Spatial Statistics最新文献

英文中文

Bayesian adaptive Lasso estimation for partially linear hierarchical spatial autoregressive model 部分线性层次空间自回归模型的贝叶斯自适应Lasso估计

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-03-12 DOI: 10.1016/j.spasta.2025.100892

Miao Long, Zhimeng Sun

This paper presents a Bayesian adaptive Lasso estimation approach for partially linear hierarchical spatial autoregressive models. Despite advancements in spatial modeling, two key gaps remain: the lack of non-linear components in hierarchical spatial autoregressive models to capture complex spatial relationships, and the insufficient application of dimensionality reduction techniques to address high-dimensionality and overfitting. This paper addresses these issues by combining partially linear models with spatial autoregressive structures and incorporating dimensionality reduction techniques to enhance model efficiency and mitigate overfitting. The hierarchical structure facilitates multi-level modeling, accommodating complex data relationships. The Bayesian adaptive Lasso technique ensures effective variable selection and regularization, improving model interpretability and performance. Simulations and real data applications demonstrate the proposed method’s excellent performance. This work offers valuable insights for researchers and practitioners in dealing with spatially correlated data in various fields.

针对部分线性层次空间自回归模型，提出了一种贝叶斯自适应Lasso估计方法。尽管在空间建模方面取得了进步，但仍然存在两个关键差距：层次空间自回归模型中缺乏非线性成分来捕捉复杂的空间关系，以及降维技术在解决高维和过拟合问题上的应用不足。本文通过将部分线性模型与空间自回归结构相结合，并结合降维技术来提高模型效率和减轻过拟合，从而解决了这些问题。层次结构有助于多级建模，适应复杂的数据关系。贝叶斯自适应Lasso技术确保了有效的变量选择和正则化，提高了模型的可解释性和性能。仿真和实际数据应用证明了该方法的优良性能。这项工作为研究人员和实践者在处理各个领域的空间相关数据提供了有价值的见解。

引用次数: 0

Bayesian analysis and variable selection for spatial count data with an application to Rio de Janeiro gun violence 空间计数数据的贝叶斯分析与变量选择——以里约热内卢枪支暴力为例

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-02-27 DOI: 10.1016/j.spasta.2025.100890

Guilherme Ludwig , Yuan Wang , Tingjin Chu , Haonan Wang , Jun Zhu

Statistical analysis has been successfully applied to crime data for identification of crime hot spots and prediction of future crimes. In this paper, our main objective is to identify key factors for gun violence in Rio de Janeiro and study the relationship between these key factors and the number of reported events. We use a Bayesian hierarchical stochastic Poisson regression model for spatial counts, which enables us to address the over-dispersed count data and to handle the spatial correlation. Moreover, we propose a variable selection method for key factor identification based on the spike-and-slab prior distribution for the regression coefficients. A new Gibbs sampler is developed for sampling from the posterior distributions with the help of augmentation of Pólya-Gamma auxiliary variables. Simulation studies are used to demonstrate the performance of our proposed approach. Our analysis of the gun violence data in Rio de Janeiro reveals the relationship between violence events and socio-demographic covariates as well as an interpretable spatial random effect that accounts for unmeasured covariate information.

统计分析已成功地应用于犯罪数据中，用于识别犯罪热点和预测未来犯罪。在本文中，我们的主要目标是确定巴西里约热内卢枪支暴力的关键因素，并研究这些关键因素与报告事件数量之间的关系。我们使用贝叶斯分层随机泊松回归模型进行空间计数，这使我们能够解决过度分散的计数数据并处理空间相关性。此外，我们还提出了一种基于回归系数的穗板先验分布的变量选择方法来识别关键因素。利用Pólya-Gamma辅助变量的增广，开发了一种新的Gibbs采样器，用于对后验分布进行采样。仿真研究证明了我们提出的方法的性能。我们对巴西里约热内卢枪支暴力数据的分析揭示了暴力事件与社会人口协变量之间的关系，以及一种可解释的空间随机效应，该效应解释了不可测量的协变量信息。

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引用次数: 0

Derivative-based spatial mediation with INLA-SPDE 基于INLA-SPDE的导数空间中介

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-02-24 DOI: 10.1016/j.spasta.2025.100885

Claudio Rubino , Chiara Di Maria , Antonino Abbruzzo , Gioacchino Bono , Germana Garofalo , Giacomo Milisenda , Giada Adelfio

In many applied fields, it may be of interest to evaluate mediational mechanisms occurring in spatial domains. The approaches proposed so far in the literature to address this issue deal with areal data and often consider linear models. In this paper, we propose an approach to assess mediation in the presence of geostatistical data by combining the integrated nested Laplace approximation (INLA) with a derivative-based approach for mediation analysis, which allows one to estimate indirect effects also in the case of nonlinear models. We investigate the effect of ignoring spatial processes in the mediator and the outcome models through a simulation study, focusing also on the case of correlated processes. To show the usefulness of our approach, we also provided an ecological application.

在许多应用领域中，评估空间域中发生的中介机制可能会引起人们的兴趣。到目前为止，文献中提出的解决这一问题的方法处理的是面数据，并且经常考虑线性模型。在本文中，我们提出了一种在地质统计数据存在的情况下评估中介的方法，通过将集成嵌套拉普拉斯近似（INLA）与基于导数的中介分析方法相结合，该方法允许人们在非线性模型的情况下估计间接影响。我们通过模拟研究考察了忽略中介和结果模型中空间过程的影响，并重点研究了相关过程的情况。为了展示我们方法的有效性，我们还提供了一个生态应用程序。

引用次数: 0

Clustered factor analysis for multivariate spatial data 多元空间数据的聚类因子分析

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-02-22 DOI: 10.1016/j.spasta.2025.100889

Yanxiu Jin , Tomoya Wakayama , Renhe Jiang , Shonosuke Sugasawa

Factor analysis has been extensively used to reveal the dependence structures among multivariate variables, offering valuable insight in various fields. However, it cannot incorporate the spatial heterogeneity that is typically present in spatial data. To address this issue, we introduce an effective method specifically designed to discover the potential dependence structures in multivariate spatial data. Our approach assumes that spatial locations can be approximately divided into a finite number of clusters, with locations within the same cluster sharing similar dependence structures. By leveraging an iterative algorithm that combines spatial clustering with factor analysis, we simultaneously detect spatial clusters and estimate a unique factor model for each cluster. The proposed method is evaluated through comprehensive simulation studies, demonstrating its flexibility. In addition, we apply the proposed method to a dataset of railway station attributes in the Tokyo metropolitan area, highlighting its practical applicability and effectiveness in uncovering complex spatial dependencies.

因子分析被广泛用于揭示多变量之间的依赖结构，在各个领域提供了有价值的见解。但是，它不能包含空间数据中通常存在的空间异质性。为了解决这个问题，我们引入了一种有效的方法来发现多元空间数据中潜在的依赖结构。我们的方法假设空间位置可以近似地划分为有限数量的集群，同一集群内的位置共享相似的依赖结构。通过利用空间聚类与因子分析相结合的迭代算法，我们同时检测空间聚类并估计每个聚类的独特因子模型。通过综合仿真研究对该方法进行了评价，证明了该方法的灵活性。此外，我们将该方法应用于东京大都市区的火车站属性数据集，突出了其在揭示复杂空间依赖关系方面的实用性和有效性。

引用次数: 0

A Hotelling spatial scan statistic for functional data: Application to economic and climate data 功能数据的酒店空间扫描统计：在经济和气候数据中的应用

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-02-22 DOI: 10.1016/j.spasta.2025.100888

Zaineb Smida , Thibault Laurent , Lionel Cucala

A scan method for functional data indexed in space has been developed. The scan statistic is derived from the Hotelling test statistic for functional data, extending the univariate and multivariate Gaussian spatial scan statistics. This method consistently outperforms existing techniques in detecting and locating spatial clusters, as demonstrated through simulations. It has been applied to two types of real data: economic data in order to identify spatial clusters of abnormal unemployment rates in Spain and climatic data in order to detect unusual climate change patterns in Great Britain, Nigeria, Pakistan, and Venezuela.

提出了一种空间索引功能数据的扫描方法。扫描统计量来源于功能数据的霍特林检验统计量，扩展了单变量和多变量高斯空间扫描统计量。仿真结果表明，该方法在探测和定位空间簇方面始终优于现有技术。它已被应用于两种类型的实际数据：经济数据，以确定西班牙异常失业率的空间集群；气候数据，以检测英国、尼日利亚、巴基斯坦和委内瑞拉的异常气候变化模式。

引用次数: 0

Statistical inference of partially linear time-varying coefficients spatial autoregressive panel data model 部分线性时变系数空间自回归面板数据模型的统计推断

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-02-18 DOI: 10.1016/j.spasta.2025.100887

Lingling Tian , Chuanhua Wei , Mixia Wu

This paper investigates a partially linear spatial autoregressive panel data model that incorporates fixed effects, constant and time-varying regression coefficients, and a time-varying spatial lag coefficient. A two-stage least squares estimation method based on profile local linear dummy variables (2SLS-PLLDV) is proposed to estimate both constant and time-varying coefficients without the need for first differencing. The asymptotic properties of the estimator are derived under certain conditions. Furthermore, a residual-based goodness-of-fit test is constructed for the model, and a residual-based bootstrap method is used to obtain p-values. Simulation studies show the good performance of the proposed method in various scenarios. For illustration, the carbon emission data from Chinese provinces and the public capital productivity data from the United States are analyzed.

本文研究了一个包含固定效应、常、时变回归系数和时变空间滞后系数的部分线性空间自回归面板数据模型。提出了一种基于剖面局部线性虚拟变量的两阶段最小二乘估计方法（2SLS-PLLDV），该方法既能估计常系数，又能估计时变系数，无需进行一次差分。在一定条件下，得到了估计量的渐近性质。在此基础上，对模型进行残差拟合优度检验，并采用残差自举法获得p值。仿真研究表明，该方法在各种场景下都具有良好的性能。本文以中国各省的碳排放数据和美国的公共资本生产率数据为例进行了分析。

引用次数: 0

A term structure geostatistical model with correlated residuals: A comparative analysis 具有相关残差的期限结构地质统计模型：比较分析

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-02-17 DOI: 10.1016/j.spasta.2025.100886

Antonella Congedi, Sandra De Iaco, Donato Posa

The growth of financial markets and the emerging derivative instruments require the development of advanced techniques for forecasting the term structure of interest rates. In this context, two significant dimensions, i.e. maturity and time, need to be jointly considered in the modeling procedure. In the literature, the Nelson–Siegel model is commonly used to explain the dependence of the interest rates on maturity and time. However, it cannot be excluded that the residuals obtained from Nelson–Siegel estimates are still correlated. At this purpose, a geostatistical approach is adopted and an innovative modeling solution is provided. Indeed, differently from the existing contributions, this paper proposes a dynamic model for predicting the term structure of spot interest rates, where the joint evolution with respect to time and maturity is considered for both the deterministic and the stochastic parts of the model. The relevance as well as the potentiality of the geostatistical modeling techniques extended to treat observations not strictly referred to a geographic system, has been properly underlined. For comparative reasons, different hypotheses on the random field, utilized to describe the interest rates and its trend component, are also assumed and a comparison among predictive performance of alternative models is discussed.

金融市场的发展和新兴的衍生工具要求发展预测利率期限结构的先进技术。在这种情况下，两个重要的维度，即成熟度和时间，需要在建模过程中共同考虑。在文献中，Nelson-Siegel模型常用来解释利率对期限和时间的依赖关系。然而，不能排除由Nelson-Siegel估计得到的残差仍然是相关的。为此，采用了地质统计学方法，并提供了一种创新的建模解决方案。事实上，与现有的贡献不同，本文提出了一个预测现货利率期限结构的动态模型，其中模型的确定性部分和随机部分都考虑了时间和期限的联合演变。已适当地强调了扩展到处理不严格涉及地理系统的观测的地质统计模拟技术的相关性和潜力。为便于比较，本文还假设了用于描述利率及其趋势分量的随机场的不同假设，并对不同模型的预测性能进行了比较。

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引用次数: 0

Bayesian geographically weighted regression using Fused Lasso prior 基于融合Lasso先验的贝叶斯地理加权回归

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-02-06 DOI: 10.1016/j.spasta.2025.100884

Toshiki Sakai , Jun Tsuchida , Hiroshi Yadohisa

A main purpose of spatial data analysis is to predict the objective variable for the unobserved locations. Although Geographically Weighted Regression (GWR) is often used for this purpose, estimation instability proves to be an issue. To address this issue, Bayesian Geographically Weighted Regression (BGWR) has been proposed. In BGWR, by setting the same prior distribution for all locations, the coefficients’ estimation stability is improved. However, when observation locations’ density is spatially different, these methods do not sufficiently consider the similarity of coefficients among locations. Moreover, the prediction accuracy of these methods becomes worse. To solve these issues, we propose Bayesian Geographically Weighted Sparse Regression (BGWSR) that uses Bayesian Fused Lasso for the prior distribution of the BGWR coefficients. Constraining the parameters to have the same values at adjacent locations is expected to improve the prediction accuracy at locations with a low number of adjacent locations. Furthermore, from the predictive distribution, it is also possible to evaluate the uncertainty of the predicted value of the objective variable. By examining numerical studies, we confirmed that BGWSR has better prediction performance than the existing methods (GWR and BGWR) when the density of observation locations is spatial difference. Finally, the BGWSR is applied to land price data in Tokyo. Thus, the results suggest that BGWSR has better prediction performance and smaller uncertainty than existing methods.

空间数据分析的一个主要目的是预测未观测位置的客观变量。虽然地理加权回归（GWR）经常用于此目的，但估计不稳定性被证明是一个问题。为了解决这个问题，贝叶斯地理加权回归（BGWR）被提出。在BGWR中，通过对所有位置设置相同的先验分布，提高了系数估计的稳定性。然而，当观测点密度在空间上不同时，这些方法没有充分考虑到观测点间系数的相似性。而且，这些方法的预测精度变差了。为了解决这些问题，我们提出了贝叶斯地理加权稀疏回归（BGWSR），该回归使用贝叶斯融合套索进行BGWR系数的先验分布。约束参数在相邻位置具有相同的值有望提高相邻位置数量较少的位置的预测精度。此外，从预测分布中还可以评估目标变量预测值的不确定性。通过数值研究证实，当观测位置密度存在空间差异时，BGWSR的预测性能优于现有方法（GWR和BGWR）。最后，将BGWSR应用于东京地价数据。结果表明，与现有方法相比，BGWSR具有更好的预测性能和更小的不确定性。

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引用次数: 0

Spatial deep convolutional neural networks 空间深度卷积神经网络

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-02-06 DOI: 10.1016/j.spasta.2025.100883

Qi Wang, Paul A. Parker, Robert Lund

Spatial prediction problems often use Gaussian process models, which can be computationally burdensome in high dimensions. Specification of an appropriate covariance function for the model can be challenging when complex non-stationarities exist. Recent work has shown that pre-computed spatial basis functions and a feed-forward neural network can capture complex spatial dependence structures while remaining computationally efficient. This paper builds on this literature by tailoring spatial basis functions for use in convolutional neural networks. Through both simulated and real data, we demonstrate that this approach yields more accurate spatial predictions than existing methods. Uncertainty quantification is also considered.

空间预测问题通常使用高斯过程模型，这在高维情况下计算量很大。当存在复杂的非平稳性时，为模型指定适当的协方差函数可能具有挑战性。最近的研究表明，预先计算的空间基函数和前馈神经网络可以捕获复杂的空间依赖结构，同时保持计算效率。本文在此文献的基础上，通过裁剪空间基函数用于卷积神经网络。通过模拟和实际数据，我们证明该方法比现有方法产生更准确的空间预测。还考虑了不确定度的量化。

引用次数: 0

Clustering of compound events based on multivariate comonotonicity 基于多元共单调性的复合事件聚类

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-01-27 DOI: 10.1016/j.spasta.2025.100881

Fabrizio Durante , Sebastian Fuchs , Roberta Pappadà

Driven by the goal of generating risk maps for flood events—characterized by various physical variables such as peak flow and volume, and measured at specific geographic locations—this work proposes several dissimilarity functions for use in unsupervised learning problems and, specifically, in clustering algorithms. These dissimilarities are rank-based, relying on the dependence occurring among the random variables involved, and assign the smallest values to pairs of subsets that are

π

-comonotonic. This concept is less restrictive than classical comonotonicity but, in the multivariate case, can offer a more intuitive understanding of compound phenomena.

An application of these measures is presented through the analysis of flood risks using data from the Po river basin, with results compared to similar studies found in the literature.

在为洪水事件生成风险图的目标驱动下——以各种物理变量为特征，如峰值流量和体积，并在特定的地理位置进行测量——这项工作提出了几个不相似函数，用于无监督学习问题，特别是聚类算法。这些差异是基于秩的，依赖于所涉及的随机变量之间的相关性，并将最小的值分配给π共单调的子集对。这个概念比经典的共单调性限制少，但在多变量情况下，可以更直观地理解复合现象。通过使用波河流域的数据对洪水风险进行分析，提出了这些措施的应用，并将结果与文献中发现的类似研究进行了比较。

引用次数: 0

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