Spatial Statistics最新文献_第4页

Model averaging for spatial autoregressive panel data models 空间自回归面板数据模型的模型平均

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-23 DOI: 10.1016/j.spasta.2025.100931

Aibing Ji, Jingxuan Li, Qingqing Li

The spatial autoregressive panel data models are widely employed in regional economics to capture spatial dependencies, but conventional specifications rely on a single spatial weight matrix, heightening the risk of model misspecification. Current research lacks systematic model averaging methods for integrating multiple weight matrices and addressing spatial effect uncertainty. This study proposes a novel model averaging framework for spatial autoregressive panel data models with fixed effects, extending model averaging methodology to the spatial panel context and enabling flexible combinations of multiple weight matrices for both dependent variables and error terms. An adaptive Mallows-type criterion is developed, dynamically adjusting to the presence or absence of spatial effects, with its asymptotic optimality established. Monte Carlo simulations confirm robustness across scenarios with no, single, or mixed spatial dependencies. An empirical application to Chinese provincial housing prices identifies economic adjacency as the key spatial dependence driver, validating the method’s predictive accuracy and policy utility for spatiotemporal data analysis.

空间自回归面板数据模型在区域经济学中被广泛应用于捕获空间依赖关系，但传统的规范依赖于单一的空间权重矩阵，增加了模型错误规范的风险。目前的研究缺乏系统的模型平均方法来积分多个权重矩阵和处理空间效应的不确定性。本研究为具有固定效应的空间自回归面板数据模型提出了一种新的模型平均框架，将模型平均方法扩展到空间面板环境，并实现了因变量和误差项的多个权重矩阵的灵活组合。提出了一种自适应mallows型准则，根据空间效应的存在或不存在进行动态调整，并建立了其渐近最优性。蒙特卡罗模拟证实了在没有、单一或混合空间依赖性的情况下的鲁棒性。通过对中国省级房价的实证分析，发现经济邻接性是主要的空间依赖驱动因素，验证了该方法在时空数据分析中的预测准确性和政策实用性。

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引用次数: 0

Uncertain spatial autoregressive model with applications to regional economic analysis and regional air quality analysis 不确定空间自回归模型及其在区域经济分析和区域空气质量分析中的应用

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-22 DOI: 10.1016/j.spasta.2025.100932

Jinsheng Xie

This study aims to establish uncertain spatial statistics by exploring the uncertain spatial autoregressive model firstly. Modeling the observations of the response variable via uncertain variables and assuming they are affected by neighboring observations, this paper explores an approach of the uncertain spatial autoregressive model to estimate relationships among the uncertain variables with spatial locations. By employing the principle of least squares, a minimization problem is provided to estimate unknown parameters in the uncertain spatial autoregressive model. Finally, two real-world examples of regional economic analysis and regional air quality analysis are given to clearly demonstrate the uncertain spatial autoregressive model.

本研究旨在通过探索不确定空间自回归模型，建立不确定空间统计量。本文通过不确定变量对响应变量的观测值进行建模，并假设它们受到相邻观测值的影响，探索了一种不确定空间自回归模型来估计不确定变量与空间位置之间关系的方法。利用最小二乘原理，给出了不确定空间自回归模型中未知参数估计的最小化问题。最后，以区域经济分析和区域空气质量分析为例，对不确定空间自回归模型进行了论证。

引用次数: 0

Joint model for zero-inflated data combining fishery-dependent and fishery-independent sources 结合渔业依赖和渔业独立来源的零膨胀数据联合模型

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-11 DOI: 10.1016/j.spasta.2025.100930

Daniela Silva , Raquel Menezes , Gonçalo Araújo , Renato Rosa , Ana Moreno , Alexandra Silva , Susana Garrido

Accurately identifying spatial patterns of species distribution is crucial for scientific insight and societal benefit, aiding our understanding of species fluctuations. The increasing quantity and quality of ecological datasets present heightened statistical challenges, complicating spatial species dynamics comprehension. Addressing the complex task of integrating multiple data sources to enhance spatial fish distribution understanding in marine ecology, this study introduces a pioneering five-layer Joint model. The model adeptly integrates fishery-independent and fishery-dependent data, accommodating zero-inflated data and distinct sampling processes. A comprehensive simulation study evaluates the model performance across various preferential sampling scenarios and sample sizes, elucidating its advantages and challenges. Our findings highlight the model’s robustness in estimating preferential parameters, emphasizing differentiation between presence–absence and biomass observations. Evaluation of estimation of spatial covariance and prediction performance underscores the model’s reliability. Augmenting sample sizes reduces parameter estimation variability, aligning with the principle that increased information enhances certainty. Assessing the contribution of each data source reveals successful integration, providing a comprehensive representation of biomass patterns. Empirical application within a real-world context further solidifies the model’s efficacy in capturing species’ spatial distribution. This research advances methodologies for integrating diverse datasets with different sampling natures further contributing to a more informed understanding of spatial dynamics of marine species.

准确识别物种分布的空间模式对于科学洞察力和社会效益至关重要，有助于我们理解物种波动。生态数据集的数量和质量的不断提高对统计提出了更高的挑战，使空间物种动态的理解复杂化。为了解决整合多个数据源以增强海洋生态学中鱼类空间分布认识的复杂任务，本研究引入了一个开创性的五层联合模型。该模型巧妙地整合了渔业独立和渔业依赖的数据，适应零膨胀数据和不同的采样过程。综合仿真研究评估了模型在各种优先采样场景和样本量下的性能，阐明了其优势和挑战。我们的发现突出了模型在估计优先参数方面的稳健性，强调了存在-缺失和生物量观测之间的区别。对空间协方差估计和预测性能的评价强调了模型的可靠性。增加样本量减少了参数估计的可变性，与增加的信息增强确定性的原则保持一致。评估每个数据源的贡献揭示了成功的整合，提供了生物量模式的全面表示。在现实环境中的经验应用进一步巩固了模型在捕捉物种空间分布方面的有效性。这项研究推进了整合不同采样性质的不同数据集的方法，进一步有助于对海洋物种的空间动态有更深入的了解。

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引用次数: 0

Variational autoencoded multivariate spatial Fay–Herriot models 变分自编码多元空间费-赫里奥特模型

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-09-10 DOI: 10.1016/j.spasta.2025.100929

Zhenhua Wang , Paul A. Parker , Scott H. Holan

Small area estimation models are essential for estimating population characteristics in regions with limited sample sizes, thereby supporting policy decisions, demographic studies, and resource allocation, among other use cases. The spatial Fay–Herriot model is one such approach that incorporates spatial dependence to improve estimation by borrowing strength from neighboring regions. However, this approach often requires substantial computational resources, limiting its scalability for high-dimensional datasets, especially when considering multiple (multivariate) responses. This paper proposes two methods that integrate the multivariate spatial Fay–Herriot model with spatial random effects, learned through variational autoencoders, to efficiently leverage spatial structure. Importantly, after training the variational autoencoder to represent spatial dependence for a given set of geographies, it may be used again in future modeling efforts, without the need for retraining. Additionally, the use of the variational autoencoder to represent spatial dependence results in extreme improvements in computational efficiency, even for massive datasets. We demonstrate the effectiveness of our approach using 5-year period estimates from the American Community Survey over all census tracts in California.

小区域估计模型对于在样本数量有限的地区估计人口特征是必不可少的，因此支持政策决策、人口统计研究和资源分配，以及其他用例。空间Fay-Herriot模型就是这样一种方法，它结合了空间依赖性，通过借鉴邻近区域的强度来改进估计。然而，这种方法通常需要大量的计算资源，限制了其对高维数据集的可伸缩性，特别是在考虑多个（多变量）响应时。本文提出了两种将变分自编码器学习到的多元空间Fay-Herriot模型与空间随机效应相结合的方法，以有效利用空间结构。重要的是，在训练变分自编码器以表示给定地理集合的空间依赖性之后，它可以在未来的建模工作中再次使用，而无需再训练。此外，使用变分自编码器来表示空间依赖性可以极大地提高计算效率，即使对于大量数据集也是如此。我们使用美国社区调查对加州所有人口普查区的5年估计来证明我们方法的有效性。

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引用次数: 0

Regime changes and spatial dependence in the 2020 US presidential election polls 2020年美国总统大选民意调查中的政权更迭和空间依赖

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-08-26 DOI: 10.1016/j.spasta.2025.100927

Giampiero M. Gallo , Demetrio Lacava , Edoardo Otranto

This paper introduces a novel two-stage modeling framework that combines Markov Switching (MS) models with an autoregressive model augmented by spatial effects to analyze the dynamics and spatial interdependence of Biden’s polling percentages during the 2020 electoral campaign. In the first stage, we employ MS models to segment each state’s daily polling time series into distinct regimes — interpreted as phases of decline, stability, and growth. This segmentation captures abrupt changes and local trends in public opinion, enabling us to link regime shifts with key political events such as debates, party conventions, and milestone campaign achievements. The inherent nonlinearity of polling data would otherwise be lost by first differencing. By removing the regime-specific components, we generate stationary residuals modeled using an Autoregressive model with exogenous variables (ARX) that incorporates political spatial interactions through two complementary effects. The spillover effect captures lagged influences arising from politically influential states, while the contagion effect reflects the contemporaneous impact of neighboring states. A recursive algorithm based on partial correlations is implemented to select the most relevant spillover sources for each state. Empirical results, based on daily data from 13 swing states, reveal robust evidence of persistent regime structures and marked spatial dependencies. While contagion effects are uniformly significant across states, spillover dynamics exhibit considerable heterogeneity in both magnitude and direction. This integrated modeling approach enhances our understanding of the complex, nonlinear temporal evolution of polling trends and the spatial diffusion of political opinions that underpinned the 2020 electoral outcome.

本文提出了一种新的两阶段建模框架，将马尔可夫切换（MS）模型与空间效应增强的自回归模型相结合，分析了拜登2020年大选期间民调百分比的动态和空间相互依赖性。在第一阶段，我们使用MS模型将每个州的日常投票时间序列划分为不同的制度-解释为下降，稳定和增长的阶段。这种划分捕捉到了公众舆论的突然变化和当地趋势，使我们能够将政权更迭与关键政治事件（如辩论、政党大会和里程碑式的竞选成就）联系起来。否则，轮询数据固有的非线性就会因第一次差分而丧失。通过去除特定于政权的成分，我们使用带有外生变量（ARX）的自回归模型生成平稳残差，该模型通过两种互补效应整合了政治空间相互作用。溢出效应反映的是政治上有影响力的国家产生的滞后影响，而传染效应反映的是邻国同时产生的影响。采用一种基于部分相关性的递归算法，为每个状态选择最相关的溢出源。基于13个摇摆州的日常数据，实证结果揭示了持久的政权结构和显著的空间依赖性的有力证据。虽然传染效应在各州都同样显著，但溢出动态在大小和方向上都表现出相当大的异质性。这种综合建模方法增强了我们对民意调查趋势的复杂、非线性时间演变的理解，以及支撑2020年选举结果的政治观点的空间扩散。

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引用次数: 0

GMM inference for the spatial autoregressive kink model with an unknown threshold 未知阈值空间自回归扭结模型的GMM推理

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-08-22 DOI: 10.1016/j.spasta.2025.100926

Wentao Wang , Dengkui Li

This paper considers spatial autoregressive kink models with an unknown threshold, where the impact of a specific explanatory variable on the response variable is piecewise linear but differs below and above this threshold. To address the endogeneity issue, the paper presents the modified generalized method of moments (GMM) that consistently estimates the threshold location and slope changes. Asymptotic properties, including the consistency and asymptotic normality of the GMM estimators, and the limiting distribution of the Sup-Wald statistic, are established under a set of regularity assumptions. In view of the nonstandard asymptotic null distribution, we use a multiplier bootstrap to approximate the

p

-value of the Sup-Wald statistic to detect the presence of the threshold. Simulation study illustrates that the estimators and inference are well-behaved in finite samples. An empirical application to the secondary industrial structure data of 280 Chinese prefecture-level cities further highlights the practical merits of our methods.

本文考虑具有未知阈值的空间自回归扭结模型，其中特定解释变量对响应变量的影响是分段线性的，但在该阈值以下和以上有所不同。为了解决内生性问题，本文提出了改进的广义矩量法（GMM），该方法可以一致地估计阈值位置和斜率变化。在一组正则性假设下，建立了GMM估计量的渐近性质，包括一致性和渐近正态性，以及Sup-Wald统计量的极限分布。考虑到非标准渐近零分布，我们使用乘法器自举来近似Sup-Wald统计量的p值来检测阈值的存在。仿真研究表明，该估计器和推理器在有限样本下表现良好。对中国280个地级市第二产业结构数据的实证应用进一步凸显了本文方法的实用性。

引用次数: 0

Conformal novelty detection for replicate point patterns with FDR or FWER control 用FDR或FWER控制复制点模式的保形新颖性检测

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-08-05 DOI: 10.1016/j.spasta.2025.100924

Christophe A.N. Biscio , Adrien Mazoyer , Martin V. Vejling

Monte Carlo tests are widely used for computing valid

p

-values without requiring known distributions of test statistics. When performing multiple Monte Carlo tests, it is essential to maintain control of the type I error. Some techniques for multiplicity control pose requirements on the joint distribution of the

p

-values, for instance independence, which can be computationally intensive to achieve, as it requires simulating disjoint null samples for each test. We refer to this as naïve multiple Monte Carlo testing. We highlight in this work that multiple Monte Carlo testing is an instance of conformal novelty detection. Leveraging this insight enables a more efficient multiple Monte Carlo testing procedure, avoiding excessive simulations by using a single null sample for all the tests, while still ensuring exact control over the false discovery rate or the family-wise error rate. We call this approach conformal multiple Monte Carlo testing. The performance is investigated in the context of global envelope tests for point pattern data through a simulation study and an application to a sweat gland data set. Results reveal that with a fixed simulation budget, our proposed method yields substantial improvements in power of the testing procedure as compared to the naïve multiple Monte Carlo testing procedure.

蒙特卡罗检验被广泛用于计算有效的p值，而不需要已知的检验统计量分布。在执行多个蒙特卡罗测试时，必须保持对第一类误差的控制。一些多重性控制技术对p值的联合分布提出了要求，例如独立性，这可能需要大量的计算才能实现，因为它需要为每个测试模拟不相交的零样本。我们将此称为naïve多重蒙特卡罗测试。在这项工作中，我们强调多重蒙特卡罗测试是保角新颖性检测的一个实例。利用这种洞察力可以实现更高效的多个蒙特卡罗测试过程，避免通过对所有测试使用单个零样本来进行过度模拟，同时仍然确保对错误发现率或家庭错误率进行精确控制。我们称这种方法为保形多重蒙特卡洛测试。通过模拟研究和对汗腺数据集的应用，在点模式数据的全局包络测试的背景下研究了性能。结果表明，在固定的模拟预算下，与naïve多重蒙特卡罗测试过程相比，我们提出的方法在测试过程的功率方面有了实质性的改进。

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引用次数: 0

Spatiotemporal dynamics of COVID-19 in Wuhan based on community notifications 基于社区通报的武汉市新冠肺炎疫情时空动态分析

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-07-29 DOI: 10.1016/j.spasta.2025.100925

Gang Xu , Qirui Zhang , Xinlei Xu , Yajie Zhang , Yansheng Li

Understanding the fine-scale spatial dynamics of infectious disease outbreaks is essential for effective urban epidemic response. This study leverages a novel dataset of over 2700 community-level epidemic notifications, shared publicly in residential areas and through social media during the early COVID-19 outbreak in Wuhan, China, to map the intra-urban spread of the virus from February 2 to March 4, 2020. After manually structuring and geocoding these notifications, we constructed a high-resolution spatiotemporal dataset of 13,346 confirmed cases across 1532 neighborhoods. Using spatial statistical techniques, we identified the evolution of spatial clustering, directional shifts in epidemic centers, and seven statistically significant spatio-temporal clusters with relative risks ranging from 1.21 to 12.48. Our results reveal the critical role of urban morphology, population density, and built environment characteristics in shaping transmission dynamics. Notably, Qingshan District emerged as a persistent hotspot due to its open neighborhood design and delayed compliance with containment measures. This research underscores the value of Volunteered Geographic Information (VGI) for early, fine-scale epidemic monitoring and demonstrates its utility as a complement to official surveillance systems in public emergencies.

了解传染病暴发的精细尺度空间动态对于有效的城市流行病应对至关重要。本研究利用了一个新的数据集，该数据集包含2700多个社区一级的疫情通报，这些通报是在中国武汉COVID-19早期爆发期间在居民区和通过社交媒体公开共享的，以绘制2020年2月2日至3月4日期间该病毒在城市内的传播情况。在对这些通知进行手动结构化和地理编码后，我们构建了一个高分辨率的时空数据集，其中包含1532个社区的13346例确诊病例。利用空间统计技术，我们确定了空间聚类的演变，疫情中心的方向转移，以及7个具有统计意义的时空聚类，相对风险范围为1.21 ~ 12.48。我们的研究结果揭示了城市形态、人口密度和建筑环境特征在塑造传播动态方面的关键作用。值得注意的是，青山区由于其开放的社区设计和遏制措施的延迟执行而成为持续的热点。本研究强调了志愿地理信息（VGI）在早期、精细流行病监测方面的价值，并展示了其在公共紧急情况下作为官方监测系统补充的效用。

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引用次数: 0

Physics-driven dynamic interpolation with application to pollution satellite images 物理驱动的动态插值及其在污染卫星图像中的应用

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-07-28 DOI: 10.1016/j.spasta.2025.100923

Won Chang , Youngdeok Hwang , Hang J. Kim

Satellite images using multiple wavelength channels provide crucial measurements over large areas, aiding the understanding of pollution generation and transport. However, these images often contain missing data due to cloud cover and algorithm limitations. In this paper, we introduce a novel method for interpolating missing values in satellite images by incorporating pollution transport dynamics influenced by wind patterns. Our approach utilizes a fundamental physics equation to structure the covariance of missing data, improving accuracy by considering pollution transport dynamics. To address computational challenges associated with large datasets, we implement a gradient ascent algorithm. We demonstrate the effectiveness of our method through a case study, showcasing its potential for accurate interpolation in high-resolution, spatio-temporal air pollution datasets.

使用多波长通道的卫星图像提供了对大面积的重要测量，有助于了解污染的产生和运输。然而，由于云层覆盖和算法限制，这些图像经常包含丢失的数据。本文介绍了一种结合风型影响的污染传输动力学的卫星图像缺失值插值新方法。我们的方法利用一个基本的物理方程来构建缺失数据的协方差，通过考虑污染传输动力学来提高准确性。为了解决与大型数据集相关的计算挑战，我们实现了梯度上升算法。我们通过一个案例研究证明了我们方法的有效性，展示了它在高分辨率、时空空气污染数据集中精确插值的潜力。

引用次数: 0

A penalized estimation of the variogram and effective sample size 对变异函数和有效样本量的一种惩罚估计

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-07-26 DOI: 10.1016/j.spasta.2025.100921

Jonathan Acosta , Ronny Vallejos , Pilar García-Soidán

The variogram function plays a key role in modeling intrinsically stationary random fields, especially in spatial prediction using kriging equations. However, determining whether a computed variogram accurately fits the underlying dependence structure can be challenging. Current nonparametric estimators often fail to guarantee a conditionally negative definite function. In this paper, we propose a new valid variogram estimator, constructed as a linear combination of functions from a predefined class, ensuring it meets essential mathematical properties. A penalty coefficient is introduced to prevent overfitting, reducing spurious fluctuations in the estimated variogram. We also extend the concept of effective sample size (ESS), an important metric in spatial regression, to a nonparametric framework. Our ESS estimator is based on the reciprocal of the average correlation and is calculated using a plug-in approach, with the consistency of the estimator being demonstrated. The performance of these estimates is investigated through Monte Carlo simulations across various scenarios. Finally, we apply the methodology to rasterized forest images, illustrating both the strengths and limitations of the proposed approach.

变异函数在固有平稳随机场的建模中起着关键作用，特别是在利用克里格方程进行空间预测时。然而，确定计算的变异图是否准确地符合潜在的依赖结构可能是具有挑战性的。目前的非参数估计方法往往不能保证有条件的负定函数。在本文中，我们提出了一种新的有效变差估计量，它是由一个预定义类的函数的线性组合构造而成，并保证了它满足基本的数学性质。引入惩罚系数以防止过拟合，减少估计变异图中的虚假波动。我们还将有效样本量（ESS）的概念扩展到非参数框架，这是空间回归中的一个重要度量。我们的ESS估计器基于平均相关性的倒数，并使用插件方法计算，并演示了估计器的一致性。这些估计的性能是通过蒙特卡罗模拟在各种情况下进行研究的。最后，我们将该方法应用于栅格化森林图像，说明了所提出方法的优点和局限性。

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引用次数: 0