Visualization for Large‐scale Gaussian Updates

In geostatistics and also in other applications in science and engineering, it is now common to perform updates on Gaussian process models with many thousands or even millions of components. These large‐scale inferences involve modelling, representational and computational challenges. We describe a visualization tool for large‐scale Gaussian updates, the ‘medal plot’. The medal plot shows the updated uncertainty at each observation location and also summarizes the sharing of information across observations, as a proxy for the sharing of information across the state vector (or latent process). As such, it reflects characteristics of both the observations and the statistical model. We illustrate with an application to assess mass trends in the Antarctic Ice Sheet, for which there are strong constraints from the observations and the physics.     

空间面板杜宾模型的Bootstrap Wald-COMFAC检验研究

将共同因子约束(COMFAC)的Wald检验问题引入到空间面板模型中,讨论空间面板杜宾模型与空间面板误差模型的识别问题。蒙特卡洛模拟表明:在有限样本下,基于渐近临界值的Wald检验有着良好的检验功效,但存在着较为严重的尺度扭曲。进一步采用残差Bootstrap方法,在不损失检验功效的前提下,能够显著地降低检验的尺度扭曲。因此,残差Bootstrap方法是更为有效的检验方法。     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

熟人社会的空间秩序

村落空间是村落历史的沉淀。不同于熟人社会研究的关系取向,文章引入空间生产的视角,基于南方村落的空间图式阐释熟人社会的空间秩序。“祠堂-阳宅”的平面空间图式定义了农民自然生命升华为道德生命的日常生活路径,而“庙宇-阴宅”的纵向空间图式定义了农民应对日常生活断裂的宗教生活模式。风水的空间实践镶嵌于村落空间图式,并促进了村落空间的结构化和立体化,而村落空间结构维系了村落熟人社会的公共性。可见,熟人社会的空间秩序是空间结构生产与社会秩序生产的统一。现代性力量逐渐抽离了村落空间的历史底蕴,释放了空间权利意识,导致村落空间结构破裂与熟人社会秩序紊乱。立足村庄社会的空间治理是突破村庄空间利益困局,实现乡村振兴的重要方式。     

Practical Maximum Pseudolikelihood for Spatial Point Patterns(with Discussion)

This paper describes a technique for computing approximate maximum pseudolikelihood estimates of the parameters of a spatial point process. The method is an extension of Berman & Turner's (1992) device for maximizing the likelihoods of inhomogeneous spatial Poisson processes. For a very wide class of spatial point process models the likelihood is intractable, while the pseudolikelihood is known explicitly, except for the computation of an integral over the sampling region. Approximation of this integral by a finite sum in a special way yields an approximate pseudolikelihood which is formally equivalent to the (weighted) likelihood of a loglinear model with Poisson responses. This can be maximized using standard statistical software for generalized linear or additive models, provided the conditional intensity of the process takes an 'exponential family' form. Using this approach a wide variety of spatial point process models of Gibbs type can be fitted rapidly, incorporating spatial trends, interaction between points, dependence on spatial covariates, and mark information.     

A general central limit theorem and a subsampling variance estimator for α‐mixing point processes

We establish a central limit theorem for multivariate summary statistics of nonstationary α‐mixing spatial point processes and a subsampling estimator of the covariance matrix of such statistics. The central limit theorem is crucial for establishing asymptotic properties of estimators in statistics for spatial point processes. The covariance matrix subsampling estimator is flexible and model free. It is needed, for example, to construct confidence intervals and ellipsoids based on asymptotic normality of estimators. We also provide a simulation study investigating an application of our results to estimating functions.     

On Bartlett correction of empirical likelihood for regularly spaced spatial data

Bartlett correction constitutes one of the attractive features of empirical likelihood because it enables the construction of confidence regions for parameters with improved coverage probabilities. We study the Bartlett correction of spatial frequency domain empirical likelihood (SFDEL) based on general spectral estimating functions for regularly spaced spatial data. This general formulation can be applied to testing and estimation problems in spatial analysis, for example testing covariance isotropy, testing covariance separability as well as estimating the parameters of spatial covariance models. We show that the SFDEL is Bartlett correctable. In particular, the improvement in coverage accuracies of the Bartlett‐corrected confidence regions depends on the underlying spatial structures. The Canadian Journal of Statistics 47: 455–472; 2019 © 2019 Statistical Society of Canada     

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top‐down (or downscaling) approach to disaggregate administrative‐unit level asset value to grid‐cell level. To do so, finding the highly correlated “surrogate” indicators is the key. A combination of three data sets—nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc‐second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.     

金融集聚、生产率增长与城乡收入差距的实证分析——基于动态空间面板模型

本文利用中国2003-2013年285个地级及以上城市的统计数据,采用动态空间面板模型实证分析了金融集聚对城市总体生产率增长及其内部城乡收入差距的影响,研究结论表明：金融集聚是促进城市总体生产率增长的重要推动力,同时也是导致城市内部城乡收入差距扩大的关键因素,这主要是因为金融集聚显著推动了城市居民人均收入水平的提高,而对农村居民人均收入水平的提升作用不显著。本文的政策含义在于,在城市金融集聚的扩散阶段还没有到来之前,政府旨在缓解城乡收入差距的金融政策可能会抑制城市总体生产率增长。     

数字经济对地区经济增长差距的影响效应分析

文章基于中国30个省(自治区、直辖市)2013—2020年的面板数据,运用固定效应模型、中介效应模型和空间杜宾模型等方法,实证检验了数字经济发展对地区经济的影响及其传导机制。研究发现,数字经济发展显著缩小地区之间的经济差距,运用工具变量估计、替换被解释变量、剔除异常值等方法进行稳健性检验后,该结论依然成立。中介效应分析表明,地区创新能力是数字经济缩小地区间经济差距的重要机制。空间溢出效应分析表明,数字经济发展对地区间经济增长差距的缩小具有负向的溢出效应。空间异质性分析表明,东部地区数字经济发展对地区经济增长差距的缩小效应相比中部、西部地区更显著;南、北方的异质性分析表明,北方地区数字经济发展对地区经济增长差距的缩小的积极作用比南方更显著。最后,为我国发展数字经济、充分发挥数字经济对缩小地区经济差距,实现地区间协调发展提出政策建议。