Spatial Statistics最新文献

{"title":"Spatiotemporal mapping and analysis of atypical COVID-19 outbreaks in Shijiazhuang City (China) using the synthetic SEIR-BME approach","authors":"Zekun Gao ,&nbsp;Yutong Jiang ,&nbsp;Junjie Yin ,&nbsp;Jiaping Wu ,&nbsp;Maria-Stephania Christakos ,&nbsp;George Christakos ,&nbsp;Junyu He","doi":"10.1016/j.spasta.2025.100910","DOIUrl":"10.1016/j.spasta.2025.100910","url":null,"abstract":"<div><div>Shijiazhuang City (Hebei Province, China) experienced two COVID-19 outbreaks: January 2021 and November 2022. Differences in the prevention and control measures implemented during the two outbreaks led to significantly distinct epidemic evolutions. During the first outbreak, these measures were implemented throughout the epidemic duration. During the second outbreak, attention was paid only at the initial epidemic stage, followed by a laissez-faire management that led to a rapid epidemic development, and only then control measures were re-implemented. In the present work, epidemic-related data during the two outbreaks and relevant risk area data during the atypical November 2022 outbreak were collected from Nation-, Hebei Province-, and Shijiazhuang City-level Health Commission sources. The study of the outbreaks involved a preliminary long time-series analysis followed by a novel synthesis of Susceptible-Exposed-Infected-Removed (SEIR) modeling with Bayesian Maximum Entropy (BME) mapping of the spatiotemporal COVID-19 spread during the November 2022 outbreak (a severe data deficiency occurred this month due to normalized management). An important advantage of the proposed SEIR-BME synthesis is that it compensated for the individual shortcomings of its components: Using SEIR we constructed transmission models of the outbreaks, while BME effectively filled in the missing data during November 2022 and subsequently generated accurate spatiotemporal disease risk maps. Our results confirmed the powerful transmission capability of COVID-19 and the considerable prevention and control progress made by the authorities from January 2021 to November 2022. We also found that during the exponential growth period of the epidemic, the COVID-19 variation results of this work closely followed the empirical COVID-19 law of <span><span>He et al. (2020)</span></span>. Lastly, our analysis provided data support for subsequent studies of the COVID-19 spread, and suggested optimal infectious disease prevention and control measures. It is hoped that the present work would laid the methodological foundations for future developments in spatiotemporal infectious disease modeling and mapping.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100910"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280122","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}

{"title":"Probabilistic spatiotemporal modeling of day-ahead wind power generation with input-warped Gaussian processes","authors":"Qiqi Li ,&nbsp;Michael Ludkovski","doi":"10.1016/j.spasta.2025.100906","DOIUrl":"10.1016/j.spasta.2025.100906","url":null,"abstract":"<div><div>We design a Gaussian process (GP) spatiotemporal model to capture features of day-ahead wind power forecasts. We work with hourly-scale day-ahead forecasts across hundreds of wind farm locations, with the main aim of constructing a fully probabilistic joint model across space and hours of the day. To this end, we design a separable space–time kernel, implementing both temporal and spatial input warping to capture the nonstationarity in the covariance of wind power. We conduct synthetic experiments to validate our choice of the spatial kernel and to demonstrate the effectiveness of warping in addressing nonstationarity. The second half of the paper is devoted to a detailed case study using a realistic, fully calibrated dataset representing wind farms in the ERCOT region of Texas.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100906"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222961","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}

{"title":"Bias correction methods for spatially lagged covariates measured with errors","authors":"Mohammad Masjkur ,&nbsp;Asep Saefuddin ,&nbsp;I. Wayan Mangku ,&nbsp;Henk Folmer ,&nbsp;Arno J. Van der Vlist ,&nbsp;Marco Grzegorczyk","doi":"10.1016/j.spasta.2025.100909","DOIUrl":"10.1016/j.spasta.2025.100909","url":null,"abstract":"<div><div>This paper compares three widely applied bias correction methods for spatially lagged covariates measured with error, namely, Monte Carlo expectation-maximization (MCEM), instrumental variables (IV), and Bayesian analysis (BA). We cross-compare these correction methods on simulated data for the special case of one single lagged covariate. We use the root mean squared error (RMSE) as evaluation criterion. The findings indicate that BA is the best bias correction method.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100909"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631898","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}

{"title":"Transfer learning for high dimensional spatial autoregressive model","authors":"Yunquan Song,&nbsp;Xuan Chen,&nbsp;Rui Yang,&nbsp;Yijun Li","doi":"10.1016/j.spasta.2025.100908","DOIUrl":"10.1016/j.spasta.2025.100908","url":null,"abstract":"<div><div>Transfer learning is a learning process that applies models learned in old domains to new domains by utilizing similarities between data, tasks, or models. At present, transfer learning has been widely applied, such as natural language processing, recommendation systems, drug analysis, etc. Research in statistical models mostly focuses on classic linear models such as classification and regression. It is still unclear how transfer learning affects spatial data. Spatial data is an important type of data and has been a hot research topic in statistics and econometrics in recent years. However, in reality, its collection and labeling are expensive and labor-intensive, and there may not be enough data to train a robust model. Therefore, this article considers using auxiliary sample sets that are different from the target dataset but have some similarity to help us estimate and predict the target model, and specifies criteria for determining similarity. We propose transfer learning algorithms based on spatial autoregressive models, which can transfer knowledge from auxiliary datasets to target models of interest to us. Its performance has been demonstrated in numerical simulations and real housing price datasets.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100908"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289162","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}

{"title":"A low-rank Bayesian approach for geoadditive modeling","authors":"Bryan Sumalinab ,&nbsp;Oswaldo Gressani ,&nbsp;Niel Hens ,&nbsp;Christel Faes","doi":"10.1016/j.spasta.2025.100907","DOIUrl":"10.1016/j.spasta.2025.100907","url":null,"abstract":"<div><div>Kriging is an established methodology for predicting spatial data in geostatistics. Current kriging techniques can handle linear dependencies on spatially referenced covariates. Although splines have shown promise in capturing nonlinear dependencies of covariates, their combination with kriging, especially in handling count data, remains underexplored. This paper proposes a new Bayesian approach to the low-rank representation of geoadditive models, which integrates splines and kriging to account for both spatial correlations and nonlinear dependencies of covariates. The proposed method accommodates Gaussian and count data inherent in many geospatial datasets. Additionally, Laplace approximations to selected posterior distributions enhances computational efficiency, resulting in faster computation times compared to Markov chain Monte Carlo techniques commonly used for Bayesian inference. Method performance is assessed through a simulation study, demonstrating the effectiveness of the proposed approach. The methodology is applied to the analysis of heavy metal concentrations in the Meuse river and vulnerability to the coronavirus disease 2019 (COVID-19) in Belgium.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100907"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229563","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}

{"title":"A marked sequential point process for disease surveillance: Modeling and optimization","authors":"François d’Alayer,&nbsp;Edith Gabriel,&nbsp;Samuel Soubeyrand","doi":"10.1016/j.spasta.2025.100913","DOIUrl":"10.1016/j.spasta.2025.100913","url":null,"abstract":"<div><div>Plant disease surveillance is essential for the management of disease outbreaks that pose significant threats to agricultural sustainability. In this study, we present a novel sequential point process model designed for disease surveillance. The model incorporates self-interaction mechanisms to account for the influence of the process’ history. To analyze the dynamics of the model, we propose new sequential summary statistics that extend traditional point process methods to scenarios where sequential interactions are critical. This model serves a dual purpose: it is employed both to propose novel and efficient sampling designs, and to characterize existing sampling schemes, implemented in real-world situations, through parameter inference.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100913"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562979","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}

{"title":"Characteristics of some isotropic covariance models with negative values","authors":"De Iaco S.,&nbsp;Posa D.","doi":"10.1016/j.spasta.2025.100905","DOIUrl":"10.1016/j.spasta.2025.100905","url":null,"abstract":"<div><div>In the literature, most of the classical covariance models characterised by negative values were derived by utilising the Bessel functions, on the other hand, recently, other classes of models with negative correlation were obtained through the difference between two covariance functions. However, although for the former, the analytic features, such as their absolute minimum values, were completely explored, for the latter these aspects have to be still investigated. In this paper, starting from the admissibility conditions and the general characteristics of three wide families of isotropic covariance models, based on the difference of Gaussian, exponential and rational models, their absolute minimum, as a function of the dimension of the Euclidean space in which they are defined, is provided. Consequently, the minimum values for the most common Euclidean dimensional spaces are given as special cases. These results fill the theoretical gap related to the analysed classes of correlation models with negative values and then can support their use. A simulation study and an application to a real data set are also presented to assess performance in terms of prediction accuracy.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100905"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479932","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}

{"title":"Magnitude-weighted goodness-of-fit scores for earthquake forecasting","authors":"Frederic Schoenberg","doi":"10.1016/j.spasta.2025.100895","DOIUrl":"10.1016/j.spasta.2025.100895","url":null,"abstract":"<div><div>Current methods for evaluating earthquake forecasts, such as the <span><math><mi>N</mi></math></span>-test, <span><math><mi>L</mi></math></span>-test, or log-likelihood score, typically do not disproportionately reward a model for more accurately forecasting the largest events, or disproportionately punish a model for less accurately forecasting the largest events. However, since the largest earthquakes are by far the most destructive and therefore of most interest to practitioners, in many circumstances, a weighted likelihood score may be more useful. Here, we propose various weighted measures, weighting each earthquake by some function of its magnitude, such as potency-weighted log-likelihood, and consider their properties. The proposed methods are applied to a catalog of earthquakes in the Western United States.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100895"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746591","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}

{"title":"A new regular grid-based spatial process on the log-symmetric model for speckled clutter","authors":"Arthur Machado,&nbsp;Francisco José A. Cysneiros,&nbsp;Abraão D.C. Nascimento","doi":"10.1016/j.spasta.2025.100900","DOIUrl":"10.1016/j.spasta.2025.100900","url":null,"abstract":"<div><div>Solving remote sensing (RS) problems is crucial for society when it comes to environmental and climate dynamics, to name just a few examples. An efficient RS source is the use of synthetic aperture radar (SAR) to describe natural and man-made phenomena through imagery. Our approach is to understand the data behind SAR images as outcomes of random variables, and then use statistics to solve RS problems. In this paper, we consider the input of a SAR image as a random variable in regular space and describe the nature of SAR intensity (a strictly positive and asymmetric feature that is affected by speckle noise and prevents direct interpretation) using a new proposal for a log-symmetric (LOGSYM) regression model in two dimensions, the 2-D LOGSYM autoregressive moving-average (2-D LOGSYMARMA) model. Besides a discussion on the physical relationship between the proposed model and SAR intensity (mentioning that it can extend a commonly used lognormal law), we derive some mathematical properties of 2-D LOGSYMARMA: matrix-based score function and Fisher information. We discuss in detail the conditional maximum likelihood (CML) estimation for the 2-D LOGSYMARMA parameters. We conduct a Monte Carlo study to quantify the performance of the resulting estimates and to verify that the asymptotic behavior expected from CML estimators is achieved. Finally, we perform an application to real SAR data, where our proposal is applied to different types of regions – ocean, forest, and urban areas – utilizing the versatility of the log-symmetric family. Results of both artificial and real experiments show that our model is an important tool for the extraction and classification of spatial information in SAR images.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100900"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882136","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}

{"title":"A spatial autoregressive graphical model","authors":"Sjoerd Hermes ,&nbsp;Joost van Heerwaarden ,&nbsp;Pariya Behrouzi","doi":"10.1016/j.spasta.2025.100893","DOIUrl":"10.1016/j.spasta.2025.100893","url":null,"abstract":"<div><div>Within the statistical literature, a significant gap exists in methods capable of modelling asymmetric multivariate spatial effects that elucidate the relationships underlying complex spatial phenomena. For such a phenomenon, observations at any location are expected to arise from a combination of within- and between-location effects, where the latter exhibit asymmetry. This asymmetry is represented by heterogeneous spatial effects between locations pertaining to two different categories, that is, a feature inherent to each location in the data, such that based on the feature label, asymmetric spatial relations are postulated between neighbouring locations with different labels. Our novel approach synergises the principles of multivariate spatial autoregressive models and the Gaussian graphical model. This synergy enables us to effectively address the gap by accommodating asymmetric spatial relations, overcoming the usual constraints in spatial analyses. However, the resulting flexibility comes at a cost: the spatial effects are not identifiable without either prior knowledge of the underlying phenomenon or additional parameter restrictions. Using a Bayesian-estimation framework, the model performance is assessed in a simulation study. We apply the model on intercropping data, where spatial effects between different crops are unlikely to be symmetric, in order to illustrate the usage of the proposed methodology. An R package containing the proposed methodology can be found on <span><span>https://CRAN.R-project.org/package=SAGM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100893"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839305","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}