Spatial Statistics最新文献

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

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 , Adam M. Sykulski , Sophie Fielding , 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

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

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

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

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, Jingxuan Li, 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

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 , Raquel Menezes , Gonçalo Araújo , Renato Rosa , Ana Moreno , Alexandra Silva , 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