Spatial Statistics最新文献

英文中文

Geographically informed graph neural networks 地理信息图神经网络

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 Epub Date: 2025-07-15 DOI: 10.1016/j.spasta.2025.100920

Xuankai Ma , Zehua Zhang , Yongze Song

Graph neural networks (GNNs) have been introduced to spatial statistical tasks due to their mechanisms of simulating spatial interactions and processes among geographical neighbours using graph structures. However, previous methods ignore quantifying differences in attributes among adjacent spatial characteristics. Considering this spatial characteristic by fitting the spatial statistic trinity (SST) framework may help improve models’ accuracy and robustness. Thus, we introduce the geographically informed graph neural network (GIGNN) by considering the additional geospatial feature: closer geographical entities may interact less when spatial disparities are captured. When setting up the model, GIGNN leverages differences of attributes by spatial stratified heterogeneity, quantifies connections between geographical entities, and inherits k-order neighbour attribute aggregation and message-passing mechanisms from GNNs. GIGNN is applied to an urbanization analysis study in the Greater Perth Area, Australia, showing higher accuracy than the existing machine learning models and other GNNs in simulation and prediction accuracy. GIGNN achieved an accuracy of 84.1% for simulation and an accuracy of 81% for prediction. Incorporating spatial characteristics into GNNs enhances simulation and prediction accuracy in geoscientific applications, highlighting the importance of spatially aware models in solving complex problems by capturing geographical data dependencies.

图神经网络（gnn）由于其利用图结构模拟地理邻居之间的空间相互作用和过程的机制而被引入到空间统计任务中。然而，以往的方法忽略了相邻空间特征之间属性差异的量化。通过拟合空间统计三位一体（SST）框架来考虑这一空间特征有助于提高模型的准确性和鲁棒性。因此，我们通过考虑额外的地理空间特征来引入地理信息图神经网络（GIGNN）：当空间差异被捕获时，距离较近的地理实体可能交互较少。在建立模型时，GIGNN通过空间分层异质性利用属性差异，量化地理实体之间的联系，并继承了gnn的k阶邻居属性聚合和消息传递机制。将GIGNN应用于澳大利亚大珀斯地区的城市化分析研究，在模拟和预测精度上均优于现有的机器学习模型和其他gnn。GIGNN的模拟精度为84.1%，预测精度为81%。将空间特征纳入gnn可以提高地球科学应用中的模拟和预测精度，突出了空间感知模型在通过捕获地理数据依赖性来解决复杂问题方面的重要性。

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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 : 2025-10-01 Epub 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

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 : 2025-10-01 Epub 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 : 2025-10-01 Epub 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 : 2025-10-01 Epub 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

Spatio-temporal intensity estimation for inhomogeneous Poisson point processes on linear networks: A roughness penalty method 线性网络上非齐次泊松点过程的时空强度估计：一种粗糙度惩罚方法

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 Epub Date: 2025-07-15 DOI: 10.1016/j.spasta.2025.100912

Simone Panzeri , Aldo Clemente , Eleonora Arnone , Jorge Mateu , Laura M. Sangalli

Nowadays, a vast amount of georeferenced data pertains to human and natural activities occurring in complex network-constrained regions, such as road or river networks. In this article, our research focuses on spatio-temporal point patterns evolving over time on linear networks, which we model as inhomogeneous Poisson point processes. Within this framework, we propose an innovative nonparametric method for intensity estimation that leverages penalized maximum likelihood with roughness penalties based on differential operators applied across space and time. We provide an efficient implementation of the proposed method, relying on advanced computational and numerical techniques that involve finite element discretizations on linear networks. We validate the method through simulation studies conducted across various scenarios, evaluating its performance compared to state-of-the-art competitors. Finally, we illustrate the method through an application to road accident data recorded in the municipality of Bergamo, Italy, during the years 2017–2019.

如今，大量的地理参考数据与发生在复杂网络约束区域（如道路或河流网络）的人类和自然活动有关。在本文中，我们的研究重点是在线性网络上随时间演变的时空点模式，我们将其建模为非齐次泊松点过程。在此框架内，我们提出了一种创新的非参数强度估计方法，该方法利用基于跨空间和时间应用的微分算子的粗糙度惩罚的惩罚最大似然。我们提供了一个有效的实现所提出的方法，依靠先进的计算和数值技术，涉及线性网络上的有限元离散化。我们通过在各种情况下进行的模拟研究来验证该方法，并与最先进的竞争对手相比评估其性能。最后，我们通过对意大利贝加莫市2017-2019年记录的道路事故数据的应用来说明该方法。

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引用次数: 0

Spatial–temporal prediction of forest attributes using latent Gaussian models and inventory data 基于隐高斯模型和清查数据的森林属性时空预测

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 Epub Date: 2025-07-22 DOI: 10.1016/j.spasta.2025.100917

Paul B. May , Andrew O. Finley

The USDA Forest Inventory and Analysis (FIA) program conducts a national forest inventory for the United States through a network of permanent field plots. FIA produces estimates of area averages and totals for plot-measured forest variables through design-based inference, assuming a fixed population and a probability sample of field plot locations. The fixed-population assumption and characteristics of the FIA sampling scheme make it difficult to estimate change in forest variables over time using design-based inference. We propose spatial–temporal models based on Gaussian processes as a flexible tool for forest inventory data, capable of inferring forest variables and change thereof over arbitrary spatial and temporal domains. It is shown to be beneficial for the covariance function governing the latent Gaussian process to account for variation at multiple scales, separating spatially local variation from ecosystem-scale variation. We demonstrate a model for forest biomass density, inferring 20 years of biomass change within two US National Forests.

美国农业部森林清查与分析（FIA）项目通过一个永久性田间小区网络为美国进行全国森林清查。FIA通过基于设计的推断，假设一个固定的人口和野外地块位置的概率样本，对地块测量的森林变量的面积平均值和总量进行估计。固定种群假设和FIA抽样方案的特征使得使用基于设计的推理来估计森林变量随时间的变化变得困难。我们提出基于高斯过程的时空模型作为森林清查数据的灵活工具，能够推断森林变量及其在任意时空域的变化。研究表明，控制潜在高斯过程的协方差函数有助于解释多尺度的变化，将空间局部变化与生态系统尺度变化分离开来。我们展示了一个森林生物量密度模型，推断了两个美国国家森林20年来的生物量变化。

引用次数: 0

Some peculiar families of correlation functions 一些特殊的相关函数族

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-10-01 Epub Date: 2025-07-08 DOI: 10.1016/j.spasta.2025.100915

D. Posa

In this paper a generalization of some families of correlation functions has been proposed; in particular, a generalization of the rational correlation family, as well as a generalization of a subclass of Matérn family have given, together with some relevant properties involving the two classes. Moreover, an extension of the subclass of Matérn family for the two-dimensional and three-dimensional Euclidean spaces has been provided; in addition, the importance of the proposed models for analysing temporal, spatial and, more generally, spatio-temporal data has been underlined, since the same models can be utilized to construct separable as well as non separable correlation functions. It will be shown that these new classes of models are flexible enough to describe both positive and negative correlation structures. On the other hand, with respect to the classical negative correlation models, the proposed families present some features which cannot be found in the same classical negative correlation functions: these relevant properties allow to get new flexible models, which can be helpful for practitioners to accommodate further case studies, as will be shown through some applications.

本文对一些相关函数族进行了推广；特别地，给出了有理相关族的推广，以及matsamyn族的一个子类的推广，以及涉及这两个类的一些相关性质。此外，还给出了二维和三维欧几里得空间的mat族子类的推广；此外，还强调了所提出的模型对分析时间、空间和更一般的时空数据的重要性，因为同样的模型可以用来构建可分离和不可分离的相关函数。这将表明，这些新的模型类是足够灵活的描述正相关和负相关结构。另一方面，就经典负相关模型而言，所提出的家族呈现出一些在相同的经典负相关函数中找不到的特征：这些相关属性允许获得新的灵活模型，这有助于从业者适应进一步的案例研究，正如将通过一些应用程序所示。

引用次数: 0

Flexible space–time models for extreme data 极端数据的灵活时空模型

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-08-01 Epub Date: 2025-07-14 DOI: 10.1016/j.spasta.2025.100916

Lorenzo Dell’Oro , Carlo Gaetan

Extreme value analysis is an essential methodology in the study of rare and extreme events, which hold significant interest in various fields, particularly in the context of environmental sciences. Models that employ the exceedances of values above suitably selected high thresholds possess the advantage of capturing the “sub-asymptotic” dependence of data. This paper presents an extension of spatial random scale mixture models to the spatio-temporal domain. A comprehensive framework for characterizing the dependence structure of extreme events across both dimensions is provided. Indeed, the model is capable of distinguishing between asymptotic dependence and independence, both in space and time, through the use of parametric inference. The high complexity of the likelihood function for the proposed model necessitates a simulation approach based on neural networks for parameter estimation, which leverages summaries of the sub-asymptotic dependence present in the data. The effectiveness of the model in assessing the limiting dependence structure of spatio-temporal processes is demonstrated through both simulation studies and an application to rainfall datasets.

极端值分析是研究罕见和极端事件的基本方法，在各个领域，特别是在环境科学的背景下，具有重要的意义。采用超出适当选择的高阈值的值的模型具有捕获数据的“次渐近”依赖性的优势。本文提出了一种将空间随机尺度混合模型扩展到时空域的方法。提供了表征极端事件在两个维度上的依赖结构的综合框架。事实上，通过使用参数推理，该模型能够在空间和时间上区分渐近依赖和独立。所提出模型的似然函数的高度复杂性需要基于神经网络进行参数估计的仿真方法，该方法利用数据中存在的次渐近依赖性的摘要。通过模拟研究和对降雨数据集的应用，证明了该模型在评估时空过程的极限依赖结构方面的有效性。

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Attribute based spatial segmentation for optimising POI placement 基于属性的空间分割优化POI位置

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-08-01 Epub Date: 2025-06-21 DOI: 10.1016/j.spasta.2025.100911

M. de Klerk, I. Fabris-Rotelli

Effective spatial planning and resource optimisation require precise demarcation of potential spatial accessible areas and optimal placement of points of interest (POIs). Our approach introduces a novel attribute based spatial segmentation methodology that utilises an iterative clustering approach to create unique macro-regions, each associated with key structural and attribute specific properties. By integrating a probabilistic attribute based structure with k-means clustering, we adaptively segment spatial regions to balance area based attributes and topological characteristics. The full geographical network is segmented into attribute based macro-regions for all spatially accessible and spatially disjoint regions. Attribute based spatial segmentation offers insights into why certain areas may be spatially disjoint and if it is identified as potential spatially accessible areas to determine which POIs can be placed to maximise accessibility. This approach transforms city planning and resource allocation by aligning POI placement with regional needs and characteristics.

有效的空间规划和资源优化需要精确划分潜在的空间可达区域和最佳的兴趣点（poi）的位置。我们的方法引入了一种新的基于属性的空间分割方法，该方法利用迭代聚类方法来创建独特的宏观区域，每个区域都与关键结构和属性特定属性相关联。通过将基于概率属性的结构与k-means聚类相结合，自适应分割空间区域，以平衡基于面积的属性和拓扑特征。将整个地理网络划分为基于属性的宏观区域，将所有空间可达和空间不相交的区域划分为宏观区域。基于属性的空间分割提供了洞察为什么某些区域可能在空间上不相交，如果它被确定为潜在的空间可达区域，以确定哪些poi可以放置以最大化可达性。这种方法通过调整POI的位置与区域需求和特征来改变城市规划和资源配置。

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