Spatial Statistics最新文献_第3页

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

Spatial deep convolutional neural networks 空间深度卷积神经网络

IF 2.1 2区数学

Spatial Statistics Pub Date : 2025-02-06 DOI: 10.1016/j.spasta.2025.100883

Qi Wang, Paul A. Parker, Robert Lund

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 : 2025-01-27 DOI: 10.1016/j.spasta.2025.100881

Fabrizio Durante , Sebastian Fuchs , Roberta Pappadà

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 : 2025-01-13 DOI: 10.1016/j.spasta.2025.100880

Roy Cerqueti , Raffaele Mattera

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 : 2025-01-10 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":"2025-01-10","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

Bias-corrected instrumental variable estimation for spatial autoregressive models with measurement errors 带有测量误差的空间自回归模型的偏差校正工具变量估计

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-27 DOI: 10.1016/j.spasta.2024.100878

Guowang Luo , Mixia Wu

引用次数: 0

An optimised rabies vaccination schedule for rural settlements 优化农村居民点狂犬病疫苗接种计划

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-17 DOI: 10.1016/j.spasta.2024.100877

Rian Botes , Inger Fabris-Rotelli , Kabelo Mahloromela , Ding-Geng Chen

引用次数: 0

Softening the criteria for determining inner and outer predicted exceedance sets 软化确定内部和外部预测超出集的标准

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-12-17 DOI: 10.1016/j.spasta.2024.100876

Thomas Suesse , Alexander Brenning

{"title":"Softening the criteria for determining inner and outer predicted exceedance sets","authors":"Thomas Suesse , Alexander Brenning","doi":"10.1016/j.spasta.2024.100876","DOIUrl":"10.1016/j.spasta.2024.100876","url":null,"abstract":"<div><div>Determining exceedance regions, such as regions where a specified threshold of a pollutant in the environment is exceeded, is of critical importance for decision-making in environmental management and public health. Inner and outer predicted exceedance sets express the uncertainties in predicted exceedance regions as they sandwich the unknown true exceedance region with high confidence, analogous to confidence regions for point estimates. It is therefore desirable to reduce the uncertainty about the locations of the true exceedance region, resulting in a narrow band between the inner and outer sets. However, in practice this is not often the case mainly due to the strict statistical subset criteria being set, which are equivalent to a multiple testing problem controlling the familywise error rate (FWER). It is well known that the FWER leads to fewer rejections compared to other criteria; in the context of exceedance regions, this would correspond to an extremely small, conservative inner predicted exceedance region. In this paper, we loosen the criteria slightly to obtain a narrower band between inner and outer sets, allowing for more nuanced uncertainty assessments. A new algorithm is proposed to construct these exceedance sets, and the methods are compared in a simulation study to assess whether they indeed control the new criteria. The methods are illustrated on two data sets: average rainfall in the state of Paraná, Brazil, and nitrogen dioxide air pollution in Germany in the year 2018.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100876"},"PeriodicalIF":2.1,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fixed effects spatial panel interval-valued autoregressive models and applications 固定效应空间面板区间值自回归模型及其应用

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-26 DOI: 10.1016/j.spasta.2024.100875

Qingqing Li, Ruizhuo Zheng, Aibing Ji, Hongyan Ma

{"title":"Fixed effects spatial panel interval-valued autoregressive models and applications","authors":"Qingqing Li, Ruizhuo Zheng, Aibing Ji, Hongyan Ma","doi":"10.1016/j.spasta.2024.100875","DOIUrl":"10.1016/j.spasta.2024.100875","url":null,"abstract":"<div><div>Interval-valued data has garnered attention across various applications, leading to increased research into spatial interval-valued data models. The integration of uncertainty variables into spatial panel data models has become crucial. This paper presents a spatial panel interval-valued autoregressive model with fixed effects, utilizing the parametric method. The quasi-maximum likelihood method is employed for parameter estimation, and its consistency and asymptotic properties are discussed. Additionally, three special cases and two degenerated models derived from our framework are presented, elucidating their significance in spatial statistics. Monte Carlo simulations are used to validate the fitting and forecasting performance of our proposed models across diverse scenarios. Furthermore, the models are implemented in real-world air quality and house price datasets for forecasting purposes. Through rigorous experimentation, the superior performance of the models is demonstrated. These results highlight the practical utility of the spatial panel interval-valued autoregressive models in addressing spatial data challenges.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100875"},"PeriodicalIF":2.1,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747091","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

Fuzzy clustering of mixed data with spatial regularization 利用空间正则化对混合数据进行模糊聚类

IF 2.1 2区数学

Spatial Statistics Pub Date : 2024-11-23 DOI: 10.1016/j.spasta.2024.100874

Pierpaolo D’Urso , Livia De Giovanni , Lorenzo Federico , Vincenzina Vitale

引用次数: 0