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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 : 2025-04-01 Epub Date: 2025-02-22 DOI: 10.1016/j.spasta.2025.100888
Zaineb Smida , Thibault Laurent , Lionel Cucala
{"title":"A Hotelling spatial scan statistic for functional data: Application to economic and climate data","authors":"Zaineb Smida ,&nbsp;Thibault Laurent ,&nbsp;Lionel Cucala","doi":"10.1016/j.spasta.2025.100888","DOIUrl":"10.1016/j.spasta.2025.100888","url":null,"abstract":"<div><div>A scan method for functional data indexed in space has been developed. The scan statistic is derived from the Hotelling test statistic for functional data, extending the univariate and multivariate Gaussian spatial scan statistics. This method consistently outperforms existing techniques in detecting and locating spatial clusters, as demonstrated through simulations. It has been applied to two types of real data: economic data in order to identify spatial clusters of abnormal unemployment rates in Spain and climatic data in order to detect unusual climate change patterns in Great Britain, Nigeria, Pakistan, and Venezuela.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"66 ","pages":"Article 100888"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508312","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
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 : 2025-04-01 Epub Date: 2025-02-06 DOI: 10.1016/j.spasta.2025.100883
Qi Wang, Paul A. Parker, Robert Lund
{"title":"Spatial deep convolutional neural networks","authors":"Qi Wang,&nbsp;Paul A. Parker,&nbsp;Robert Lund","doi":"10.1016/j.spasta.2025.100883","DOIUrl":"10.1016/j.spasta.2025.100883","url":null,"abstract":"<div><div>Spatial prediction problems often use Gaussian process models, which can be computationally burdensome in high dimensions. Specification of an appropriate covariance function for the model can be challenging when complex non-stationarities exist. Recent work has shown that pre-computed spatial basis functions and a feed-forward neural network can capture complex spatial dependence structures while remaining computationally efficient. This paper builds on this literature by tailoring spatial basis functions for use in convolutional neural networks. Through both simulated and real data, we demonstrate that this approach yields more accurate spatial predictions than existing methods. Uncertainty quantification is also considered.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"66 ","pages":"Article 100883"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402602","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
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 : 2025-03-01 Epub Date: 2024-11-23 DOI: 10.1016/j.spasta.2024.100874
Pierpaolo D’Urso , Livia De Giovanni , Lorenzo Federico , Vincenzina Vitale
{"title":"Fuzzy clustering of mixed data with spatial regularization","authors":"Pierpaolo D’Urso ,&nbsp;Livia De Giovanni ,&nbsp;Lorenzo Federico ,&nbsp;Vincenzina Vitale","doi":"10.1016/j.spasta.2024.100874","DOIUrl":"10.1016/j.spasta.2024.100874","url":null,"abstract":"<div><div>A fuzzy clustering model for data with mixed features and spatial constraints is proposed. The clustering model allows different types of variables, or attributes, to be taken into account. This result is achieved by combining the dissimilarity measures for each attribute employing a weighting scheme, to obtain a distance measure for multiple attributes. The weights are objectively computed during the optimization process. The weights reflect the relevance of each attribute type in the clustering results. A spatial term is taken into account, considering a wide definition of contiguity, either physical contiguity or the adjacency matrix in a network. Simulation studies and two empirical applications, including both physical and abstract definitions of contiguity are presented that show the effectiveness of the proposed clustering model.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100874"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719822","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
Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices 结合半参数 SAR 模型和 GAMLSS 模型为对冲价格建立均值和非均质方差的联合空间模型
IF 2.1 2区 数学
Spatial Statistics Pub Date : 2025-03-01 Epub Date: 2024-11-16 DOI: 10.1016/j.spasta.2024.100864
J.D. Toloza-Delgado , O.O. Melo , N.A. Cruz
{"title":"Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices","authors":"J.D. Toloza-Delgado ,&nbsp;O.O. Melo ,&nbsp;N.A. Cruz","doi":"10.1016/j.spasta.2024.100864","DOIUrl":"10.1016/j.spasta.2024.100864","url":null,"abstract":"<div><div>In the context of spatial econometrics, it is very useful to have methodologies that allow modeling the spatial dependence of the observed variables and obtaining more precise predictions of both the mean and the variability of the response variable, something very useful in territorial planning and public policies. This paper proposes a new methodology that jointly models the mean and the variance. Also, it allows to model the spatial dependence of the dependent variable as a function of covariates and to model the semiparametric effects in both models. The algorithms developed are based on generalized additive models that allow the inclusion of non-parametric terms in both the mean and the variance, maintaining the traditional theoretical framework of spatial regression. The theoretical developments of the estimation of this model are carried out, obtaining desirable statistical properties in the estimators. A simulation study is developed to verify that the proposed method has a remarkable predictive capacity in terms of the mean square error and shows a notable improvement in the estimation of the spatial autoregressive parameter, compared to other traditional methods and some recent developments. The model is also tested on data from the construction of a hedonic price model for the city of Bogotá, highlighting as the main result the ability to model the variability of housing prices, and the wealth in the analysis obtained.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100864"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707294","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
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 : 2025-03-01 Epub 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,&nbsp;Ruizhuo Zheng,&nbsp;Aibing Ji,&nbsp;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":"2025-03-01","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
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 : 2025-03-01 Epub Date: 2024-12-27 DOI: 10.1016/j.spasta.2024.100878
Guowang Luo , Mixia Wu
{"title":"Bias-corrected instrumental variable estimation for spatial autoregressive models with measurement errors","authors":"Guowang Luo ,&nbsp;Mixia Wu","doi":"10.1016/j.spasta.2024.100878","DOIUrl":"10.1016/j.spasta.2024.100878","url":null,"abstract":"<div><div>In this paper, bias-corrected instrumental variable estimation methods, specifically the bias-corrected two-stage least square (2SLS) estimation and the bias-corrected asymptotically best 2SLS estimation, are proposed for spatial autoregressive (SAR) models with covariate measurement errors, utilizing available information regarding the variance of the measurement error. Under mild assumptions, the consistency and asymptotic normality of the proposed estimators are derived. Simulation studies further reveal that the proposed methods exhibit robustness regardless of the presence of spatial dependence in the model. Additionally, a real data example is utilized to illustrate the developed methods.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100878"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163870","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
Simulation of conditional non-Gaussian random fields with directional asymmetry 模拟具有方向不对称性的条件非高斯随机场
IF 2.1 2区 数学
Spatial Statistics Pub Date : 2025-03-01 Epub Date: 2024-11-17 DOI: 10.1016/j.spasta.2024.100872
Sebastian Hörning , András Bárdossy
{"title":"Simulation of conditional non-Gaussian random fields with directional asymmetry","authors":"Sebastian Hörning ,&nbsp;András Bárdossy","doi":"10.1016/j.spasta.2024.100872","DOIUrl":"10.1016/j.spasta.2024.100872","url":null,"abstract":"<div><div>Observed environmental are usually the results of physical, chemical, or biological processes. These processes often introduce asymmetries which should be considered when analysing and modelling the observed variables. In a geostatistical context, there are two main types of asymmetry. The first is rank-asymmetry, i.e., low and high values exhibit different spatial dependence structures. The second is order-asymmetry, i.e., the spatial dependence structure is distinguishable in different directions. Both asymmetries, if significant, indicate that the corresponding random field has a non-Gaussian dependence structure. These asymmetries are not part of the classical geostatistical workflow. Taking asymmetry into account however is likely to improve the estimation and the uncertainty assessment at unobserved locations. In this contribution a stochastic model which can be used to simulate asymmetrical random fields with any of the asymmetries or with their combination is presented. Synthetically simulated flow fields and the well known Walker lake dataset are used to demonstrate the methodology.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100872"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707342","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
Fast mixture spatial regression: A mixture in the geographical and feature space applied to predict porosity in the post-salt 快速混合空间回归：将地理空间和特征空间的混合应用于预测盐湖开采后的孔隙度
IF 2.1 2区 数学
Spatial Statistics Pub Date : 2025-03-01 Epub Date: 2024-11-22 DOI: 10.1016/j.spasta.2024.100873
Lucas Michelin , Lucas C. Godoy , Heitor S. Ramos , Marcos O. Prates
{"title":"Fast mixture spatial regression: A mixture in the geographical and feature space applied to predict porosity in the post-salt","authors":"Lucas Michelin ,&nbsp;Lucas C. Godoy ,&nbsp;Heitor S. Ramos ,&nbsp;Marcos O. Prates","doi":"10.1016/j.spasta.2024.100873","DOIUrl":"10.1016/j.spasta.2024.100873","url":null,"abstract":"<div><div>Extracting geological resources like hydrocarbon fluids requires significant investments and precise decision-making processes. To optimize the efficiency of the extraction process, researchers and industry experts have explored innovative methodologies, including the prediction of optimal drilling locations. Porosity, a key attribute of reservoir rocks, plays a crucial role in determining fluid storage capacity. Geostatistical techniques, such as kriging, have been widely used for estimating porosity by capturing spatial dependence in sampled point-referenced data. However, the reliance on geographical coordinates for determining spatial distances may present challenges in scenarios with small and widely separated samples. In this paper, we develop a mixture model that combines the covariance generated by geographical space and the covariance generated in an appropriate feature space to enhance estimation accuracy. Developed within the Bayesian framework, our approach utilizes flexible Markov Chain Monte Carlo (MCMC) methods and leverages the Nearest-Neighbor Gaussian Process (NNGP) strategy for scalability. We present a controlled empirical comparison, considering various data generation configurations, to assess the performance of the mixture model in comparison to the marginal models. Applying our models to a three-dimensional reservoir demonstrates its practical applicability and scalability. This research presents a novel approach for improved porosity estimation by integrating spatial and covariate information, offering the potential for optimizing reservoir exploration and extraction activities.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100873"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719821","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
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 : 2025-03-01 Epub Date: 2024-12-17 DOI: 10.1016/j.spasta.2024.100877
Rian Botes , Inger Fabris-Rotelli , Kabelo Mahloromela , Ding-Geng Chen
{"title":"An optimised rabies vaccination schedule for rural settlements","authors":"Rian Botes ,&nbsp;Inger Fabris-Rotelli ,&nbsp;Kabelo Mahloromela ,&nbsp;Ding-Geng Chen","doi":"10.1016/j.spasta.2024.100877","DOIUrl":"10.1016/j.spasta.2024.100877","url":null,"abstract":"<div><div>The timely and efficient administration of rabies vaccinations to animals in rural villages is necessary to attain a state of herd immunity. Efficient sampling of households in a rural village is of utmost importance in reaching the most animals for vaccination, with the least effort, and in the lowest time. This research seeks to both optimise the spatial sampling scheme used to sample households, as well as the route travelled by persons performing door-to-door vaccinations. The walking time in minutes is regarded as the cost of a vaccination scheme and is minimised in this paper. The distribution of houses in a rural village constitutes a spatial point pattern in <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, and as such, spatial point pattern analysis techniques as well as some spatial sampling schemes are applied throughout this research. The penultimate aim of this work is to provide policy makers with additional tools to combat rabies, a disease which remains endemic to some countries in West and Central Africa, and Asia.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100877"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163871","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
Pixel isotropy test based on directional perimeters 基于方向周长的像素各向同性测试
IF 2.1 2区 数学
Spatial Statistics Pub Date : 2025-03-01 Epub Date: 2024-11-20 DOI: 10.1016/j.spasta.2024.100869
Mariem Abaach , Hermine Biermé , Elena Di Bernardino , Anne Estrade
{"title":"Pixel isotropy test based on directional perimeters","authors":"Mariem Abaach ,&nbsp;Hermine Biermé ,&nbsp;Elena Di Bernardino ,&nbsp;Anne Estrade","doi":"10.1016/j.spasta.2024.100869","DOIUrl":"10.1016/j.spasta.2024.100869","url":null,"abstract":"<div><div>In this paper we consider the so-called directional perimeters of a thresholded gray-level image. These geometrical quantities are built by considering separately the horizontal and vertical contributions of the pixel. We explicitly compute the first two moments of the directional perimeter under the hypothesis of an underlying discrete Gaussian stationary random field. We establish a central limit theorem (CLT), as the number of pixels goes to infinity, for the joint directional perimeters at various levels under a weak summability condition of the covariance function. By using the CLT previously established, we construct a consistent pixel isotropy test, based on the ratio of the directional perimeters. Our theoretical study is completed by extensive numerical illustrations based on simulated data. Finally, we apply our method to detect pixel anisotropy in calcaneus X-ray images.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"65 ","pages":"Article 100869"},"PeriodicalIF":2.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707295","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
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