these results and the simplicity of computing the hybrid estimates from weighted least squares estimates, we suggest that software that currently estimates the semivariogram by weighted least squares methods be amended to include hybrid estimation as an option.     

Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments

Hyperspectral remote sensing research was conducted to document the biophysical and biochemical characteristics of controlled forest plots subjected to various nutrient and irrigation treatments. The experimental plots were located on the Savannah River Site near Aiken, SC. AISA hyperspectral imagery were analysed using three approaches, including: (1) normalized difference vegetation index based simple linear regression (NSLR), (2) partial least squares regression (PLSR) and (3) machine-learning regression trees (MLRT) to predict the biophysical and biochemical characteristics of the crops (leaf area index, stem biomass and five leaf nutrients concentrations). The calibration and cross-validation results were compared between the three techniques. The PLSR approach generally resulted in good predictive performance. The MLRT approach appeared to be a useful method to predict characteristics in a complex environment (i.e. many tree species and numerous fertilization and/or irrigation treatments) due to its powerful adaptability.     

黄土丘陵沟壑区县域土壤有机质空间分布特征及预测

分析、预测土壤属性空间变异及其动态是区域土地质量评价和可持续土地利用的一个重要组成部分。在陕西省横山县采集了254个耕层(0~20cm)土样,利用数字地形与遥感影像分析技术,提取了相关地形与遥感指数,分析不同土地利用类型、不同地形条件下土壤有机质空间变异及分布特征,并利用相关因子进行回归预测分析。结果表明,县域土壤有机质平均含量很低,变异性较大。不同土地利用类型土壤有机质差异显著,其中以水稻田有机质含量最高,而林地和灌木林地相对较低。不同土地利用类型土壤有机质含量次序为:水稻田>川地>梯田>坝地>荒草地>坡耕地>林地>灌木林地。不同坡度分析表明,“0~3°”这一坡度等级有机质含量显著高于其它坡度等级;不同坡向有机质含量差异不显著,但不同坡向有机质含量存在一个明显的趋势,阴坡有机质含量整体上要比阳坡高。相关分析表明土壤有机质与高程h呈现负相关关系,与坡向的余弦值COSα正相关,与复合地形指数CTI正相关;土壤有机质和修正后的土壤调节植被指数(MSAVI)以及湿度指数(WI)正相关。利用相关环境变量及遥感指数进行多元线性逐步回归分析,预测结果不甚理想,存在一个平滑效应,对于残差解释相对较低,须进一步研究以更好的解释残差。     

汶川地震强余震与引潮力关系的初步研究

以汶川地震为例,统计分析2008年5月12日—2009年9月30日5级以上强余震与引潮力的关系,发现平行断层方向的线应变引潮力峰(谷)值时间与汶川地震及强余震发震时间的对应关系较好,且其对应程度与断层性质有关。综合分析认为,引潮力可能是地震的诱发因素之一。     

小块体重的多元线性回归方程在锡铁山铅锌矿床储量计算中的应用

从锡铁山铅锌矿床小块体重样品原始测定数据入手，运用数学地质中的回归分析，建立了本矿床的小块体重多元线性回归方程，为矿床储量计算提供了科学参数。利用回归方程法计算小块体重进而可以推广到所有贱金属矿床的储量计算中。     

Spatially filtered ridge regression (SFRR): A regression framework to understanding impacts of land cover patterns on urban climate

Understanding the impacts of land cover pattern on the heat island effect is essential for sustainable urban development. Conventional model fitting methods have restricted ability to produce accurate estimates of the land cover‐temperature association due to the lack of procedures to address two important issues: spatial dependence in proximal spatial units and high correlations among predictor variables. In this study, we seek to develop an effective framework called spatially filtered ridge regression (SFRR) to estimate the variations in the quantity and distribution of land surface temperature (LST) in response to various land cover patterns. The SFRR effectively integrates spatial autoregressive models and ridge regression, and it achieves reliable parameter estimates with substantially reduced mean square errors. We show this by comparing the performance of the SFRR to other widely adopted models using Monte Carlo simulation followed by an empirical study over central Phoenix. Results highlight the great potential of the SFRR in producing accurate statistical estimates, providing a positive step toward informed and unbiased decision‐making across a wide variety of disciplines. (Code and data to reproduce the results in the case study are available at: https://github.com/cfan13/SFRRTGIS.git .)     

地磁低点时间与经度的相关性分析

地磁低点时间在空间分布上的主要特征是随着经度变化而变化。为了求出这种变化关系的解析式,首先使用国家地磁台网的数据计算甘肃省近三年13个地磁台站的低点时间序列和低点时间的期望,并绘制了低点时间序列的频次分布图和概率密度曲线;然后对低点时间与台站经度的关系做线性回归,并指出这个线性回归方程应无限趋近于“格林尼治时间-经度”关系式。文章第一次提出台站经度与地磁Z分量低点时间的关系式,并提出它的趋近方程。各台站低点时间的期望就是地磁低点时间的正常背景值,它是判定地磁低点时间异常与否的参考标准。本文的工作完善和发展了丁鉴海先生的地磁低点位移法。     

加权Logistic回归模型在火山岩型铜矿预测中的应用:以宁芜盆地中段为例

文中探讨了加权Logistic回归模型在宁芜盆地中段火山岩型铜矿预测中的应用。首先,结合研究区的成矿地质背景,提取地质体、构造、围岩蚀变三大类证据因子;其次,分析各证据因子与铜矿点之间的空间关系,认为姑山旋回、娘娘山旋回火山机构控制了本区火山岩型铜矿的空间分布,根据计算结果,选取与火山岩型铜矿密切相关的龙王山组、姑山组地层,姑山旋回粗面斑岩、娘娘山旋回二长斑岩、NW向构造1.5 km缓冲区、NE向构造1.3 km缓冲区、EW向构造4.5 km缓冲区、硅化、褐铁矿化、黄铜矿化等作为模型自变量;最后采用加权Logistic回归模型进行成矿概率计算,并结合成矿地质背景,圈定四个成矿远景区,分别为P1、P2、P3、P4,其中P1、P2、P3呈北东向展布,主要受娘娘山和姑山火山机构控制,P4为东西向分布,主要受龙王山火山机构控制,在这些预测区中,均存在已发现的铜矿体,说明预测可信度较高。