Spatial Statistics最新文献

{"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":"2025-06-13","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":"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":"2025-06-07","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":"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":"2025-06-03","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":"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":"2025-06-01","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":"A J-test for spatial autoregressive binary models","authors":"Gianfranco Piras ,&nbsp;Mauricio Sarrias","doi":"10.1016/j.spasta.2025.100903","DOIUrl":"10.1016/j.spasta.2025.100903","url":null,"abstract":"<div><div>Spatial autoregressive binary models are well established in spatial statistics and econometric literature. Recently, different estimation methods have been proposed that account for logistic as well as probit regressions. In spatial models the choice of the spatial weighting matrix is crucial to reflect the amount of correlation in the data. This article proposes a simple <span><math><mi>J</mi></math></span>-test procedure for spatial autoregressive binary model. Since the <span><math><mi>J</mi></math></span>-test is a non-nested test, it can be used, among other things, to test the specification of the spatial weighting matrix. The <span><math><mi>J</mi></math></span>-test is based on augmenting the null model with the predictor from the alternative model(s). After defining these predictors, we develop the theory and derive the steps for the <span><math><mi>J</mi></math></span>-test. We also evaluate the finite sample properties in the context of a Monte Carlo experiment. An empirical application on firms’ decisions to reopen in the aftermath of Hurricane Katrina for New Orleans is also presented.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100903"},"PeriodicalIF":2.1,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894998","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 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":"2025-04-25","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":"Stochastic spatial stream networks for scalable inferences of riverscape processes","authors":"Xinyi Lu ,&nbsp;Andee Kaplan ,&nbsp;Yoichiro Kanno ,&nbsp;George Valentine ,&nbsp;Jacob M. Rash ,&nbsp;Mevin Hooten","doi":"10.1016/j.spasta.2025.100902","DOIUrl":"10.1016/j.spasta.2025.100902","url":null,"abstract":"<div><div>Spatial stream networks (SSN) models characterize correlated ecological processes in dendritic ecosystems. Conventional SSN models rely on pre-processed stream networks and point-to-point hydrologic distances. However, this data processing may be labor-intensive and time-consuming over large spatial domains. Therefore, we propose to infer the functional connectivity of stream networks stochastically. Our physically-guided model utilizes the knowledge that water flows from high elevation to low elevation, and flow rate typically increases when two tributaries merge. We also leverage the hierarchical branching architecture of dendritic networks to alleviate computing and reduce uncertainty. Spatial autoregressive models composed of inferred SSNs propagate stochasticity between network connectivity and dynamic ecological processes in a Bayesian framework. We show in simulated examples that our mechanistic model facilitated learning about the functional network and enhanced predictive performance. We also demonstrate our approach in a large-scale case study using native brook trout (<em>Salvelinus fontinalis</em>) count data. A population model based on our stochastic SSN outperformed that with a conventional SSN in predicting abundance and expedited the analysis by circumventing data processing.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100902"},"PeriodicalIF":2.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898418","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":"Random elastic space–time (REST) prediction","authors":"Nicolas Coloma,&nbsp;William Kleiber","doi":"10.1016/j.spasta.2025.100904","DOIUrl":"10.1016/j.spasta.2025.100904","url":null,"abstract":"<div><div>Statistical modeling and interpolation of space–time processes has gained increasing relevance over the last few years. However, real world data often exhibit characteristics that challenge conventional methods such as nonstationarity and temporal misalignment. For example, high frequency solar irradiance data are typically observed at fine temporal scales, but at sparse spatial sampling, so space–time interpolation is necessary to support solar energy studies. The nonstationarity and phase misalignment of such data challenges extant approaches. We propose random elastic space–time (REST) prediction, a novel method that addresses temporally-varying phase misalignment by combining elastic alignment and conventional kriging techniques. Moreover, uncertainty in both amplitude and phase alignment can be readily quantified in a conditional simulation framework, whereas conventional space–time methods only address amplitude uncertainty. We illustrate our approach on a challenging solar irradiance dataset, where our method demonstrates superior predictive distributions compared to existing geostatistical and functional data analytic techniques.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100904"},"PeriodicalIF":2.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886951","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":"Computationally efficient spatio-temporal disease mapping for big data","authors":"Duncan Lee","doi":"10.1016/j.spasta.2025.100901","DOIUrl":"10.1016/j.spasta.2025.100901","url":null,"abstract":"<div><div>Disease mapping models estimate the spatio-temporal variation in population-level disease risks or rates across a set of <span><math><mi>K</mi></math></span> areal units for <span><math><mi>N</mi></math></span> time periods, aiming to identify temporal trends and spatial hotspots. Highly parameterised Bayesian hierarchical models with over <span><math><mrow><mi>K</mi><mi>N</mi></mrow></math></span> random effects are commonly used to estimate this spatio-temporal variation, which are assigned autoregressive and conditional autoregressive prior distributions. These models work well when there are tens of thousands of data points, but are likely to be computationally burdensome when this rises to hundreds of thousands or above. This paper proposes a computationally efficient alternative, which can fit a range of spatio-temporal disease trends almost as well as existing highly parameterised models but only takes around 5% to 40% of the time to implement. It achieves this by modelling the average spatial and temporal trends in the data with autoregressive type random effects, which are augmented by an observation-driven process using functions of earlier data as additional covariates in the model. The efficacy of this methodology is tested by simulation, before being applied to the motivating study that estimates the spatio-temporal trends in asthma, cancer, coronary heart and chronic obstructive pulmonary disease prevalences for <span><math><mrow><mi>K</mi><mo>=</mo><mn>32</mn><mo>,</mo><mn>751</mn></mrow></math></span> small areas over <span><math><mrow><mi>N</mi><mo>=</mo><mn>13</mn></mrow></math></span> years in England.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100901"},"PeriodicalIF":2.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873638","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":"2025-04-15","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}