Pub Date : 2025-06-21DOI: 10.1016/j.spasta.2025.100911
M. de Klerk, I. Fabris-Rotelli
Effective spatial planning and resource optimisation require precise demarcation of potential spatial accessible areas and optimal placement of points of interest (POIs). Our approach introduces a novel attribute based spatial segmentation methodology that utilises an iterative clustering approach to create unique macro-regions, each associated with key structural and attribute specific properties. By integrating a probabilistic attribute based structure with k-means clustering, we adaptively segment spatial regions to balance area based attributes and topological characteristics. The full geographical network is segmented into attribute based macro-regions for all spatially accessible and spatially disjoint regions. Attribute based spatial segmentation offers insights into why certain areas may be spatially disjoint and if it is identified as potential spatially accessible areas to determine which POIs can be placed to maximise accessibility. This approach transforms city planning and resource allocation by aligning POI placement with regional needs and characteristics.
{"title":"Attribute based spatial segmentation for optimising POI placement","authors":"M. de Klerk, I. Fabris-Rotelli","doi":"10.1016/j.spasta.2025.100911","DOIUrl":"10.1016/j.spasta.2025.100911","url":null,"abstract":"<div><div>Effective spatial planning and resource optimisation require precise demarcation of potential spatial accessible areas and optimal placement of points of interest (POIs). Our approach introduces a novel attribute based spatial segmentation methodology that utilises an iterative clustering approach to create unique macro-regions, each associated with key structural and attribute specific properties. By integrating a probabilistic attribute based structure with k-means clustering, we adaptively segment spatial regions to balance area based attributes and topological characteristics. The full geographical network is segmented into attribute based macro-regions for all spatially accessible and spatially disjoint regions. Attribute based spatial segmentation offers insights into why certain areas may be spatially disjoint and if it is identified as potential spatially accessible areas to determine which POIs can be placed to maximise accessibility. This approach transforms city planning and resource allocation by aligning POI placement with regional needs and characteristics.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100911"},"PeriodicalIF":2.1,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480059","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}
Pub Date : 2025-06-13DOI: 10.1016/j.spasta.2025.100908
Yunquan Song, Xuan Chen, Rui Yang, Yijun Li
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.
{"title":"Transfer learning for high dimensional spatial autoregressive model","authors":"Yunquan Song, Xuan Chen, Rui Yang, 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}
Pub Date : 2025-06-08DOI: 10.1016/j.spasta.2025.100905
De Iaco S., Posa D.
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.
{"title":"Characteristics of some isotropic covariance models with negative values","authors":"De Iaco S., 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":"2025-06-08","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}
Pub Date : 2025-06-07DOI: 10.1016/j.spasta.2025.100910
Zekun Gao , Yutong Jiang , Junjie Yin , Jiaping Wu , Maria-Stephania Christakos , George Christakos , Junyu He
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 He et al. (2020). 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.
中国河北省石家庄市经历了两次COVID-19疫情：2021年1月和2022年11月。在两次疫情期间实施的预防和控制措施的差异导致了明显不同的流行病演变。在第一次疫情期间，这些措施在整个疫情期间都得到了实施。在第二次暴发期间，只注意了最初的流行阶段，随后采取了放任管理，导致流行病迅速发展，直到那时才重新实施控制措施。在本工作中，从国家、河北省和石家庄市卫生委员会收集了两次疫情期间的流行病学相关数据和2022年11月非典型疫情期间的相关风险区域数据。对疫情的研究包括初步的长时间序列分析，然后对2022年11月疫情期间COVID-19时空传播的贝叶斯最大熵（BME）映射进行易感-暴露-感染-去除（SEIR）模型的新颖综合（由于规范化管理，本月发生了严重的数据不足）。提出的SEIR-BME综合的一个重要优势是它弥补了其组成部分的单个缺点：使用SEIR，我们构建了疫情的传播模型，而BME有效地填补了2022年11月期间缺失的数据，随后生成了准确的时空疾病风险图。我们的结果证实了2019冠状病毒病的强大传播能力，以及当局在2021年1月至2022年11月期间取得的相当大的防控进展。我们还发现，在疫情的指数增长期，本工作的COVID-19变异结果与He et al.（2020）的经验COVID-19规律密切相关。最后，我们的分析为后续的COVID-19传播研究提供了数据支持，并提出了最佳的传染病防控措施。希望本研究能为传染病时空建模和制图的未来发展奠定方法学基础。
{"title":"Spatiotemporal mapping and analysis of atypical COVID-19 outbreaks in Shijiazhuang City (China) using the synthetic SEIR-BME approach","authors":"Zekun Gao , Yutong Jiang , Junjie Yin , Jiaping Wu , Maria-Stephania Christakos , George Christakos , 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}
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.
{"title":"A low-rank Bayesian approach for geoadditive modeling","authors":"Bryan Sumalinab , Oswaldo Gressani , Niel Hens , 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}
Pub Date : 2025-06-01DOI: 10.1016/j.spasta.2025.100906
Qiqi Li , Michael Ludkovski
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.
{"title":"Probabilistic spatiotemporal modeling of day-ahead wind power generation with input-warped Gaussian processes","authors":"Qiqi Li , 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}
Pub Date : 2025-05-29DOI: 10.1016/j.spasta.2025.100909
Mohammad Masjkur , Asep Saefuddin , I. Wayan Mangku , Henk Folmer , Arno J. Van der Vlist , Marco Grzegorczyk
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.
{"title":"Bias correction methods for spatially lagged covariates measured with errors","authors":"Mohammad Masjkur , Asep Saefuddin , I. Wayan Mangku , Henk Folmer , Arno J. Van der Vlist , 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":"2025-05-29","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}
Pub Date : 2025-04-28DOI: 10.1016/j.spasta.2025.100903
Gianfranco Piras , Mauricio Sarrias
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 -test procedure for spatial autoregressive binary model. Since the -test is a non-nested test, it can be used, among other things, to test the specification of the spatial weighting matrix. The -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 -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.
{"title":"A J-test for spatial autoregressive binary models","authors":"Gianfranco Piras , 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}
Pub Date : 2025-04-25DOI: 10.1016/j.spasta.2025.100900
Arthur Machado, Francisco José A. Cysneiros, Abraão D.C. Nascimento
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.
{"title":"A new regular grid-based spatial process on the log-symmetric model for speckled clutter","authors":"Arthur Machado, Francisco José A. Cysneiros, 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}
Pub Date : 2025-04-25DOI: 10.1016/j.spasta.2025.100902
Xinyi Lu , Andee Kaplan , Yoichiro Kanno , George Valentine , Jacob M. Rash , Mevin Hooten
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 (Salvelinus fontinalis) 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.
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