Spatial Statistics最新文献

A J-test for spatial autoregressive binary models 空间自回归二元模型的j检验

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-28 DOI: 10.1016/j.spasta.2025.100903

Gianfranco Piras , Mauricio Sarrias

引用次数: 0

A new regular grid-based spatial process on the log-symmetric model for speckled clutter 基于对数对称模型的斑点杂波规则网格空间处理方法

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-25 DOI: 10.1016/j.spasta.2025.100900

Arthur Machado, Francisco José A. Cysneiros, Abraão D.C. Nascimento

{"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}

引用次数: 0

Stochastic spatial stream networks for scalable inferences of riverscape processes 用于河流景观过程可扩展推理的随机空间流网络

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-25 DOI: 10.1016/j.spasta.2025.100902

Xinyi Lu , Andee Kaplan , Yoichiro Kanno , George Valentine , Jacob M. Rash , Mevin Hooten

{"title":"Stochastic spatial stream networks for scalable inferences of riverscape processes","authors":"Xinyi Lu , Andee Kaplan , Yoichiro Kanno , George Valentine , Jacob M. Rash , 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}

引用次数: 0

Random elastic space–time (REST) prediction 随机弹性时空（REST）预测

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-24 DOI: 10.1016/j.spasta.2025.100904

Nicolas Coloma, William Kleiber

引用次数: 0

Computationally efficient spatio-temporal disease mapping for big data 面向大数据的高效时空疾病制图

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-23 DOI: 10.1016/j.spasta.2025.100901

Duncan Lee

{"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}

引用次数: 0

A spatial autoregressive graphical model 空间自回归图形模型

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-15 DOI: 10.1016/j.spasta.2025.100893

Sjoerd Hermes , Joost van Heerwaarden , Pariya Behrouzi

{"title":"A spatial autoregressive graphical model","authors":"Sjoerd Hermes , Joost van Heerwaarden , 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}

引用次数: 0

Isotropy testing in spatial point patterns: nonparametric versus parametric replication under misspecification 空间点模式的各向同性测试：错误规范下的非参数与参数复制

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-05 DOI: 10.1016/j.spasta.2025.100898

Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin

引用次数: 0

Nonparametric approaches for direct approximation of the spatial quantiles 直接逼近空间分位数的非参数方法

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-05 DOI: 10.1016/j.spasta.2025.100896

Pilar García-Soidán , Tomás R. Cotos-Yáñez

{"title":"Nonparametric approaches for direct approximation of the spatial quantiles","authors":"Pilar García-Soidán , Tomás R. Cotos-Yáñez","doi":"10.1016/j.spasta.2025.100896","DOIUrl":"10.1016/j.spasta.2025.100896","url":null,"abstract":"<div><div>The estimation of the spatial quantiles provides information on the thresholds of a spatial variable. This methodology is particularly appealing for its application to data of pollutants, so as to assess their level of risk. A spatial quantile can be approximated through different mechanisms, proposed in the statistics literature, although these approaches suffer from several drawbacks, regarding their lack of optimality or the fact of not leading to direct approximations. Thus, the current work introduces alternative procedures, which try to overcome the aforementioned issues by employing order statistics, similarly as done for independent data. With this aim, the available observations are appropriately transformed to yield a sample of the process at each target site, so that the data obtained are then ordered and used to derive the spatial quantile at the corresponding location. The new methodology can be directly applied to data from processes that are either stationary or that deviate from this condition for a non-constant trend and, additionally, it can be even extended to heteroscedastic data. Simulation studies under different scenarios have been accomplished, whose results show the adequate performance of the proposed estimators. A further step of this research is the application of the new approaches to data of nitrogen dioxide concentrations, to exemplify the potential of the quantile estimates to check the thresholds of a pollutant at a specific moment, as well as their evolution over time.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100896"},"PeriodicalIF":2.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783992","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

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-03-29 DOI: 10.1016/j.spasta.2025.100897

Shifeng Yu , Xiaoyu Hu , Yehua Sheng , Chenmeng Zhao

{"title":"Similarity and geographically weighted regression considering spatial scales of features space","authors":"Shifeng Yu , Xiaoyu Hu , Yehua Sheng , Chenmeng Zhao","doi":"10.1016/j.spasta.2025.100897","DOIUrl":"10.1016/j.spasta.2025.100897","url":null,"abstract":"<div><div>Unlike a geographically weighted regression (GWR), a similarity and geographically weighted regression (SGWR) calculates weights using attribute and geographic similarities, thereby effectively improving the accuracy of the model. Nevertheless, SGWR does not set an attribute similarity bandwidth. This leads to its inability to measure the scale of variation of spatial processes in feature space. In addition, owing to the solving method used, SGWR can get stuck in local optima. To address these issues, this study proposed an improved similarity and geographically weighted regression model (SGWR-GD) that adds bandwidth to the attribute similarity kernel function. This parameter gives SGWR-GD the ability to measure the scale of change of spatial processes in the attribute dimension and thus enhances the flexibility of modelling. When solving the model, SGWR-GD first calculated the gradient of the model's Modified Akaike Information Criterion (AICc) with respect to the two bandwidths and the impact ratio. Subsequently, the optimal global solution of the model was obtained based on a gradient descent algorithm with box constraints. SGWR-GD and SGWR were applied to five different datasets and the accuracies of their fitting results were compared. SGWR-GD significantly improved the accuracy of the model compared to SGWR. In addition, the SGWR-GD stably determined the global optimal solution for each parameter. Simultaneously, the distribution of local residuals was also more stable.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100897"},"PeriodicalIF":2.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760349","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

Magnitude-weighted goodness-of-fit scores for earthquake forecasting 地震预报的震级加权拟合优度分数

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-03-28 DOI: 10.1016/j.spasta.2025.100895

Frederic Schoenberg

引用次数: 0