Spatial Statistics最新文献

Uncovering hidden alignments in two-dimensional point fields 揭示二维点域中的隐藏排列

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-10-31 DOI: 10.1016/j.spasta.2024.100868

Eulogio Pardo-Igúzquiza , Peter A. Dowd

引用次数: 0

Spatio-temporal data fusion for the analysis of in situ and remote sensing data using the INLA-SPDE approach 利用 INLA-SPDE 方法进行时空数据融合，以分析原地数据和遥感数据

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-10-30 DOI: 10.1016/j.spasta.2024.100863

Shiyu He, Samuel W.K. Wong

{"title":"Spatio-temporal data fusion for the analysis of in situ and remote sensing data using the INLA-SPDE approach","authors":"Shiyu He, Samuel W.K. Wong","doi":"10.1016/j.spasta.2024.100863","DOIUrl":"10.1016/j.spasta.2024.100863","url":null,"abstract":"<div><div>We propose a Bayesian hierarchical model to address the challenge of spatial misalignment in spatio-temporal data obtained from in situ and satellite sources. The model is fit using the INLA-SPDE approach, which provides efficient computation. Our methodology combines the different data sources in a “fusion” model via the construction of projection matrices in both spatial and temporal domains. Through simulation studies, we demonstrate that the fusion model has superior performance in prediction accuracy across space and time compared to standalone “in situ” and “satellite” models based on only in situ or satellite data, respectively. The fusion model also generally outperforms the standalone models in terms of parameter inference. Such a modeling approach is motivated by environmental problems, and our specific focus is on the analysis and prediction of harmful algae bloom (HAB) events, where the convention is to conduct separate analyses based on either in situ samples or satellite images. A real data analysis shows that the proposed model is a necessary step towards a unified characterization of bloom dynamics and identifying the key drivers of HAB events.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting nearest-neighbour maps for estimating the variance of sample mean in equal-probability systematic sampling of spatial populations 利用近邻图估计空间种群等概率系统抽样中样本平均值的方差

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-10-24 DOI: 10.1016/j.spasta.2024.100865

Sara Franceschi , Lorenzo Fattorini , Timothy G Gregoire

{"title":"Exploiting nearest-neighbour maps for estimating the variance of sample mean in equal-probability systematic sampling of spatial populations","authors":"Sara Franceschi , Lorenzo Fattorini , Timothy G Gregoire","doi":"10.1016/j.spasta.2024.100865","DOIUrl":"10.1016/j.spasta.2024.100865","url":null,"abstract":"<div><div>Because of its ease of implementation, equal probability systematic sampling is of wide use in spatial surveys with sample mean that constitutes an unbiased estimator of population mean. A serious drawback, however, is that no unbiased estimator of the variance of the sample mean is available. As the search for an omnibus variance estimator able to provide reliable results under any spatial population has been lacking, we propose a design-consistent estimator that invariably converges to the true variance as the population and sample size increase. The proposal is based on the nearest-neighbour maps that are taken as pseudo-populations from which all the possible systematic samples can be enumerated. As nearest-neighbour maps are design-consistent under equal-probability systematic sampling and mild conditions, the variance of the sample mean achieved from all the possible systematic samples selected from the map is also a consistent estimator of the true variance. Through a simulation study based on artificial and real populations we show that our proposal generally outperforms the familiar estimators proposed in literature.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variable selection of nonparametric spatial autoregressive models via deep learning 通过深度学习选择非参数空间自回归模型的变量

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-10-16 DOI: 10.1016/j.spasta.2024.100862

Xiaodi Zhang, Yunquan Song

引用次数: 0

Estimation and inference of multi-effect generalized geographically and temporally weighted regression models 多效应广义地理和时间加权回归模型的估计和推论

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-10-02 DOI: 10.1016/j.spasta.2024.100861

Zhi Zhang , Ruochen Mei , Changlin Mei

{"title":"Estimation and inference of multi-effect generalized geographically and temporally weighted regression models","authors":"Zhi Zhang , Ruochen Mei , Changlin Mei","doi":"10.1016/j.spasta.2024.100861","DOIUrl":"10.1016/j.spasta.2024.100861","url":null,"abstract":"<div><div>Geographically and temporally weighted regression (GTWR) models have been an effective tool for exploring spatiotemporal heterogeneity of regression relationships. However, they cannot effectively model such response variables that follows discrete distributions. In this study, we first extend the distributions of response variables to one-parameter exponential family of distributions and formulate generalized geographically and temporally weighted regression (GGTWR) models with their unilaterally temporally weighted maximum likelihood estimation method. Furthermore, we propose so-called multi-effect GGTWR (MEGGTWR) models in which spatiotemporally varying, constant, temporally varying, and spatially varying coefficients may simultaneously be included to reflect different effects of explanatory variables. A coefficient-average-based estimation method is suggested to calibrate MEGGTWR models and a generalized likelihood ratio statistic based test is formulated to identify the types of coefficients. Simulation studies are then conducted to assess the performance of the proposed estimation and inference methods with the impact of multicollinearity among explanatory variables also examined. The results show that the estimation method for MEGGTWR models can accurately estimate various types of coefficients and the test method is of valid type <span><math><mi>I</mi></math></span> error and satisfactory power. Finally, the relationship between childhood hand, foot, and mouth disease cases and climate factors is analyzed by the proposed models with their estimation and inference methods and some interesting spatiotemporal patterns are uncovered.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425602","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

A spatio-temporal model for temporal evolution of spatial extremal dependence 空间极值依赖性时间演化的时空模型

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-09-30 DOI: 10.1016/j.spasta.2024.100860

Véronique Maume-Deschamps , Pierre Ribereau , Manal Zeidan

引用次数: 0

Nonparametric isotropy test for spatial point processes using random rotations 利用随机旋转对空间点过程进行非参数各向同性检验

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-09-12 DOI: 10.1016/j.spasta.2024.100858

Chiara Fend, Claudia Redenbach

引用次数: 0

Spatio-temporal clustering using generalized lasso to identify the spread of Covid-19 in Indonesia according to provincial flight route-based connections 利用广义套索进行时空聚类，根据各省航班航线的连接情况确定 Covid-19 在印度尼西亚的传播情况

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-09-04 DOI: 10.1016/j.spasta.2024.100857

Septian Rahardiantoro, Sachnaz Desta Oktarina, Anang Kurnia, Nickyta Shavira Maharani, Alfidhia Rahman Nasa Juhanda

引用次数: 0

Spatial statistics: Climate and the environment 空间统计：气候与环境

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-08-17 DOI: 10.1016/j.spasta.2024.100856

Christopher K. Wikle , Mevin B. Hooten , William Kleiber , Douglas W. Nychka

引用次数: 0

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-08-14 DOI: 10.1016/j.spasta.2024.100855

Daniel A. Griffith

{"title":"Self-correlated spatial random variables: From an auto- to a sui- model respecification","authors":"Daniel A. Griffith","doi":"10.1016/j.spasta.2024.100855","DOIUrl":"10.1016/j.spasta.2024.100855","url":null,"abstract":"<div><p>This paper marks the 50-year publication anniversary of Besag's seminal spatial auto- models paper. His classic article synthesizes generic autoregressive specifications (i.e., a response variable appears on both sides of a regression equation and/or probability function equal sign) for the following six popular random variables: normal, logistic (i.e., Bernoulli), binomial, Poisson, exponential, and gamma. Besag dismisses these last two while recognizing failures of both as well as the more scientifically critical counts-oriented auto-Poisson. His initially unsuccessful subsequent work first attempted to repair them (e.g., pseudo-likelihood estimation), and then successfully revise them within the context of mixed models, formulating a spatially structured random effects term that effectively and efficiently absorbs and accounts for spatial autocorrelation in geospatial data. One remaining weakness of all but the auto-normal is a need to resort to Markov chain Monte Carlo (MCMC) techniques for legitimate estimation purposes. Recently, Griffith succeeded in devising an innovative uniform distribution genre—sui-uniform random variables—that accommodates spatial autocorrelation, too. Its most appealing feature is that, by applying two powerful mathematical statistical theorems (i.e., the probability integral transform, and the quantile function), it redeems Besag's auto- model failures. This paper details conversion of Besag's initial six modified variates, exemplifying them with both simulation experiments and publicly accessible real-world georeferenced data. The principal outcome is valuable spatial statistical advancements, with special reference to Moran eigenvector spatial filtering.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083754","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