Spatial Statistics最新文献

英文中文

Computationally efficient spatio-temporal disease mapping for big data 面向大数据的高效时空疾病制图

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-06-01 Epub Date: 2025-04-23 DOI: 10.1016/j.spasta.2025.100901

Duncan Lee

Disease mapping models estimate the spatio-temporal variation in population-level disease risks or rates across a set of

K

areal units for

N

time periods, aiming to identify temporal trends and spatial hotspots. Highly parameterised Bayesian hierarchical models with over

K N

random effects are commonly used to estimate this spatio-temporal variation, which are assigned autoregressive and conditional autoregressive prior distributions. These models work well when there are tens of thousands of data points, but are likely to be computationally burdensome when this rises to hundreds of thousands or above. This paper proposes a computationally efficient alternative, which can fit a range of spatio-temporal disease trends almost as well as existing highly parameterised models but only takes around 5% to 40% of the time to implement. It achieves this by modelling the average spatial and temporal trends in the data with autoregressive type random effects, which are augmented by an observation-driven process using functions of earlier data as additional covariates in the model. The efficacy of this methodology is tested by simulation, before being applied to the motivating study that estimates the spatio-temporal trends in asthma, cancer, coronary heart and chronic obstructive pulmonary disease prevalences for

K = 32, 751

small areas over

N = 13

years in England.

疾病制图模型估算了N个时间段内K个面积单位的人群水平疾病风险或发病率的时空变化，旨在确定时间趋势和空间热点。具有超过KN随机效应的高参数化贝叶斯层次模型通常用于估计这种时空变化，该模型被分配为自回归和条件自回归先验分布。当有数以万计的数据点时，这些模型工作得很好，但当数据点增加到数十万或更多时，计算负担可能会很重。本文提出了一种计算效率高的替代方案，它可以拟合一系列时空疾病趋势，几乎和现有的高度参数化模型一样，但只需要大约5%到40%的时间来实现。它通过用自回归型随机效应对数据中的平均空间和时间趋势进行建模来实现这一点，这些趋势通过使用早期数据的函数作为模型中的附加协变量的观测驱动过程来增强。该方法的有效性通过模拟测试，然后应用于一项激励研究，该研究估计了英国K=32,751个小地区在N=13年内哮喘、癌症、冠心病和慢性阻塞性肺病患病率的时空趋势。

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引用次数: 0

Random elastic space–time (REST) prediction 随机弹性时空（REST）预测

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-06-01 Epub Date: 2025-04-24 DOI: 10.1016/j.spasta.2025.100904

Nicolas Coloma, William Kleiber

Statistical modeling and interpolation of space–time processes has gained increasing relevance over the last few years. However, real world data often exhibit characteristics that challenge conventional methods such as nonstationarity and temporal misalignment. For example, high frequency solar irradiance data are typically observed at fine temporal scales, but at sparse spatial sampling, so space–time interpolation is necessary to support solar energy studies. The nonstationarity and phase misalignment of such data challenges extant approaches. We propose random elastic space–time (REST) prediction, a novel method that addresses temporally-varying phase misalignment by combining elastic alignment and conventional kriging techniques. Moreover, uncertainty in both amplitude and phase alignment can be readily quantified in a conditional simulation framework, whereas conventional space–time methods only address amplitude uncertainty. We illustrate our approach on a challenging solar irradiance dataset, where our method demonstrates superior predictive distributions compared to existing geostatistical and functional data analytic techniques.

时空过程的统计建模和插值在过去几年中获得了越来越多的相关性。然而，现实世界的数据经常表现出挑战传统方法的特征，如非平稳性和时间偏差。例如，高频太阳辐照度数据通常是在精细的时间尺度上观测到的，但在稀疏的空间采样上，因此需要时空插值来支持太阳能研究。这些数据的非平稳性和相位失调对现有的方法提出了挑战。我们提出随机弹性时空（REST）预测，这是一种结合弹性对准和传统克里格技术来解决时变相位失调的新方法。此外，振幅和相位对准的不确定性可以很容易地在条件模拟框架中量化，而传统的时空方法只处理振幅的不确定性。我们在一个具有挑战性的太阳辐照度数据集上说明了我们的方法，与现有的地质统计和功能数据分析技术相比，我们的方法展示了优越的预测分布。

引用次数: 0

Fast computation of the statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences 基于脉冲触发二值伪随机序列的时空感受野估计统计显著性检验的快速计算

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-06-01 Epub Date: 2025-03-27 DOI: 10.1016/j.spasta.2025.100899

Murat Okatan

Background

Spatio-temporal receptive fields (STRFs) of visual neurons are often estimated using spike-triggered averaging (STA) with binary pseudo-random stimulus sequences. An exact analytical test—called the STA-BPRS test—has been developed to determine the statistical significance of each pixel in the STRF estimate. However, computing this test can take minutes to days, or even longer, for certain neurons.

New method

Here, the STA-BPRS test is accelerated by approximating the null distribution of STRF pixel estimates with a Normal distribution. This methodological refinement significantly reduces computation time, making large-scale data analysis feasible.

Results

The approximate test is systematically validated on real mouse retinal ganglion cell data and synthetic spike train data, demonstrating that it yields identical significance thresholds to the exact test. For neurons where, exact computation would be prohibitively long (e.g., hundreds of years), the approximate test completes in seconds or minutes.

Comparison with existing methods

Few approaches address pixel-by-pixel significance in STA-based STRF estimates. While subspace methods like spike-triggered covariance exist for STRF estimation, they typically do not provide direct voxel-wise or pixel-wise p-values. The proposed method specifically accelerates an exact distribution-based test.

Conclusions and impact

The proposed Normal approximation drastically reduces computation time, enabling high-throughput analysis of STRF mapping from spike data. This advancement may foster broader adoption of precise statistical tests of STRFs in large-scale, high-density electrophysiological recordings. Moreover, fast detection of significant STRF features could facilitate closed-loop experiments where stimuli dynamically adapt to changing STRF structures.

背景：视觉神经元的时空感受野（strf）通常是用二值伪随机刺激序列的spike-triggered averaging （STA）来估计的。已经开发了一种精确的分析测试，称为STA-BPRS测试，以确定STRF估计中每个像素的统计显著性。然而，对于某些神经元来说，计算这个测试可能需要几分钟到几天，甚至更长时间。本文通过用正态分布近似STRF像素估计的零分布来加速STA-BPRS检验。这种方法的改进大大减少了计算时间，使大规模数据分析成为可能。结果在真实小鼠视网膜神经节细胞数据和合成脉冲序列数据上系统地验证了近似测试，表明它与精确测试产生相同的显著性阈值。对于神经元，精确的计算将会非常长（例如，数百年），近似的测试在几秒钟或几分钟内完成。与现有方法的比较很少有方法可以解决基于sta的STRF估计中逐像素的显著性问题。虽然存在像尖峰触发协方差这样的子空间方法用于STRF估计，但它们通常不能提供直接的体素或像素p值。该方法特别加速了基于精确分布的测试。结论和影响所提出的正态近似大大减少了计算时间，使高通量分析STRF映射从峰值数据。这一进展可能促进在大规模高密度电生理记录中更广泛地采用精确的strf统计测试。此外，快速检测重要的STRF特征可以促进闭环实验，其中刺激动态适应变化的STRF结构。

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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 : 2025-06-01 Epub 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

A J-test for spatial autoregressive binary models 空间自回归二元模型的j检验

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-06-01 Epub Date: 2025-04-28 DOI: 10.1016/j.spasta.2025.100903

Gianfranco Piras , Mauricio Sarrias

Spatial autoregressive binary models are well established in spatial statistics and econometric literature. Recently, different estimation methods have been proposed that account for logistic as well as probit regressions. In spatial models the choice of the spatial weighting matrix is crucial to reflect the amount of correlation in the data. This article proposes a simple

J

-test procedure for spatial autoregressive binary model. Since the

J

-test is a non-nested test, it can be used, among other things, to test the specification of the spatial weighting matrix. The

J

-test is based on augmenting the null model with the predictor from the alternative model(s). After defining these predictors, we develop the theory and derive the steps for the

J

-test. We also evaluate the finite sample properties in the context of a Monte Carlo experiment. An empirical application on firms’ decisions to reopen in the aftermath of Hurricane Katrina for New Orleans is also presented.

空间自回归二元模型在空间统计学和计量经济学文献中得到了很好的建立。最近，人们提出了不同的估计方法，既考虑了逻辑回归，也考虑了概率回归。在空间模型中，空间加权矩阵的选择是反映数据中相关程度的关键。本文提出了空间自回归二元模型的一个简单的j检验程序。由于J-test是一个非嵌套测试，因此它可以用于测试空间加权矩阵的规格。j检验是基于用来自备选模型的预测器对零模型进行扩充。在定义了这些预测因子之后，我们发展了理论并推导了j检验的步骤。我们还在蒙特卡罗实验的背景下评估了有限样本的性质。本文还对新奥尔良市卡特里娜飓风过后企业重新开业的决策进行了实证分析。

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 : 2025-06-01 Epub 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

Isotropy testing in spatial point patterns: nonparametric versus parametric replication under misspecification 空间点模式的各向同性测试：错误规范下的非参数与参数复制

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-06-01 Epub Date: 2025-04-05 DOI: 10.1016/j.spasta.2025.100898

Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin

Several hypothesis testing methods have been proposed to validate the assumption of isotropy in spatial point patterns. A majority of these methods are characterised by an unknown distribution of the test statistic under the null hypothesis of isotropy. Parametric approaches to approximating the distribution involve simulation of patterns from a user-specified isotropic model. Alternatively, nonparametric replicates of the test statistic under isotropy can be used to waive the need for specifying a model. In this paper, we first present a general framework which allows for the integration of a selected nonparametric replication method into isotropy testing. We then conduct a large simulation study comprising application-like scenarios to assess the performance of tests with different parametric and nonparametric replication methods. In particular, we explore distortions in test size and power caused by model misspecification, and demonstrate the advantages of nonparametric replication in such scenarios.

为了验证空间点图的各向同性假设，提出了几种假设检验方法。大多数这些方法的特点是在各向同性的零假设下检验统计量的未知分布。逼近分布的参数化方法包括从用户指定的各向同性模型模拟模式。另外，可以使用各向同性下检验统计量的非参数重复来免除指定模型的需要。在本文中，我们首先提出了一个一般框架，该框架允许将选择的非参数复制方法集成到各向同性测试中。然后，我们进行了一个大型模拟研究，包括类似应用程序的场景，以评估不同参数和非参数复制方法的测试性能。特别地，我们探讨了由模型规格错误引起的测试尺寸和功率的扭曲，并展示了在这种情况下非参数复制的优势。

引用次数: 0

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 : 2025-06-01 Epub 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

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-06-01 Epub Date: 2025-03-29 DOI: 10.1016/j.spasta.2025.100897

Shifeng Yu , Xiaoyu Hu , Yehua Sheng , Chenmeng Zhao

Unlike a geographically weighted regression (GWR), a similarity and geographically weighted regression (SGWR) calculates weights using attribute and geographic similarities, thereby effectively improving the accuracy of the model. Nevertheless, SGWR does not set an attribute similarity bandwidth. This leads to its inability to measure the scale of variation of spatial processes in feature space. In addition, owing to the solving method used, SGWR can get stuck in local optima. To address these issues, this study proposed an improved similarity and geographically weighted regression model (SGWR-GD) that adds bandwidth to the attribute similarity kernel function. This parameter gives SGWR-GD the ability to measure the scale of change of spatial processes in the attribute dimension and thus enhances the flexibility of modelling. When solving the model, SGWR-GD first calculated the gradient of the model's Modified Akaike Information Criterion (AICc) with respect to the two bandwidths and the impact ratio. Subsequently, the optimal global solution of the model was obtained based on a gradient descent algorithm with box constraints. SGWR-GD and SGWR were applied to five different datasets and the accuracies of their fitting results were compared. SGWR-GD significantly improved the accuracy of the model compared to SGWR. In addition, the SGWR-GD stably determined the global optimal solution for each parameter. Simultaneously, the distribution of local residuals was also more stable.

与地理加权回归（GWR）不同，相似度和地理加权回归（SGWR）利用属性和地理相似度计算权重，从而有效地提高了模型的准确性。但是，SGWR不设置属性相似带宽。这导致其无法测量特征空间中空间过程的变化尺度。此外，由于采用的求解方法，SGWR可能陷入局部最优。为了解决这些问题，本研究提出了一种改进的相似度和地理加权回归模型（SGWR-GD），该模型在属性相似度核函数中增加了带宽。该参数使SGWR-GD能够在属性维度上度量空间过程的变化尺度，从而增强建模的灵活性。在求解模型时，SGWR-GD首先计算模型的修正赤池信息准则（Modified Akaike Information Criterion, AICc）相对于两个带宽和冲击比的梯度。随后，基于带框约束的梯度下降算法，得到了模型的全局最优解。将SGWR- gd和SGWR应用于5个不同的数据集，并比较了它们的拟合结果的精度。与SGWR相比，SGWR- gd显著提高了模型的精度。此外，SGWR-GD稳定地确定了各参数的全局最优解。同时，局部残差的分布也更加稳定。

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引用次数: 0

Nonparametric approaches for direct approximation of the spatial quantiles 直接逼近空间分位数的非参数方法

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-06-01 Epub Date: 2025-04-05 DOI: 10.1016/j.spasta.2025.100896

Pilar García-Soidán , Tomás R. Cotos-Yáñez

The estimation of the spatial quantiles provides information on the thresholds of a spatial variable. This methodology is particularly appealing for its application to data of pollutants, so as to assess their level of risk. A spatial quantile can be approximated through different mechanisms, proposed in the statistics literature, although these approaches suffer from several drawbacks, regarding their lack of optimality or the fact of not leading to direct approximations. Thus, the current work introduces alternative procedures, which try to overcome the aforementioned issues by employing order statistics, similarly as done for independent data. With this aim, the available observations are appropriately transformed to yield a sample of the process at each target site, so that the data obtained are then ordered and used to derive the spatial quantile at the corresponding location. The new methodology can be directly applied to data from processes that are either stationary or that deviate from this condition for a non-constant trend and, additionally, it can be even extended to heteroscedastic data. Simulation studies under different scenarios have been accomplished, whose results show the adequate performance of the proposed estimators. A further step of this research is the application of the new approaches to data of nitrogen dioxide concentrations, to exemplify the potential of the quantile estimates to check the thresholds of a pollutant at a specific moment, as well as their evolution over time.

空间分位数的估计提供了关于空间变量阈值的信息。这种方法特别有吸引力，因为它适用于污染物的数据，以便评估它们的风险水平。空间分位数可以通过统计文献中提出的不同机制进行近似，尽管这些方法存在一些缺点，例如缺乏最优性或无法直接近似。因此，目前的工作引入了替代程序，这些程序试图通过使用顺序统计来克服上述问题，类似于对独立数据所做的。为了达到这个目的，对现有的观测结果进行适当的转换，以产生每个目标地点的过程样本，以便对获得的数据进行排序，并用于推导相应位置的空间分位数。新方法可以直接应用于平稳或偏离此条件的非恒定趋势过程的数据，此外，它甚至可以扩展到异方差数据。在不同的场景下进行了仿真研究，结果表明所提出的估计器具有良好的性能。这项研究的下一步是应用二氧化氮浓度数据的新方法，以举例说明分位数估计在特定时刻检查污染物阈值的潜力，以及它们随时间的演变。

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