Spatial Statistics最新文献_第8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 : 2025-06-01 Epub 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":"2025-06-01","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 : 2025-06-01 Epub 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

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

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 : 2025-04-01 Epub Date: 2025-02-22 DOI: 10.1016/j.spasta.2025.100889

Yanxiu Jin , Tomoya Wakayama , Renhe Jiang , Shonosuke Sugasawa

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 : 2025-04-01 Epub 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

Clustering of compound events based on multivariate comonotonicity 基于多元共单调性的复合事件聚类

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-01 Epub Date: 2025-01-27 DOI: 10.1016/j.spasta.2025.100881

Fabrizio Durante , Sebastian Fuchs , Roberta Pappadà

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 : 2025-04-01 Epub Date: 2025-02-18 DOI: 10.1016/j.spasta.2025.100887

Lingling Tian , Chuanhua Wei , Mixia Wu

引用次数: 0

Measuring unit relevance and stability in hierarchical spatio-temporal clustering 层次时空聚类中度量单元相关性和稳定性

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-01 Epub Date: 2025-01-13 DOI: 10.1016/j.spasta.2025.100880

Roy Cerqueti , Raffaele Mattera

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 : 2025-04-01 Epub 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":"2025-04-01","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

The Multifractal Gaussian Mixture Model for unsupervised segmentation of complex data sets 复杂数据集无监督分割的多重分形高斯混合模型

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-04-01 Epub Date: 2025-01-10 DOI: 10.1016/j.spasta.2025.100879

Garry Jacyna, Damon Frezza, David M. Slater, James R. Thompson

{"title":"The Multifractal Gaussian Mixture Model for unsupervised segmentation of complex data sets","authors":"Garry Jacyna, Damon Frezza, David M. Slater, James R. Thompson","doi":"10.1016/j.spasta.2025.100879","DOIUrl":"10.1016/j.spasta.2025.100879","url":null,"abstract":"<div><div>We derive the Multifractal Gaussian Mixture Model algorithm for decomposing data sets into different multifractal regimes building on the empirical observation that simulated multifractals have log wavelet leaders that are well-approximated by a Gaussian distribution. We test the algorithm on composite images constructed from multifractal random walks with known multifractal spectra. The algorithm is able to correctly segment the pixels corresponding to different multifractals when the constituent multifractals are most distinct from each other. It also estimates the multifractal parameters with minimal error when compared to the theoretical spectra used to generate the original multifractal random walks. We also apply the algorithm to satellite images with varying degrees of cloud cover taken from the LandSat 8 Cloud Validation Data set. The algorithm is able to segment the pixels into their corresponding cloud mask category, and it detects different texture and features in the images that are unrelated to clouds. The results indicate that the Multifractal Gaussian Mixture Model algorithm is well-suited for semi-automated unsupervised data segmentation when the data being analyzed exhibit complex, scale-invariant characteristics.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"66 ","pages":"Article 100879"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151694","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