Spatial Statistics最新文献_第7页

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 : 2025-06-01 Epub 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":"2025-06-01","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

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 : 2025-06-01 Epub Date: 2025-04-24 DOI: 10.1016/j.spasta.2025.100904

Nicolas Coloma, William Kleiber

引用次数: 0

Fast computation of the statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences 基于脉冲触发二值伪随机序列的时空感受野估计统计显著性检验的快速计算

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-06-01 Epub Date: 2025-03-27 DOI: 10.1016/j.spasta.2025.100899

Murat Okatan

{"title":"Fast computation of the statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences","authors":"Murat Okatan","doi":"10.1016/j.spasta.2025.100899","DOIUrl":"10.1016/j.spasta.2025.100899","url":null,"abstract":"<div><h3>Background</h3><div>Spatio-temporal receptive fields (STRFs) of visual neurons are often estimated using spike-triggered averaging (STA) with binary pseudo-random stimulus sequences. An exact analytical test—called the STA-BPRS test—has been developed to determine the statistical significance of each pixel in the STRF estimate. However, computing this test can take minutes to days, or even longer, for certain neurons.</div></div><div><h3>New method</h3><div>Here, the STA-BPRS test is accelerated by approximating the null distribution of STRF pixel estimates with a Normal distribution. This methodological refinement significantly reduces computation time, making large-scale data analysis feasible.</div></div><div><h3>Results</h3><div>The approximate test is systematically validated on real mouse retinal ganglion cell data and synthetic spike train data, demonstrating that it yields identical significance thresholds to the exact test. For neurons where, exact computation would be prohibitively long (e.g., hundreds of years), the approximate test completes in seconds or minutes.</div></div><div><h3>Comparison with existing methods</h3><div>Few approaches address pixel-by-pixel significance in STA-based STRF estimates. While subspace methods like spike-triggered covariance exist for STRF estimation, they typically do not provide direct voxel-wise or pixel-wise p-values. The proposed method specifically accelerates an exact distribution-based test.</div></div><div><h3>Conclusions and impact</h3><div>The proposed Normal approximation drastically reduces computation time, enabling high-throughput analysis of STRF mapping from spike data. This advancement may foster broader adoption of precise statistical tests of STRFs in large-scale, high-density electrophysiological recordings. Moreover, fast detection of significant STRF features could facilitate closed-loop experiments where stimuli dynamically adapt to changing STRF structures.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100899"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767964","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 term structure geostatistical model with correlated residuals: A comparative analysis 具有相关残差的期限结构地质统计模型：比较分析

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-06-01 Epub Date: 2025-02-17 DOI: 10.1016/j.spasta.2025.100886

Antonella Congedi, Sandra De Iaco, Donato Posa

{"title":"A term structure geostatistical model with correlated residuals: A comparative analysis","authors":"Antonella Congedi, Sandra De Iaco, Donato Posa","doi":"10.1016/j.spasta.2025.100886","DOIUrl":"10.1016/j.spasta.2025.100886","url":null,"abstract":"<div><div>The growth of financial markets and the emerging derivative instruments require the development of advanced techniques for forecasting the term structure of interest rates. In this context, two significant dimensions, i.e. maturity and time, need to be jointly considered in the modeling procedure. In the literature, the Nelson–Siegel model is commonly used to explain the dependence of the interest rates on maturity and time. However, it cannot be excluded that the residuals obtained from Nelson–Siegel estimates are still correlated. At this purpose, a geostatistical approach is adopted and an innovative modeling solution is provided. Indeed, differently from the existing contributions, this paper proposes a dynamic model for predicting the term structure of spot interest rates, where the joint evolution with respect to time and maturity is considered for both the deterministic and the stochastic parts of the model. The relevance as well as the potentiality of the geostatistical modeling techniques extended to treat observations not strictly referred to a geographic system, has been properly underlined. For comparative reasons, different hypotheses on the random field, utilized to describe the interest rates and its trend component, are also assumed and a comparison among predictive performance of alternative models is discussed.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100886"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519048","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 : 2025-06-01 Epub 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

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 : 2025-06-01 Epub Date: 2025-04-28 DOI: 10.1016/j.spasta.2025.100903

Gianfranco Piras , Mauricio Sarrias

引用次数: 0

Bayesian analysis and variable selection for spatial count data with an application to Rio de Janeiro gun violence 空间计数数据的贝叶斯分析与变量选择——以里约热内卢枪支暴力为例

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-06-01 Epub Date: 2025-02-27 DOI: 10.1016/j.spasta.2025.100890

Guilherme Ludwig , Yuan Wang , Tingjin Chu , Haonan Wang , Jun Zhu

{"title":"Bayesian analysis and variable selection for spatial count data with an application to Rio de Janeiro gun violence","authors":"Guilherme Ludwig , Yuan Wang , Tingjin Chu , Haonan Wang , Jun Zhu","doi":"10.1016/j.spasta.2025.100890","DOIUrl":"10.1016/j.spasta.2025.100890","url":null,"abstract":"<div><div>Statistical analysis has been successfully applied to crime data for identification of crime hot spots and prediction of future crimes. In this paper, our main objective is to identify key factors for gun violence in Rio de Janeiro and study the relationship between these key factors and the number of reported events. We use a Bayesian hierarchical stochastic Poisson regression model for spatial counts, which enables us to address the over-dispersed count data and to handle the spatial correlation. Moreover, we propose a variable selection method for key factor identification based on the spike-and-slab prior distribution for the regression coefficients. A new Gibbs sampler is developed for sampling from the posterior distributions with the help of augmentation of Pólya-Gamma auxiliary variables. Simulation studies are used to demonstrate the performance of our proposed approach. Our analysis of the gun violence data in Rio de Janeiro reveals the relationship between violence events and socio-demographic covariates as well as an interpretable spatial random effect that accounts for unmeasured covariate information.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100890"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519049","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

Bayesian adaptive Lasso estimation for partially linear hierarchical spatial autoregressive model 部分线性层次空间自回归模型的贝叶斯自适应Lasso估计

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-06-01 Epub Date: 2025-03-12 DOI: 10.1016/j.spasta.2025.100892

Miao Long, Zhimeng Sun

引用次数: 0

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-06-01 Epub 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":"2025-06-01","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

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 : 2025-06-01 Epub 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":"2025-06-01","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