Spatial Statistics最新文献_第2页

Integrating multi-source geospatial information using Bayesian maximum entropy: A case study on design ground snow load prediction 基于贝叶斯最大熵的多源地理空间信息集成——以设计地面雪荷载预测为例

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-03-26 DOI: 10.1016/j.spasta.2025.100894

Kinspride Duah, Yan Sun, Brennan Bean

{"title":"Integrating multi-source geospatial information using Bayesian maximum entropy: A case study on design ground snow load prediction","authors":"Kinspride Duah, Yan Sun, Brennan Bean","doi":"10.1016/j.spasta.2025.100894","DOIUrl":"10.1016/j.spasta.2025.100894","url":null,"abstract":"<div><div>Environmental data are often imprecise due to various limitations and uncertainties in the measuring process. As a result, they often consist of a combination of both precise and imprecise information, referred to as hard and soft data, respectively. Often in practice, soft data are characterized as intervals as a simple form to properly preserve the underlying imprecision. Bayesian maximum entropy (BME) is a generalized spatial interpolation method that processes both hard and soft data simultaneously to effectively account for both spatial uncertainty and measurement imprecision. This paper presents a rigorous evaluation to compare the performances of BME and kriging through both simulation and a case study of reliability-targeted design ground snow load (RTDSL) prediction in Utah. The dataset contains a mixture of hard and soft-interval observations, and kriging uses the soft-interval data by extracting the midpoints in addition to the hard data. The cross-validated results show that BME outperforms kriging on multiple error metrics. Specifically for hard data locations where precise observations are known, BME yields a mean error (ME) of 0.0334, a mean absolute error (MAE) of 0.2309, and a root mean squared error (RMSE) of 0.2833, whereas kriging produces a ME of 0.1960, MAE of 0.2793, and RMSE of 0.3698. These results highlight the superior prediction accuracy of BME, particularly in the presence of soft data and/or non-Gaussian hard data.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"67 ","pages":"Article 100894"},"PeriodicalIF":2.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739998","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

Functional summary statistics and testing for independence in multi-type point processes on the surface of three dimensional convex shapes 三维凸形表面多类型点加工的功能汇总统计与独立性检验

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-03-13 DOI: 10.1016/j.spasta.2025.100891

S. Ward, E.A.K. Cohen, N.M. Adams

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 : 2025-03-12 DOI: 10.1016/j.spasta.2025.100892

Miao Long, Zhimeng Sun

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 : 2025-02-27 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":"2025-02-27","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

Derivative-based spatial mediation with INLA-SPDE 基于INLA-SPDE的导数空间中介

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-02-24 DOI: 10.1016/j.spasta.2025.100885

Claudio Rubino , Chiara Di Maria , Antonino Abbruzzo , Gioacchino Bono , Germana Garofalo , Giacomo Milisenda , Giada Adelfio

引用次数: 0

Clustered factor analysis for multivariate spatial data 多元空间数据的聚类因子分析

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-02-22 DOI: 10.1016/j.spasta.2025.100889

Yanxiu Jin , Tomoya Wakayama , Renhe Jiang , Shonosuke Sugasawa

引用次数: 0

A Hotelling spatial scan statistic for functional data: Application to economic and climate data 功能数据的酒店空间扫描统计：在经济和气候数据中的应用

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-02-22 DOI: 10.1016/j.spasta.2025.100888

Zaineb Smida , Thibault Laurent , Lionel Cucala

引用次数: 0

Statistical inference of partially linear time-varying coefficients spatial autoregressive panel data model 部分线性时变系数空间自回归面板数据模型的统计推断

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-02-18 DOI: 10.1016/j.spasta.2025.100887

Lingling Tian , Chuanhua Wei , Mixia Wu

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 : 2025-02-17 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":"2025-02-17","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

Bayesian geographically weighted regression using Fused Lasso prior 基于融合Lasso先验的贝叶斯地理加权回归

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-02-06 DOI: 10.1016/j.spasta.2025.100884

Toshiki Sakai , Jun Tsuchida , Hiroshi Yadohisa

{"title":"Bayesian geographically weighted regression using Fused Lasso prior","authors":"Toshiki Sakai , Jun Tsuchida , Hiroshi Yadohisa","doi":"10.1016/j.spasta.2025.100884","DOIUrl":"10.1016/j.spasta.2025.100884","url":null,"abstract":"<div><div>A main purpose of spatial data analysis is to predict the objective variable for the unobserved locations. Although Geographically Weighted Regression (GWR) is often used for this purpose, estimation instability proves to be an issue. To address this issue, Bayesian Geographically Weighted Regression (BGWR) has been proposed. In BGWR, by setting the same prior distribution for all locations, the coefficients’ estimation stability is improved. However, when observation locations’ density is spatially different, these methods do not sufficiently consider the similarity of coefficients among locations. Moreover, the prediction accuracy of these methods becomes worse. To solve these issues, we propose Bayesian Geographically Weighted Sparse Regression (BGWSR) that uses Bayesian Fused Lasso for the prior distribution of the BGWR coefficients. Constraining the parameters to have the same values at adjacent locations is expected to improve the prediction accuracy at locations with a low number of adjacent locations. Furthermore, from the predictive distribution, it is also possible to evaluate the uncertainty of the predicted value of the objective variable. By examining numerical studies, we confirmed that BGWSR has better prediction performance than the existing methods (GWR and BGWR) when the density of observation locations is spatial difference. Finally, the BGWSR is applied to land price data in Tokyo. Thus, the results suggest that BGWSR has better prediction performance and smaller uncertainty than existing methods.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"66 ","pages":"Article 100884"},"PeriodicalIF":2.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387115","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