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Growth of spatial statistics for agriculture and environment: The example of BioSP at INRAE 农业与环境空间统计的增长:以印度农业与环境研究所的BioSP为例
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-10-01 DOI: 10.1016/j.spasta.2025.100938
Denis Allard
{"title":"Growth of spatial statistics for agriculture and environment: The example of BioSP at INRAE","authors":"Denis Allard","doi":"10.1016/j.spasta.2025.100938","DOIUrl":"10.1016/j.spasta.2025.100938","url":null,"abstract":"<div><div>This paper illustrates how progress in spatial statistics is fueled by scientific questions arising from applications in agriculture and environment. The unifying theme is the work that has been carried out at BioSP, a statistics and mathematics research unit mainly affiliated to the “Mathematics and Digital Technologies” division at INRAE, the French National Research Institute for Agriculture, Food and Environment. Starting from the 20 contributions that BioSP members have published in <em>Spatial Statistics</em> since its creation in 2012, almost fifteen years of advances are reviewed, spanning point processes, (multivariate) spatio-temporal Gaussian processes, compositional data, stochastic weather generators and extreme value theory. Most of the content is focused on theoretical and methodological developments, with examples being limited due to length constraints for the article. Attention is given to how these advances have been inspired by problems arising in other research domains. In return, it will be shown how they have opened new research questions in spatial statistics and how they had impact in the scientific fields they originated from. In conclusion, some perspectives and outlooks are discussed, in particular in relation to the AI revolution.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100938"},"PeriodicalIF":2.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269553","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 concordance coefficient for lattice data: An application to poverty indices in Chile 格点数据的一致性系数:在智利贫困指数中的应用
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-27 DOI: 10.1016/j.spasta.2025.100936
Ronny Vallejos , Clemente Ferrer , Jorge Mateu
{"title":"A concordance coefficient for lattice data: An application to poverty indices in Chile","authors":"Ronny Vallejos ,&nbsp;Clemente Ferrer ,&nbsp;Jorge Mateu","doi":"10.1016/j.spasta.2025.100936","DOIUrl":"10.1016/j.spasta.2025.100936","url":null,"abstract":"<div><div>This paper introduces a novel coefficient for measuring agreement between two lattice sequences observed in the same areal units, motivated by the analysis of different methodologies for measuring poverty rates in Chile. Building on the multivariate concordance coefficient framework, our approach accounts for dependencies in the multivariate lattice process using a non-negative definite matrix of weights, assuming a Multivariate Conditionally Autoregressive (GMCAR) process. We adopt a Bayesian perspective for inference, using summaries from Bayesian estimates. The methodology is illustrated through an analysis of poverty rates in the Metropolitan and Valparaíso regions of Chile, with High Posterior Density (HPD) intervals provided for the poverty rates. This work addresses a methodological gap in the understanding of agreement coefficients and enhances the usability of these measures in the context of social variables typically assessed in areal units.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100936"},"PeriodicalIF":2.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222041","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
Navigating challenges in spatio-temporal modelling of Antarctic krill abundance: Addressing zero-inflated data and misaligned covariates 南极磷虾丰度时空建模中的导航挑战:解决零膨胀数据和错位协变量
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-26 DOI: 10.1016/j.spasta.2025.100937
André Victor Ribeiro Amaral , Adam M. Sykulski , Sophie Fielding , Emma Cavan
{"title":"Navigating challenges in spatio-temporal modelling of Antarctic krill abundance: Addressing zero-inflated data and misaligned covariates","authors":"André Victor Ribeiro Amaral ,&nbsp;Adam M. Sykulski ,&nbsp;Sophie Fielding ,&nbsp;Emma Cavan","doi":"10.1016/j.spasta.2025.100937","DOIUrl":"10.1016/j.spasta.2025.100937","url":null,"abstract":"<div><div>Antarctic krill (<em>Euphausia superba</em>) are among the most abundant species on our planet and serve as a vital food source for many marine predators in the Southern Ocean. In this paper, we utilise statistical spatio-temporal methods to combine data from various sources and resolutions, aiming to model krill abundance. Our focus lies in fitting the model to a dataset comprising acoustic measurements of krill biomass. To achieve this, we integrate climate covariates obtained from satellite imagery and from drifting surface buoys (also known as drifters). Additionally, we use sparsely collected krill biomass data obtained from net fishing efforts (KRILLBASE) for validation. However, integrating these multiple heterogeneous data sources presents significant modelling challenges, including spatio-temporal misalignment and inflated zeros in the observed data. To address these challenges, we fit a Hurdle-Gamma model to jointly describe the occurrence of zeros and the krill biomass for the non-zero observations, while also accounting for misaligned and heterogeneous data sources, including drifters. Therefore, our work presents a comprehensive framework for analysing and predicting krill abundance in the Southern Ocean, leveraging information from various sources and formats. This is crucial due to the impact of krill fishing, as understanding their distribution is essential for informed management decisions and fishing regulations aimed at protecting the species.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100937"},"PeriodicalIF":2.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222039","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 framework for analysing point patterns on nonconvex domains using visibility graphs and multidimensional scaling 一个使用可见性图和多维尺度分析非凸域上点模式的框架
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-25 DOI: 10.1016/j.spasta.2025.100935
Kabelo Mahloromela, Inger Fabris-Rotelli
{"title":"A framework for analysing point patterns on nonconvex domains using visibility graphs and multidimensional scaling","authors":"Kabelo Mahloromela,&nbsp;Inger Fabris-Rotelli","doi":"10.1016/j.spasta.2025.100935","DOIUrl":"10.1016/j.spasta.2025.100935","url":null,"abstract":"<div><div>A point pattern is typically analysed to understand the first- and second-order properties of the underlying point process. These properties are usually inferred using estimation procedures that depend on interpoint distance and are thus sensitive to the choice of distance metric. Euclidean distance is conventionally used to quantify proximity between points, but it does not accurately reflect spatial relationships when points are constrained within irregular, nonconvex spatial domains. Herein, we propose a strategy to embed visibility graph distances into Euclidean metric space using multidimensional scaling. The aim is to simplify analyses, leverage well-developed methods based on Euclidean distance, and retain, as far as possible, the true proximity relationships on a nonconvex spatial domain. The kernel smoothed intensity estimate and the <span><math><mi>K</mi></math></span>-function are computed in this new spatial context and used to validate the effectiveness of the embedding strategy.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100935"},"PeriodicalIF":2.5,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222040","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
Bandwidth selection for the intensity in spatial point processes 空间点过程中强度的带宽选择
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-25 DOI: 10.1016/j.spasta.2025.100928
Yangha Chung , Ji Meng Loh , Woncheol Jang
{"title":"Bandwidth selection for the intensity in spatial point processes","authors":"Yangha Chung ,&nbsp;Ji Meng Loh ,&nbsp;Woncheol Jang","doi":"10.1016/j.spasta.2025.100928","DOIUrl":"10.1016/j.spasta.2025.100928","url":null,"abstract":"<div><div>We introduce a doubly-smoothed bandwidth selection method to obtain bandwidth matrices <span><math><mi>H</mi></math></span> for estimating the intensity function of a spatial point process. The doubly-smoothed bootstrap involves taking bootstrap samples by adding random noise and using Dirichlet rather than multinomial weights. The mean integrated squared error (MISE) and asymptotic mean integrated squared error (AMISE) as a function of <span><math><mi>H</mi></math></span> can then be computed numerically using the bootstrap samples, with optimal <span><math><mi>H</mi></math></span> obtained by minimizing the MISE or AMISE with respect to <span><math><mrow><mi>H</mi><mo>.</mo></mrow></math></span> We present simulation results comparing the doubly-smoothed bandwidth selection method with other methods for a number of intensity functions. We also apply our methods to a data set of police pedestrian stops in New York City.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100928"},"PeriodicalIF":2.5,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222042","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 and spatio-temporal cluster detection using stacking 基于叠加的时空聚类检测
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-24 DOI: 10.1016/j.spasta.2025.100933
Maria E. Kamenetsky , Jun Zhu , Ronald E. Gangnon
{"title":"Spatial and spatio-temporal cluster detection using stacking","authors":"Maria E. Kamenetsky ,&nbsp;Jun Zhu ,&nbsp;Ronald E. Gangnon","doi":"10.1016/j.spasta.2025.100933","DOIUrl":"10.1016/j.spasta.2025.100933","url":null,"abstract":"<div><div>Patterns in disease across space and time are important to epidemiologists and health professionals because they may indicate underlying elevated disease risk. In some cases, elevated risk may be driven by environmental exposures, infectious diseases or other factors where timely public health interventions are important. The spatial and spatio-temporal scan statistics identify a single most likely cluster or equivalently select a single correct model. We instead consider an ensemble of single cluster models. We use stacking, a model-averaging technique, to combine relative risk estimates from all of the single cluster models into a sequence of meta-models indexed by the effective number of parameters/clusters. The number of parameters/spatio-temporal clusters is chosen using information criteria. A simulation study is conducted to demonstrate the statistical properties of the stacking method. The method is illustrated using a dataset of female breast cancer incidence data at the municipality level in Japan.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100933"},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159567","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
Specifying spatial effects in panel data: Locally robust vs. conditional tests 指定面板数据中的空间效果:局部鲁棒测试与条件测试
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-23 DOI: 10.1016/j.spasta.2025.100934
Giovanni Millo
{"title":"Specifying spatial effects in panel data: Locally robust vs. conditional tests","authors":"Giovanni Millo","doi":"10.1016/j.spasta.2025.100934","DOIUrl":"10.1016/j.spasta.2025.100934","url":null,"abstract":"<div><div>We address the issue of specifying a spatial lag vs. spatial error process in spatial panel models. The popular locally robust Lagrange multiplier (RLM) tests for spatial lag vs. error are compared to optimal alternatives based on maximum likelihood estimation: Wald and likelihood ratio (LR) tests requiring estimation of the full encompassing model, and conditional Lagrange multiplier (CLM) tests drawing on the reduced specification. Monte Carlo simulations are performed in a typical spatial panel context. Individual effects are successfully eliminated through the forward orthogonal deviations transformation, making the RLM suitable for panel data. Nevertheless, the statistical properties of Wald and LR are superior to those of the RLM. The CLM also dominates the RLM, as long as the sample is at least of moderate size. The RLM are computationally very convenient, but ML-based tests are feasible in most usage cases on mainstream hardware.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100934"},"PeriodicalIF":2.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159568","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
Model averaging for spatial autoregressive panel data models 空间自回归面板数据模型的模型平均
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-23 DOI: 10.1016/j.spasta.2025.100931
Aibing Ji, Jingxuan Li, Qingqing Li
{"title":"Model averaging for spatial autoregressive panel data models","authors":"Aibing Ji,&nbsp;Jingxuan Li,&nbsp;Qingqing Li","doi":"10.1016/j.spasta.2025.100931","DOIUrl":"10.1016/j.spasta.2025.100931","url":null,"abstract":"<div><div>The spatial autoregressive panel data models are widely employed in regional economics to capture spatial dependencies, but conventional specifications rely on a single spatial weight matrix, heightening the risk of model misspecification. Current research lacks systematic model averaging methods for integrating multiple weight matrices and addressing spatial effect uncertainty. This study proposes a novel model averaging framework for spatial autoregressive panel data models with fixed effects, extending model averaging methodology to the spatial panel context and enabling flexible combinations of multiple weight matrices for both dependent variables and error terms. An adaptive Mallows-type criterion is developed, dynamically adjusting to the presence or absence of spatial effects, with its asymptotic optimality established. Monte Carlo simulations confirm robustness across scenarios with no, single, or mixed spatial dependencies. An empirical application to Chinese provincial housing prices identifies economic adjacency as the key spatial dependence driver, validating the method’s predictive accuracy and policy utility for spatiotemporal data analysis.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100931"},"PeriodicalIF":2.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159566","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
Uncertain spatial autoregressive model with applications to regional economic analysis and regional air quality analysis 不确定空间自回归模型及其在区域经济分析和区域空气质量分析中的应用
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-22 DOI: 10.1016/j.spasta.2025.100932
Jinsheng Xie
{"title":"Uncertain spatial autoregressive model with applications to regional economic analysis and regional air quality analysis","authors":"Jinsheng Xie","doi":"10.1016/j.spasta.2025.100932","DOIUrl":"10.1016/j.spasta.2025.100932","url":null,"abstract":"<div><div>This study aims to establish uncertain spatial statistics by exploring the uncertain spatial autoregressive model firstly. Modeling the observations of the response variable via uncertain variables and assuming they are affected by neighboring observations, this paper explores an approach of the uncertain spatial autoregressive model to estimate relationships among the uncertain variables with spatial locations. By employing the principle of least squares, a minimization problem is provided to estimate unknown parameters in the uncertain spatial autoregressive model. Finally, two real-world examples of regional economic analysis and regional air quality analysis are given to clearly demonstrate the uncertain spatial autoregressive model.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100932"},"PeriodicalIF":2.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222043","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 model for zero-inflated data combining fishery-dependent and fishery-independent sources 结合渔业依赖和渔业独立来源的零膨胀数据联合模型
IF 2.5 2区 数学
Spatial Statistics Pub Date : 2025-09-11 DOI: 10.1016/j.spasta.2025.100930
Daniela Silva , Raquel Menezes , Gonçalo Araújo , Renato Rosa , Ana Moreno , Alexandra Silva , Susana Garrido
{"title":"Joint model for zero-inflated data combining fishery-dependent and fishery-independent sources","authors":"Daniela Silva ,&nbsp;Raquel Menezes ,&nbsp;Gonçalo Araújo ,&nbsp;Renato Rosa ,&nbsp;Ana Moreno ,&nbsp;Alexandra Silva ,&nbsp;Susana Garrido","doi":"10.1016/j.spasta.2025.100930","DOIUrl":"10.1016/j.spasta.2025.100930","url":null,"abstract":"<div><div>Accurately identifying spatial patterns of species distribution is crucial for scientific insight and societal benefit, aiding our understanding of species fluctuations. The increasing quantity and quality of ecological datasets present heightened statistical challenges, complicating spatial species dynamics comprehension. Addressing the complex task of integrating multiple data sources to enhance spatial fish distribution understanding in marine ecology, this study introduces a pioneering five-layer Joint model. The model adeptly integrates fishery-independent and fishery-dependent data, accommodating zero-inflated data and distinct sampling processes. A comprehensive simulation study evaluates the model performance across various preferential sampling scenarios and sample sizes, elucidating its advantages and challenges. Our findings highlight the model’s robustness in estimating preferential parameters, emphasizing differentiation between presence–absence and biomass observations. Evaluation of estimation of spatial covariance and prediction performance underscores the model’s reliability. Augmenting sample sizes reduces parameter estimation variability, aligning with the principle that increased information enhances certainty. Assessing the contribution of each data source reveals successful integration, providing a comprehensive representation of biomass patterns. Empirical application within a real-world context further solidifies the model’s efficacy in capturing species’ spatial distribution. This research advances methodologies for integrating diverse datasets with different sampling natures further contributing to a more informed understanding of spatial dynamics of marine species.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100930"},"PeriodicalIF":2.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107607","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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