Spatial Statistics最新文献_第2页

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

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

Jonathan Acosta , Ronny Vallejos , Pilar García-Soidán

{"title":"A penalized estimation of the variogram and effective sample size","authors":"Jonathan Acosta , Ronny Vallejos , Pilar García-Soidán","doi":"10.1016/j.spasta.2025.100921","DOIUrl":"10.1016/j.spasta.2025.100921","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100921"},"PeriodicalIF":2.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Spatial Statistics Pub Date : 2025-07-24 DOI: 10.1016/j.spasta.2025.100922

Lingling Tian , Chuanhua Wei , Wenxing Ding , Mixia Wu

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

Spatial Statistics Pub Date : 2025-07-22 DOI: 10.1016/j.spasta.2025.100917

Paul B. May , Andrew O. Finley

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

Spatial Statistics Pub Date : 2025-07-21 DOI: 10.1016/j.spasta.2025.100919

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

{"title":"Sampling design for binary geostatistical data, application to inspection actions of fishing activity in Portugal","authors":"Belchior Miguel , Paula Simões , Rui Gonçalves de Deus , Isabel Natário","doi":"10.1016/j.spasta.2025.100919","DOIUrl":"10.1016/j.spasta.2025.100919","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100919"},"PeriodicalIF":2.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-07-15 DOI: 10.1016/j.spasta.2025.100920

Xuankai Ma , Zehua Zhang , Yongze Song

{"title":"Geographically informed graph neural networks","authors":"Xuankai Ma , Zehua Zhang , Yongze Song","doi":"10.1016/j.spasta.2025.100920","DOIUrl":"10.1016/j.spasta.2025.100920","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100920"},"PeriodicalIF":2.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Spatial Statistics Pub Date : 2025-07-15 DOI: 10.1016/j.spasta.2025.100912

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

{"title":"Spatio-temporal intensity estimation for inhomogeneous Poisson point processes on linear networks: A roughness penalty method","authors":"Simone Panzeri , Aldo Clemente , Eleonora Arnone , Jorge Mateu , Laura M. Sangalli","doi":"10.1016/j.spasta.2025.100912","DOIUrl":"10.1016/j.spasta.2025.100912","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100912"},"PeriodicalIF":2.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Spatial Statistics Pub Date : 2025-07-14 DOI: 10.1016/j.spasta.2025.100916

Lorenzo Dell’Oro , Carlo Gaetan

{"title":"Flexible space–time models for extreme data","authors":"Lorenzo Dell’Oro , Carlo Gaetan","doi":"10.1016/j.spasta.2025.100916","DOIUrl":"10.1016/j.spasta.2025.100916","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100916"},"PeriodicalIF":2.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Spatial Statistics Pub Date : 2025-07-08 DOI: 10.1016/j.spasta.2025.100915

D. Posa

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

Spatial Statistics Pub 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

{"title":"A two-step sampling strategy to improve the prediction accuracy of contamination hotspots and identify hotspot boundaries","authors":"Joonmyoung Kim , Seonwoo Lee , Taekseon Ryu , Jonghyun Na , Taehyun Yun , Jeongho Lee , Hansuk Kim , Man Jae Kwon , Ho Young Jo , Yongsung Joo","doi":"10.1016/j.spasta.2025.100918","DOIUrl":"10.1016/j.spasta.2025.100918","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100918"},"PeriodicalIF":2.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Spatial Statistics Pub Date : 2025-07-02 DOI: 10.1016/j.spasta.2025.100914

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

引用次数: 0