Spatial Statistics最新文献

英文中文

Navigating challenges in spatio-temporal modelling of Antarctic krill abundance: Addressing zero-inflated data and misaligned covariates 南极磷虾丰度时空建模中的导航挑战：解决零膨胀数据和错位协变量

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-12-01 Epub Date: 2025-09-26 DOI: 10.1016/j.spasta.2025.100937

André Victor Ribeiro Amaral , Adam M. Sykulski , Sophie Fielding , Emma Cavan

Antarctic krill (Euphausia superba) are among the most abundant species on our planet and serve as a vital food source for many marine predators in the Southern Ocean. In this paper, we utilise statistical spatio-temporal methods to combine data from various sources and resolutions, aiming to model krill abundance. Our focus lies in fitting the model to a dataset comprising acoustic measurements of krill biomass. To achieve this, we integrate climate covariates obtained from satellite imagery and from drifting surface buoys (also known as drifters). Additionally, we use sparsely collected krill biomass data obtained from net fishing efforts (KRILLBASE) for validation. However, integrating these multiple heterogeneous data sources presents significant modelling challenges, including spatio-temporal misalignment and inflated zeros in the observed data. To address these challenges, we fit a Hurdle-Gamma model to jointly describe the occurrence of zeros and the krill biomass for the non-zero observations, while also accounting for misaligned and heterogeneous data sources, including drifters. Therefore, our work presents a comprehensive framework for analysing and predicting krill abundance in the Southern Ocean, leveraging information from various sources and formats. This is crucial due to the impact of krill fishing, as understanding their distribution is essential for informed management decisions and fishing regulations aimed at protecting the species.

南极磷虾（Euphausia superba）是地球上最丰富的物种之一，是南大洋许多海洋捕食者的重要食物来源。在本文中，我们利用统计时空的方法来结合来自不同来源和分辨率的数据，旨在模拟磷虾丰度。我们的重点在于将模型拟合到包含磷虾生物量声学测量的数据集。为了实现这一目标，我们整合了从卫星图像和漂流水面浮标（也称为漂流浮标）获得的气候协变量。此外，我们使用从网捕捞努力中获得的稀疏收集的磷虾生物量数据（KRILLBASE）进行验证。然而，整合这些多个异构数据源带来了重大的建模挑战，包括观测数据中的时空错位和虚零。为了解决这些挑战，我们拟合了一个障碍-伽玛模型来共同描述非零观测值的零和磷虾生物量的发生，同时也考虑了不对齐和异构数据源，包括漂移。因此，我们的工作提出了一个综合框架，用于分析和预测南大洋磷虾丰度，利用各种来源和格式的信息。由于磷虾捕捞的影响，这一点至关重要，因为了解磷虾的分布对于明智的管理决策和旨在保护该物种的捕捞法规至关重要。

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引用次数: 0

Dynamic spatial regimes for spatial panel data 空间面板数据的动态空间机制

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-12-01 Epub Date: 2025-11-01 DOI: 10.1016/j.spasta.2025.100939

Anna Gloria Billé , Roberto Benedetti , Paolo Postiglione

Spatial heterogeneity in terms of spatially-varying coefficients is often not properly considered in modeling economic data. This neglect might cause serious problems in the estimation of the parameters of a model specification when group-wise heterogeneity is at work. In this paper we propose a two-step algorithm for the identification of endogenous (data-driven) spatial regimes by using an iterative procedure that is based on weighting functions updated dynamically over time. In the first step, clusters of spatial units (i.e. spatial regimes) are defined using both space and time information. In the second step, a spatial panel data model with random effects is estimated with the spatial regimes identified in the previous step. The additional random effects assumption on the model specification ensures the possibility of controlling also for individual effects as well as group-wise slope coefficients. The proposed method is applied to two real data sets to illustrate our procedure.

在经济数据建模中，往往没有适当地考虑空间变化系数的空间异质性。当群体异质性在起作用时，这种忽视可能会导致模型规范参数估计中的严重问题。在本文中，我们提出了一种两步算法，通过使用基于随时间动态更新的权重函数的迭代过程来识别内源性（数据驱动）空间制度。在第一步中，使用空间和时间信息定义空间单元簇（即空间状态）。在第二步中，利用前一步中识别的空间状态估计具有随机效应的空间面板数据模型。模型规范上附加的随机效应假设确保了控制个体效应和群体斜率系数的可能性。将该方法应用于两个实际数据集来说明我们的方法。

引用次数: 0

Spatial survival models based on Weibull random fields 基于威布尔随机场的空间生存模型

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-12-01 Epub Date: 2025-11-07 DOI: 10.1016/j.spasta.2025.100943

Christian Caamaño-Carrillo , Moreno Bevilacqua , Diego I. Gallardo

We propose a novel spatial survival model based on a Weibull random field, designed to overcome the limitations of existing copula-based approaches; particularly those relying on the Farlie–Gumbel–Morgenstern (FGM) copula. Although the FGM model only captures weak dependence and enforces reflection symmetry and a forced nugget effect, our model allows for stronger spatial dependence, reflection asymmetry, and mean-square continuity. These properties provide a more flexible and realistic framework for analyzing spatially correlated time-to-event data. The model unifies the proportional hazards (PH), the accelerated failure time (AFT), and the mean parameterizations of the Weibull distribution, allowing a clear interpretation of the effects of the covariates. Due to the analytical intractability of the full likelihood, parameter estimation is performed using a weighted pairwise composite likelihood method based on nearest neighbors. This method offers computational efficiency and robustness to right-censored data. Simulation studies confirm the effectiveness of the proposed model, and an application to real housing data illustrates its practical value.

我们提出了一种新的基于威布尔随机场的空间生存模型，旨在克服现有基于copula方法的局限性；特别是那些依靠法利-甘贝尔-摩根斯特恩（FGM）组合的人。尽管FGM模型只捕获弱依赖性并强制反射对称性和强制金块效应，但我们的模型允许更强的空间依赖性、反射不对称性和均方连续性。这些属性为分析空间相关的时间到事件数据提供了更灵活、更现实的框架。该模型统一了比例风险（PH）、加速失效时间（AFT）和威布尔分布的平均参数化，从而可以清楚地解释协变量的影响。由于全似然的分析困难，采用基于最近邻的加权两两复合似然方法进行参数估计。该方法对右截尾数据具有较高的计算效率和鲁棒性。仿真研究证实了该模型的有效性，并通过对实际房屋数据的应用说明了该模型的实用价值。

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引用次数: 0

A concordance coefficient for lattice data: An application to poverty indices in Chile 格点数据的一致性系数：在智利贫困指数中的应用

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-12-01 Epub Date: 2025-09-27 DOI: 10.1016/j.spasta.2025.100936

Ronny Vallejos , Clemente Ferrer , Jorge Mateu

This paper introduces a novel coefficient for measuring agreement between two lattice sequences observed in the same areal units, motivated by the analysis of different methodologies for measuring poverty rates in Chile. Building on the multivariate concordance coefficient framework, our approach accounts for dependencies in the multivariate lattice process using a non-negative definite matrix of weights, assuming a Multivariate Conditionally Autoregressive (GMCAR) process. We adopt a Bayesian perspective for inference, using summaries from Bayesian estimates. The methodology is illustrated through an analysis of poverty rates in the Metropolitan and Valparaíso regions of Chile, with High Posterior Density (HPD) intervals provided for the poverty rates. This work addresses a methodological gap in the understanding of agreement coefficients and enhances the usability of these measures in the context of social variables typically assessed in areal units.

本文介绍了一种新的系数，用于测量在同一面积单位中观察到的两个晶格序列之间的一致性，其动机是对智利测量贫困率的不同方法的分析。在多元一致性系数框架的基础上，我们的方法使用非负确定的权重矩阵来解释多元格过程中的依赖关系，假设一个多元条件自回归（GMCAR）过程。我们采用贝叶斯的观点进行推理，使用贝叶斯估计的总结。该方法通过对智利大都市和Valparaíso地区贫困率的分析来说明，并为贫困率提供了高后验密度（HPD）间隔。这项工作解决了在理解协议系数方面的方法差距，并提高了这些措施在通常以面积单位评估的社会变量背景下的可用性。

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 : 2025-10-01 Epub 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

Spatial robust fuzzy clustering of mixed data with electoral study 基于选举研究的混合数据空间鲁棒模糊聚类

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 Epub Date: 2025-07-02 DOI: 10.1016/j.spasta.2025.100914

Domenico Cangemi , Pierpaolo D’Urso , Livia De Giovanni , Lorenzo Federico , Vincenzina Vitale

A robust fuzzy clustering model for data with mixed features and spatial constraints is proposed to analyze the turnout and the preferences of the voters at the provincial level in the European elections. The 2024 European elections in Italy were held in June to elect the 76 members of the European Parliament due to Italy. The clustering model accommodates various types of variables or attributes by integrating dissimilarity measures for each one through a weighting approach. This method produces a composite distance (or dissimilarity) metric that captures multiple attribute types. The weights are determined objectively during the optimization process and indicate the importance of each attribute type. The model also incorporates robustness via the introduction of a Noise cluster, and accounts for a spatial component. The application shows consistency of the results both at the level of units’ attributes and at a spatial level.

提出了一种具有混合特征和空间约束数据的鲁棒模糊聚类模型，用于分析欧洲选举中省级选民的投票率和偏好。意大利于今年6月举行了2024年欧洲议会选举，选出了76名欧洲议会议员。聚类模型通过加权方法整合不同类型的变量或属性的不同度量，从而容纳不同类型的变量或属性。此方法生成捕获多个属性类型的复合距离（或不相似度）度量。权重是在优化过程中客观确定的，并表示各属性类型的重要程度。该模型还通过引入噪声聚类来整合鲁棒性，并考虑了空间分量。该应用程序显示了在单元属性级别和空间级别上结果的一致性。

引用次数: 0

A two-step sampling strategy to improve the prediction accuracy of contamination hotspots and identify hotspot boundaries 采用两步采样策略提高污染热点预测精度并识别热点边界

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 Epub Date: 2025-07-07 DOI: 10.1016/j.spasta.2025.100918

Joonmyoung Kim , Seonwoo Lee , Taekseon Ryu , Jonghyun Na , Taehyun Yun , Jeongho Lee , Hansuk Kim , Man Jae Kwon , Ho Young Jo , Yongsung Joo

Efficient soil remediation, both economically and environmentally, depends on accurate mapping of contaminant concentrations and boundaries of hotspots (areas with concentrations exceeding a critical threshold) through an effective allocation of limited soil sampling sites. This paper introduces a novel two-step sampling location selection method, referred to as the weighted stepwise spatial sampling (WSSS) method. The WSSS method is specifically designed to provide accurate estimates of contaminant concentrations within hotspots and their boundaries. In the first step, dispersed sampling locations are selected for broad exploration, while in the second step, guided by the digital soil mapping results based on the first-step sampling data, sampling locations are selected to focus on identifying potential hotspots. A simulation study using total petroleum hydrocarbon soil data from South Korea demonstrates the superior accuracy and stability of the WSSS in identifying hotspot boundaries and predicting contaminant concentrations within hotspots, compared to other sampling location selection methods. This performance is achieved through an objective function specifically designed to ensure that the selection of sampling locations in the second step is robust to potential inaccuracies or uncertainties in the initial predictions.

在经济上和环境上，有效的土壤修复取决于通过有效分配有限的土壤采样点来准确绘制污染物浓度和热点（浓度超过临界阈值的地区）的边界。本文介绍了一种新的两步采样位置选择方法，即加权逐步空间采样（WSSS）方法。WSSS方法是专门设计用于提供热点及其边界内污染物浓度的准确估计。第一步，选择分散的采样点进行广泛探索，第二步，在第一步采样数据的基础上，以数字土壤制图结果为指导，选择采样点，重点识别潜在热点。一项使用韩国总石油烃土壤数据的模拟研究表明，与其他采样地点选择方法相比，WSSS在识别热点边界和预测热点内污染物浓度方面具有更高的准确性和稳定性。这种性能是通过一个专门设计的目标函数来实现的，该目标函数旨在确保第二步中采样位置的选择对初始预测中的潜在不准确性或不确定性具有鲁棒性。

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引用次数: 0

Sampling design for binary geostatistical data, application to inspection actions of fishing activity in Portugal 二元地质统计数据的抽样设计，在葡萄牙渔业活动检查行动中的应用

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 Epub Date: 2025-07-21 DOI: 10.1016/j.spasta.2025.100919

Belchior Miguel , Paula Simões , Rui Gonçalves de Deus , Isabel Natário

The definition of surveillance routes is a very important but complex issue. The Portuguese Navy, in its common form of operation is in charge of the Naval Standard Device, which is distributed throughout the various coastal areas of the country. Enforcement actions can involve very high costs, so a good plan for the sampling designs used are in order, as to maximize the efficiency in obtaining information from the data of the actions developed over the area under consideration. The main objective of this study is to propose sampling design criteria based on geostatistical models, in the context of binary data on presumed maritime infractions in the Portuguese coast, that are advantageous in the optimization of maritime surveillance actions, in terms of efforts employed in their execution, in the maritime area of Portugal’s responsibility. Two sampling design selection criteria are proposed: Maximum Risk Sampling design and Maximum Variance Risk Sampling Design. These are compared to the simple random design by the root mean square error (RMSE). A comparison of the designs at different sample sizes is made and the estimated risk maximization sampling design presents the best RMSE value. The proposed sampling designs may assist in the creation of alternative enforcement Portuguese Navy routes, optimizing the scheduling that maximizes the probability of finding a higher number of presumed fishing perpetrators with less resource efforts.

监测路线的确定是一个非常重要而又复杂的问题。葡萄牙海军在其共同的行动形式中负责海军标准装置，该装置分布在该国各个沿海地区。执法行动可能涉及非常高的成本，因此，为所使用的抽样设计制定一个良好的计划是有必要的，以便最大限度地从所考虑的地区开展的行动的数据中获得信息。本研究的主要目的是提出基于地质统计模型的抽样设计标准，在葡萄牙海岸推定的海事违规行为的二进制数据的背景下，这有利于在葡萄牙负责的海事区域内优化海事监视行动，就其执行所采取的努力而言。提出了两种抽样设计选择准则：最大风险抽样设计和最大方差风险抽样设计。通过均方根误差（RMSE）将这些与简单随机设计进行比较。对不同样本量下的设计进行了比较，发现风险最大化的估计样本量设计呈现出最佳的RMSE值。建议的抽样设计可能有助于创建葡萄牙海军的替代执法路线，优化调度，最大限度地利用较少的资源努力找到更多的推定捕鱼肇事者。

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引用次数: 0

Estimation and testing of time-varying coefficients spatial autoregressive panel data model 时变系数空间自回归面板数据模型的估计与检验

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 Epub Date: 2025-07-24 DOI: 10.1016/j.spasta.2025.100922

Lingling Tian , Chuanhua Wei , Wenxing Ding , Mixia Wu

This paper investigates a spatial autoregressive (SAR) panel data model featuring fixed effects and time-varying coefficients in both the covariates and spatial dependence. We propose a two-stage least squares estimation based on local linear dummy variables (2SLS-LLDV). This method effectively captures individual heterogeneity via dummy variable construction while maintaining computational tractability. Under mild regularity conditions, we establish the asymptotic normality of the proposed estimators. Furthermore, we devise a residual-based bootstrap procedure to test the temporal stability of time-varying spatial dependence parameter, providing a robust mechanism for p-value calculation in finite-sample scenarios. Monte Carlo simulations are conducted to evaluate the finite sample performance of our proposed methods. Finally, we employ our proposed estimation and testing methods to analyze carbon emissions in China and cigarette demand in the United States, demonstrating their practical applicability.

本文研究了具有固定效应和时变系数的空间自回归面板数据模型，该模型具有协变量和空间相关性。我们提出了一种基于局部线性虚拟变量的两阶段最小二乘估计（2SLS-LLDV）。该方法通过虚拟变量构造有效捕获个体异质性，同时保持计算可跟踪性。在温和正则性条件下，我们建立了所提估计量的渐近正态性。此外，我们设计了一个基于残差的自举过程来测试时变空间依赖参数的时间稳定性，为有限样本场景下的p值计算提供了一个稳健的机制。通过蒙特卡罗模拟来评估我们提出的方法的有限样本性能。最后，运用本文提出的估算和检验方法对中国的碳排放和美国的卷烟需求进行了分析，验证了其实用性。

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 : 2025-10-01 Epub 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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