Spatial continuity measures for probabilistic and deterministic geostatistics

Geostatistics has traditionally used a probabilistic framework, one in which expected values or ensemble averages are of primary importance. The less familiar deterministic framework views geostatistical problems in terms of spatial integrals. This paper outlines the two frameworks and examines the issue of which spatial continuity measure, the covarianceC (h) or the variogram (h), is appropriate for each framework. AlthoughC (h) and (h) were defined originally in terms of spatial integrals, the convenience of probabilistic notation made the expected value definitions more common. These now classical expected value definitions entail a linear relationship betweenC (h) and (h); the spatial integral definitions do not. In a probabilistic framework, where available sample information is extrapolated to domains other than the one which was sampled, the expected value definitions are appropriate; furthermore, within a probabilistic framework, reasons exist for preferring the variogram to the covariance function. In a deterministic framework, where available sample information is interpolated within the same domain, the spatial integral definitions are appropriate and no reasons are known for preferring the variogram. A case study on a Wiener-Levy process demonstrates differences between the two frameworks and shows that, for most estimation problems, the deterministic viewpoint is more appropriate. Several case studies on real data sets reveal that the sample covariance function reflects the character of spatial continuity better than the sample variogram. From both theoretical and practical considerations, clearly for most geostatistical problems, direct estimation of the covariance is better than the traditional variogram approach.This paper was presented at MGUS 87 Conference, Redwood City, California, 14 April 1987.     

有偏估计与LS估计的比较与选择

从假设检验的角度研究测量平差Gauss-Markov模型中有偏估计与LS估计的选择问题。首先在均方误差准则下对目前应用最广泛的2种有偏估计－－岭估计和主成分估计与LS估计进行了比较研究，得到了岭估计、主成分估计优于LS估计的条件；然后运用统计方法对这些条件的成立进行了假设检验；最后通过数值实验说明，在一定显著性水平下当原假设被接受时，说明没有理由不相信采用有偏估计来代替LS估计的合理性，可认为采用有偏估计将对LS估计做出比较有效的改进，当原假设被拒绝时，说明对采用有偏估计的优越性产生了怀疑，此时建议仍采用LS估计。     

新几何光学核驱动BRDF模型反演地表反照率的算法

MODIS的反照率和二向反射产品由基于核驱动模型的AMBRALS程序提供。目前 AMBRALS算法系统中所用的描述几何光学散射的核为LiSparseR核。新提出的一个几何光学核－LiTransit核兼有LiSparse核向LiDense核过渡的优点，比LiSparseR核更符合几何光学模型的基本原理。验证结果表明：与LiSparseR核比较，RossThick-LiTransit的核组合更能反映直入扇出反照率随太阳天顶角变化的趋势。因此在下一代的AMBRALS算法系统中，将用新的LiTransit核取代LiSparseR核。目前AMBRALS算法系统为了快速处理每天大量的数据，用多项式拟合核的半球积分。因此，为了替换LiSparseR核时，同时又不影响整个算法的系统性，本文研究了LiTransit核的多项式拟合。结果表明：拟合的多项式与核半球积分的相关性很好。     

GPS基线非线性解算的精度评定方法

在GPS基线向量非线性平差解算的基础上，分析了单位权中误差估值，平差值的精度估计和基线长度的精度估计，并给出了简明的直接估计公式，为GPS基线向量非线性平差解算的精度评定提供了直接的评定公式。     

中国生态文明建设效率空间均衡性及格局演变特征

研究中国生态文明建设效率的空间分布状态及格局演变特征,有利于厘清生态文明建设效率的空间差异机理,对于缩小区域差异及其驱动因素的探索具有重要的指导意义和参考价值。因此,基于“自然—经济—社会”（Nature-Economy-Society, N-E-S）复合生态系统理论和生态文明建设内涵目标构建生态文明建设效率的投入产出指标体系,并运用考虑非期望产出的SBM模型对中国2003-2015年的生态文明建设效率进行了测算,在此基础上,综合运用核密度估计、Theil指数、重心—标准差椭圆等方法,对中国生态文明建设效率的空间均衡性及其空间分布格局进行了探究。结果表明：① 中国生态文明建设效率存在明显的空间非均衡性,在研究期内（2003-2015年）空间非均衡性表现为先逐渐缩小后又逐渐扩大的动态演变特征;由三大区域的Theil指数均值可知,西部地区空间非均衡程度最大（0.1174）,东部次之（0.0365）,中部最小（0.0223）。② 从重心移动轨迹来看,中国生态文明建设效率的重心位于河南境内,历经了先向东南再向东北移动的过程,重心移动的方向表明位于该方向上的地区生态文明建设效率有所提升。③ 从标准差椭圆来看,中国生态文明建设效率空间分布呈现出偏东北—偏西南格局,并且这种格局有向正北—正南方向转变的趋势。     

通道特性相关时带乘性噪声系统观测噪声的最优估计

研究在较弱的乘性噪声条件下系统观测噪声的最优估计问题,就乘性噪声为一般随机矩阵且各观测通道乘性噪声在同时刻相关的情形,给出了在线性最小方差意义下的观测噪声最优滤波算法和最优平滑算法。针对这些算法进行了仿真研究,仿真结果表明算法有较好的估计效果。     

中国城市碳排放强度的空间溢出效应及驱动因素探究（英文）

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.     

Spatial spillover effect and driving forces of carbon emission intensity at the city level in China

Journal of Geographical Sciences - In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial...     

遥感影像与POI数据相结合的城市建成区提取适用性研究——以沈阳市为例

城市建成区的发展状况是地理国情监测的重要内容,本文基于遥感影像数据和POI数据对城市建成区进行提取,针对二者的适用性问题进行了研究。试验以沈阳市为研究区域,在研究区域内选择2016年遥感影像数据和POI数据作为数据源进行对比分析。首先,对遥感影像数据和POI数据进行预处理;其次,通过监督分类的方法对遥感影像进行建成区的提取;然后,采用核密度估计法分析POI数据并提取出建成区;最后,利用叠加分析法对比分析这两种数据的适用性。试验结果表明：使用遥感影像数据作为数据源可以较为全面客观地反映城市建成区的发展现状;利用POI数据提取出的城市建成区具有较强的经济属性,能够很好地反映出城市中的经济活跃区。     

一种基于蓝牙室内指纹定位的贝叶斯改进算法

贝叶斯估计是重要的位置指纹定位算法,但传统的等值贝叶斯先验概率在动态定位中不适用。针对该问题,本文提出了一种基于贝叶斯指纹定位的改进算法。首先,借助陀螺仪获取的航向信息和高斯核函数模型建立概率投票算法,计算先验概率;然后,结合先验概率和信号强度计算待测点位于参考点上的后验概率;最后,选取概率最高的参考点,以概率为权重计算待测点的最或然值。以智能手机为试验对象,在规则路径试验中,改进算法的平均定位误差为1.15 m,定位误差小于2 m的概率为96.1%,不规则路径试验中,平均定位误差为0.50 m,定位误差在1 m的可信度为94.8%;并且改进算法对定位中位置跳变的现象有明显改善,具有较好的稳健性。