Spatial Statistics最新文献_第5页

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

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

{"title":"Regime changes and spatial dependence in the 2020 US presidential election polls","authors":"Giampiero M. Gallo , Demetrio Lacava , Edoardo Otranto","doi":"10.1016/j.spasta.2025.100927","DOIUrl":"10.1016/j.spasta.2025.100927","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100927"},"PeriodicalIF":2.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909070","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

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

Spatial Statistics Pub Date : 2025-10-01 Epub Date: 2025-08-22 DOI: 10.1016/j.spasta.2025.100926

Wentao Wang , Dengkui Li

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

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

{"title":"Spatiotemporal dynamics of COVID-19 in Wuhan based on community notifications","authors":"Gang Xu , Qirui Zhang , Xinlei Xu , Yajie Zhang , Yansheng Li","doi":"10.1016/j.spasta.2025.100925","DOIUrl":"10.1016/j.spasta.2025.100925","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100925"},"PeriodicalIF":2.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771829","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

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

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

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

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

{"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":"2025-10-01","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

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

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

{"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":"2025-10-01","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

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

Paul B. May , Andrew O. Finley

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

D. Posa

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 : 2025-08-01 Epub 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":"2025-08-01","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

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

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

引用次数: 0