Spatial Statistics最新文献_第4页

Fast mixture spatial regression: A mixture in the geographical and feature space applied to predict porosity in the post-salt 快速混合空间回归：将地理空间和特征空间的混合应用于预测盐湖开采后的孔隙度

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-22 DOI: 10.1016/j.spasta.2024.100873

Lucas Michelin , Lucas C. Godoy , Heitor S. Ramos , Marcos O. Prates

{"title":"Fast mixture spatial regression: A mixture in the geographical and feature space applied to predict porosity in the post-salt","authors":"Lucas Michelin , Lucas C. Godoy , Heitor S. Ramos , Marcos O. Prates","doi":"10.1016/j.spasta.2024.100873","DOIUrl":"10.1016/j.spasta.2024.100873","url":null,"abstract":"<div><div>Extracting geological resources like hydrocarbon fluids requires significant investments and precise decision-making processes. To optimize the efficiency of the extraction process, researchers and industry experts have explored innovative methodologies, including the prediction of optimal drilling locations. Porosity, a key attribute of reservoir rocks, plays a crucial role in determining fluid storage capacity. Geostatistical techniques, such as kriging, have been widely used for estimating porosity by capturing spatial dependence in sampled point-referenced data. However, the reliance on geographical coordinates for determining spatial distances may present challenges in scenarios with small and widely separated samples. In this paper, we develop a mixture model that combines the covariance generated by geographical space and the covariance generated in an appropriate feature space to enhance estimation accuracy. Developed within the Bayesian framework, our approach utilizes flexible Markov Chain Monte Carlo (MCMC) methods and leverages the Nearest-Neighbor Gaussian Process (NNGP) strategy for scalability. We present a controlled empirical comparison, considering various data generation configurations, to assess the performance of the mixture model in comparison to the marginal models. Applying our models to a three-dimensional reservoir demonstrates its practical applicability and scalability. This research presents a novel approach for improved porosity estimation by integrating spatial and covariate information, offering the potential for optimizing reservoir exploration and extraction activities.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100873"},"PeriodicalIF":2.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719821","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

Pixel isotropy test based on directional perimeters 基于方向周长的像素各向同性测试

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-20 DOI: 10.1016/j.spasta.2024.100869

Mariem Abaach , Hermine Biermé , Elena Di Bernardino , Anne Estrade

引用次数: 0

Simulation of conditional non-Gaussian random fields with directional asymmetry 模拟具有方向不对称性的条件非高斯随机场

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-17 DOI: 10.1016/j.spasta.2024.100872

Sebastian Hörning , András Bárdossy

引用次数: 0

Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices 结合半参数 SAR 模型和 GAMLSS 模型为对冲价格建立均值和非均质方差的联合空间模型

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-16 DOI: 10.1016/j.spasta.2024.100864

J.D. Toloza-Delgado , O.O. Melo , N.A. Cruz

{"title":"Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices","authors":"J.D. Toloza-Delgado , O.O. Melo , N.A. Cruz","doi":"10.1016/j.spasta.2024.100864","DOIUrl":"10.1016/j.spasta.2024.100864","url":null,"abstract":"<div><div>In the context of spatial econometrics, it is very useful to have methodologies that allow modeling the spatial dependence of the observed variables and obtaining more precise predictions of both the mean and the variability of the response variable, something very useful in territorial planning and public policies. This paper proposes a new methodology that jointly models the mean and the variance. Also, it allows to model the spatial dependence of the dependent variable as a function of covariates and to model the semiparametric effects in both models. The algorithms developed are based on generalized additive models that allow the inclusion of non-parametric terms in both the mean and the variance, maintaining the traditional theoretical framework of spatial regression. The theoretical developments of the estimation of this model are carried out, obtaining desirable statistical properties in the estimators. A simulation study is developed to verify that the proposed method has a remarkable predictive capacity in terms of the mean square error and shows a notable improvement in the estimation of the spatial autoregressive parameter, compared to other traditional methods and some recent developments. The model is also tested on data from the construction of a hedonic price model for the city of Bogotá, highlighting as the main result the ability to model the variability of housing prices, and the wealth in the analysis obtained.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100864"},"PeriodicalIF":2.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707294","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

Epidemiological insights and geographic clusters for COVID-19 in Taiwan using a mixture scan statistic 利用混合扫描统计法洞察台湾 COVID-19 的流行病学和地理集群

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-13 DOI: 10.1016/j.spasta.2024.100871

Yi-Hung Kung

{"title":"Epidemiological insights and geographic clusters for COVID-19 in Taiwan using a mixture scan statistic","authors":"Yi-Hung Kung","doi":"10.1016/j.spasta.2024.100871","DOIUrl":"10.1016/j.spasta.2024.100871","url":null,"abstract":"<div><div>The COVID-19 pandemic has posed unprecedented public health challenges worldwide, necessitating a comprehensive understanding of its transmission dynamics. This study examines the correlation between COVID-19 transmission and various risk factors, focusing on the impact of population structure and socio-economic conditions in Taiwan. By analyzing official government databases, we explore how factors such as population density, dependency ratios, and socio-economic environment influence the spread of COVID-19. Our findings highlight that densely populated areas, along with regions characterized by higher child dependency ratios and a significant number of low- and middle-income households, exhibit higher transmission rates. This research underscores the importance of considering socio-economic disparities and healthcare access in developing effective public health strategies. Furthermore, we utilize a mixture scan statistic to identify disease hotspots, taking into account spatial correlation and covariate effects. This approach can detect clusters based on known risk factors and help to assess possible unknown geographic risks, facilitating targeted interventions and resource allocation. Our study contributes to the broader understanding of COVID-19 transmission dynamics, offering insights into the importance of integrating socio-economic factors and spatial analysis in pandemic response efforts.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100871"},"PeriodicalIF":2.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707293","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 flexible class of priors for orthonormal matrices with basis function-specific structure 一类灵活的正交矩阵先验，具有基函数特定结构

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-12 DOI: 10.1016/j.spasta.2024.100866

Joshua S. North , Mark D. Risser , F. Jay Breidt

{"title":"A flexible class of priors for orthonormal matrices with basis function-specific structure","authors":"Joshua S. North , Mark D. Risser , F. Jay Breidt","doi":"10.1016/j.spasta.2024.100866","DOIUrl":"10.1016/j.spasta.2024.100866","url":null,"abstract":"<div><div>Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure <em>a priori</em>. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"64 ","pages":"Article 100866"},"PeriodicalIF":2.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660822","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

Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model 利用非参数、非稳态空间模型对集合卡尔曼滤波器进行规范化处理

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-09 DOI: 10.1016/j.spasta.2024.100870

Michael Tsyrulnikov , Arseniy Sotskiy

{"title":"Regularization of the Ensemble Kalman Filter using a non-parametric, non-stationary spatial model","authors":"Michael Tsyrulnikov , Arseniy Sotskiy","doi":"10.1016/j.spasta.2024.100870","DOIUrl":"10.1016/j.spasta.2024.100870","url":null,"abstract":"<div><div>The sample covariance matrix of a random vector is a good estimate of the true covariance matrix if the sample size is much larger than the length of the vector. In high-dimensional problems, this condition is never met. As a result, in high dimensions the Ensemble Kalman Filter’s (EnKF) ensemble does not contain enough information to specify the prior covariance matrix accurately. This necessitates the need for regularization of the analysis (observation update) problem. We propose a regularization technique based on a new spatial model. The model is a constrained version of the general Gaussian process convolution model. The constraints include local stationarity and smoothness of local spectra. We regularize EnKF by postulating that its prior covariances obey the spatial model. Placing a hyperprior distribution on the model parameters and using the likelihood of the prior ensemble data allows for an optimized use of both the ensemble and the hyperprior. A linear version of the respective estimator is shown to be consistent. A more accurate nonlinear neural-Bayes implementation of the estimator is developed. In simulation experiments, the new technique led to substantially better EnKF performance than several existing techniques.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"64 ","pages":"Article 100870"},"PeriodicalIF":2.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660823","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

Non-stationary spatio-temporal modeling using the stochastic advection–diffusion equation 利用随机平流扩散方程进行非稳态时空建模

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-06 DOI: 10.1016/j.spasta.2024.100867

Martin Outzen Berild, Geir-Arne Fuglstad

引用次数: 0

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":"64 ","pages":"Article 100863"},"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