This paper investigates a spatial autoregressive (SAR) panel data model featuring fixed effects and time-varying coefficients in both the covariates and spatial dependence. We propose a two-stage least squares estimation based on local linear dummy variables (2SLS-LLDV). This method effectively captures individual heterogeneity via dummy variable construction while maintaining computational tractability. Under mild regularity conditions, we establish the asymptotic normality of the proposed estimators. Furthermore, we devise a residual-based bootstrap procedure to test the temporal stability of time-varying spatial dependence parameter, providing a robust mechanism for p-value calculation in finite-sample scenarios. Monte Carlo simulations are conducted to evaluate the finite sample performance of our proposed methods. Finally, we employ our proposed estimation and testing methods to analyze carbon emissions in China and cigarette demand in the United States, demonstrating their practical applicability.
{"title":"Estimation and testing of time-varying coefficients spatial autoregressive panel data model","authors":"Lingling Tian , Chuanhua Wei , Wenxing Ding , Mixia Wu","doi":"10.1016/j.spasta.2025.100922","DOIUrl":"10.1016/j.spasta.2025.100922","url":null,"abstract":"<div><div>This paper investigates a spatial autoregressive (SAR) panel data model featuring fixed effects and time-varying coefficients in both the covariates and spatial dependence. We propose a two-stage least squares estimation based on local linear dummy variables (2SLS-LLDV). This method effectively captures individual heterogeneity via dummy variable construction while maintaining computational tractability. Under mild regularity conditions, we establish the asymptotic normality of the proposed estimators. Furthermore, we devise a residual-based bootstrap procedure to test the temporal stability of time-varying spatial dependence parameter, providing a robust mechanism for p-value calculation in finite-sample scenarios. Monte Carlo simulations are conducted to evaluate the finite sample performance of our proposed methods. Finally, we employ our proposed estimation and testing methods to analyze carbon emissions in China and cigarette demand in the United States, demonstrating their practical applicability.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100922"},"PeriodicalIF":2.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713187","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}
Pub Date : 2025-07-22DOI: 10.1016/j.spasta.2025.100917
Paul B. May , Andrew O. Finley
The USDA Forest Inventory and Analysis (FIA) program conducts a national forest inventory for the United States through a network of permanent field plots. FIA produces estimates of area averages and totals for plot-measured forest variables through design-based inference, assuming a fixed population and a probability sample of field plot locations. The fixed-population assumption and characteristics of the FIA sampling scheme make it difficult to estimate change in forest variables over time using design-based inference. We propose spatial–temporal models based on Gaussian processes as a flexible tool for forest inventory data, capable of inferring forest variables and change thereof over arbitrary spatial and temporal domains. It is shown to be beneficial for the covariance function governing the latent Gaussian process to account for variation at multiple scales, separating spatially local variation from ecosystem-scale variation. We demonstrate a model for forest biomass density, inferring 20 years of biomass change within two US National Forests.
{"title":"Spatial–temporal prediction of forest attributes using latent Gaussian models and inventory data","authors":"Paul B. May , Andrew O. Finley","doi":"10.1016/j.spasta.2025.100917","DOIUrl":"10.1016/j.spasta.2025.100917","url":null,"abstract":"<div><div>The USDA Forest Inventory and Analysis (FIA) program conducts a national forest inventory for the United States through a network of permanent field plots. FIA produces estimates of area averages and totals for plot-measured forest variables through design-based inference, assuming a fixed population and a probability sample of field plot locations. The fixed-population assumption and characteristics of the FIA sampling scheme make it difficult to estimate change in forest variables over time using design-based inference. We propose spatial–temporal models based on Gaussian processes as a flexible tool for forest inventory data, capable of inferring forest variables and change thereof over arbitrary spatial and temporal domains. It is shown to be beneficial for the covariance function governing the latent Gaussian process to account for variation at multiple scales, separating spatially local variation from ecosystem-scale variation. We demonstrate a model for forest biomass density, inferring 20 years of biomass change within two US National Forests.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100917"},"PeriodicalIF":2.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685519","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}
Pub Date : 2025-07-21DOI: 10.1016/j.spasta.2025.100919
Belchior Miguel , Paula Simões , Rui Gonçalves de Deus , Isabel Natário
The definition of surveillance routes is a very important but complex issue. The Portuguese Navy, in its common form of operation is in charge of the Naval Standard Device, which is distributed throughout the various coastal areas of the country. Enforcement actions can involve very high costs, so a good plan for the sampling designs used are in order, as to maximize the efficiency in obtaining information from the data of the actions developed over the area under consideration. The main objective of this study is to propose sampling design criteria based on geostatistical models, in the context of binary data on presumed maritime infractions in the Portuguese coast, that are advantageous in the optimization of maritime surveillance actions, in terms of efforts employed in their execution, in the maritime area of Portugal’s responsibility. Two sampling design selection criteria are proposed: Maximum Risk Sampling design and Maximum Variance Risk Sampling Design. These are compared to the simple random design by the root mean square error (RMSE). A comparison of the designs at different sample sizes is made and the estimated risk maximization sampling design presents the best RMSE value. The proposed sampling designs may assist in the creation of alternative enforcement Portuguese Navy routes, optimizing the scheduling that maximizes the probability of finding a higher number of presumed fishing perpetrators with less resource efforts.
{"title":"Sampling design for binary geostatistical data, application to inspection actions of fishing activity in Portugal","authors":"Belchior Miguel , Paula Simões , Rui Gonçalves de Deus , Isabel Natário","doi":"10.1016/j.spasta.2025.100919","DOIUrl":"10.1016/j.spasta.2025.100919","url":null,"abstract":"<div><div>The definition of surveillance routes is a very important but complex issue. The Portuguese Navy, in its common form of operation is in charge of the Naval Standard Device, which is distributed throughout the various coastal areas of the country. Enforcement actions can involve very high costs, so a good plan for the sampling designs used are in order, as to maximize the efficiency in obtaining information from the data of the actions developed over the area under consideration. The main objective of this study is to propose sampling design criteria based on geostatistical models, in the context of binary data on presumed maritime infractions in the Portuguese coast, that are advantageous in the optimization of maritime surveillance actions, in terms of efforts employed in their execution, in the maritime area of Portugal’s responsibility. Two sampling design selection criteria are proposed: Maximum Risk Sampling design and Maximum Variance Risk Sampling Design. These are compared to the simple random design by the root mean square error (RMSE). A comparison of the designs at different sample sizes is made and the estimated risk maximization sampling design presents the best RMSE value. The proposed sampling designs may assist in the creation of alternative enforcement Portuguese Navy routes, optimizing the scheduling that maximizes the probability of finding a higher number of presumed fishing perpetrators with less resource efforts.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100919"},"PeriodicalIF":2.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702833","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}
Pub Date : 2025-07-15DOI: 10.1016/j.spasta.2025.100920
Xuankai Ma , Zehua Zhang , Yongze Song
Graph neural networks (GNNs) have been introduced to spatial statistical tasks due to their mechanisms of simulating spatial interactions and processes among geographical neighbours using graph structures. However, previous methods ignore quantifying differences in attributes among adjacent spatial characteristics. Considering this spatial characteristic by fitting the spatial statistic trinity (SST) framework may help improve models’ accuracy and robustness. Thus, we introduce the geographically informed graph neural network (GIGNN) by considering the additional geospatial feature: closer geographical entities may interact less when spatial disparities are captured. When setting up the model, GIGNN leverages differences of attributes by spatial stratified heterogeneity, quantifies connections between geographical entities, and inherits k-order neighbour attribute aggregation and message-passing mechanisms from GNNs. GIGNN is applied to an urbanization analysis study in the Greater Perth Area, Australia, showing higher accuracy than the existing machine learning models and other GNNs in simulation and prediction accuracy. GIGNN achieved an accuracy of 84.1% for simulation and an accuracy of 81% for prediction. Incorporating spatial characteristics into GNNs enhances simulation and prediction accuracy in geoscientific applications, highlighting the importance of spatially aware models in solving complex problems by capturing geographical data dependencies.
{"title":"Geographically informed graph neural networks","authors":"Xuankai Ma , Zehua Zhang , Yongze Song","doi":"10.1016/j.spasta.2025.100920","DOIUrl":"10.1016/j.spasta.2025.100920","url":null,"abstract":"<div><div>Graph neural networks (GNNs) have been introduced to spatial statistical tasks due to their mechanisms of simulating spatial interactions and processes among geographical neighbours using graph structures. However, previous methods ignore quantifying differences in attributes among adjacent spatial characteristics. Considering this spatial characteristic by fitting the spatial statistic trinity (SST) framework may help improve models’ accuracy and robustness. Thus, we introduce the geographically informed graph neural network (GIGNN) by considering the additional geospatial feature: closer geographical entities may interact less when spatial disparities are captured. When setting up the model, GIGNN leverages differences of attributes by spatial stratified heterogeneity, quantifies connections between geographical entities, and inherits k-order neighbour attribute aggregation and message-passing mechanisms from GNNs. GIGNN is applied to an urbanization analysis study in the Greater Perth Area, Australia, showing higher accuracy than the existing machine learning models and other GNNs in simulation and prediction accuracy. GIGNN achieved an accuracy of 84.1% for simulation and an accuracy of 81% for prediction. Incorporating spatial characteristics into GNNs enhances simulation and prediction accuracy in geoscientific applications, highlighting the importance of spatially aware models in solving complex problems by capturing geographical data dependencies.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100920"},"PeriodicalIF":2.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657301","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}
Pub Date : 2025-07-15DOI: 10.1016/j.spasta.2025.100912
Simone Panzeri , Aldo Clemente , Eleonora Arnone , Jorge Mateu , Laura M. Sangalli
Nowadays, a vast amount of georeferenced data pertains to human and natural activities occurring in complex network-constrained regions, such as road or river networks. In this article, our research focuses on spatio-temporal point patterns evolving over time on linear networks, which we model as inhomogeneous Poisson point processes. Within this framework, we propose an innovative nonparametric method for intensity estimation that leverages penalized maximum likelihood with roughness penalties based on differential operators applied across space and time. We provide an efficient implementation of the proposed method, relying on advanced computational and numerical techniques that involve finite element discretizations on linear networks. We validate the method through simulation studies conducted across various scenarios, evaluating its performance compared to state-of-the-art competitors. Finally, we illustrate the method through an application to road accident data recorded in the municipality of Bergamo, Italy, during the years 2017–2019.
{"title":"Spatio-temporal intensity estimation for inhomogeneous Poisson point processes on linear networks: A roughness penalty method","authors":"Simone Panzeri , Aldo Clemente , Eleonora Arnone , Jorge Mateu , Laura M. Sangalli","doi":"10.1016/j.spasta.2025.100912","DOIUrl":"10.1016/j.spasta.2025.100912","url":null,"abstract":"<div><div>Nowadays, a vast amount of georeferenced data pertains to human and natural activities occurring in complex network-constrained regions, such as road or river networks. In this article, our research focuses on spatio-temporal point patterns evolving over time on linear networks, which we model as inhomogeneous Poisson point processes. Within this framework, we propose an innovative nonparametric method for intensity estimation that leverages penalized maximum likelihood with roughness penalties based on differential operators applied across space and time. We provide an efficient implementation of the proposed method, relying on advanced computational and numerical techniques that involve finite element discretizations on linear networks. We validate the method through simulation studies conducted across various scenarios, evaluating its performance compared to state-of-the-art competitors. Finally, we illustrate the method through an application to road accident data recorded in the municipality of Bergamo, Italy, during the years 2017–2019.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100912"},"PeriodicalIF":2.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721668","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}
Pub Date : 2025-07-14DOI: 10.1016/j.spasta.2025.100916
Lorenzo Dell’Oro , Carlo Gaetan
Extreme value analysis is an essential methodology in the study of rare and extreme events, which hold significant interest in various fields, particularly in the context of environmental sciences. Models that employ the exceedances of values above suitably selected high thresholds possess the advantage of capturing the “sub-asymptotic” dependence of data. This paper presents an extension of spatial random scale mixture models to the spatio-temporal domain. A comprehensive framework for characterizing the dependence structure of extreme events across both dimensions is provided. Indeed, the model is capable of distinguishing between asymptotic dependence and independence, both in space and time, through the use of parametric inference. The high complexity of the likelihood function for the proposed model necessitates a simulation approach based on neural networks for parameter estimation, which leverages summaries of the sub-asymptotic dependence present in the data. The effectiveness of the model in assessing the limiting dependence structure of spatio-temporal processes is demonstrated through both simulation studies and an application to rainfall datasets.
{"title":"Flexible space–time models for extreme data","authors":"Lorenzo Dell’Oro , Carlo Gaetan","doi":"10.1016/j.spasta.2025.100916","DOIUrl":"10.1016/j.spasta.2025.100916","url":null,"abstract":"<div><div>Extreme value analysis is an essential methodology in the study of rare and extreme events, which hold significant interest in various fields, particularly in the context of environmental sciences. Models that employ the exceedances of values above suitably selected high thresholds possess the advantage of capturing the “sub-asymptotic” dependence of data. This paper presents an extension of spatial random scale mixture models to the spatio-temporal domain. A comprehensive framework for characterizing the dependence structure of extreme events across both dimensions is provided. Indeed, the model is capable of distinguishing between asymptotic dependence and independence, both in space and time, through the use of parametric inference. The high complexity of the likelihood function for the proposed model necessitates a simulation approach based on neural networks for parameter estimation, which leverages summaries of the sub-asymptotic dependence present in the data. The effectiveness of the model in assessing the limiting dependence structure of spatio-temporal processes is demonstrated through both simulation studies and an application to rainfall datasets.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100916"},"PeriodicalIF":2.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631897","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}
Pub Date : 2025-07-08DOI: 10.1016/j.spasta.2025.100915
D. Posa
In this paper a generalization of some families of correlation functions has been proposed; in particular, a generalization of the rational correlation family, as well as a generalization of a subclass of Matérn family have given, together with some relevant properties involving the two classes. Moreover, an extension of the subclass of Matérn family for the two-dimensional and three-dimensional Euclidean spaces has been provided; in addition, the importance of the proposed models for analysing temporal, spatial and, more generally, spatio-temporal data has been underlined, since the same models can be utilized to construct separable as well as non separable correlation functions. It will be shown that these new classes of models are flexible enough to describe both positive and negative correlation structures. On the other hand, with respect to the classical negative correlation models, the proposed families present some features which cannot be found in the same classical negative correlation functions: these relevant properties allow to get new flexible models, which can be helpful for practitioners to accommodate further case studies, as will be shown through some applications.
{"title":"Some peculiar families of correlation functions","authors":"D. Posa","doi":"10.1016/j.spasta.2025.100915","DOIUrl":"10.1016/j.spasta.2025.100915","url":null,"abstract":"<div><div>In this paper a generalization of some families of correlation functions has been proposed; in particular, a generalization of the rational correlation family, as well as a generalization of a subclass of Matérn family have given, together with some relevant properties involving the two classes. Moreover, an extension of the subclass of Matérn family for the two-dimensional and three-dimensional Euclidean spaces has been provided; in addition, the importance of the proposed models for analysing temporal, spatial and, more generally, spatio-temporal data has been underlined, since the same models can be utilized to construct separable as well as non separable correlation functions. It will be shown that these new classes of models are flexible enough to describe both positive and negative correlation structures. On the other hand, with respect to the classical negative correlation models, the proposed families present some features which cannot be found in the same classical negative correlation functions: these relevant properties allow to get new flexible models, which can be helpful for practitioners to accommodate further case studies, as will be shown through some applications.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100915"},"PeriodicalIF":2.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657302","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}
Pub Date : 2025-07-07DOI: 10.1016/j.spasta.2025.100918
Joonmyoung Kim , Seonwoo Lee , Taekseon Ryu , Jonghyun Na , Taehyun Yun , Jeongho Lee , Hansuk Kim , Man Jae Kwon , Ho Young Jo , Yongsung Joo
Efficient soil remediation, both economically and environmentally, depends on accurate mapping of contaminant concentrations and boundaries of hotspots (areas with concentrations exceeding a critical threshold) through an effective allocation of limited soil sampling sites. This paper introduces a novel two-step sampling location selection method, referred to as the weighted stepwise spatial sampling (WSSS) method. The WSSS method is specifically designed to provide accurate estimates of contaminant concentrations within hotspots and their boundaries. In the first step, dispersed sampling locations are selected for broad exploration, while in the second step, guided by the digital soil mapping results based on the first-step sampling data, sampling locations are selected to focus on identifying potential hotspots. A simulation study using total petroleum hydrocarbon soil data from South Korea demonstrates the superior accuracy and stability of the WSSS in identifying hotspot boundaries and predicting contaminant concentrations within hotspots, compared to other sampling location selection methods. This performance is achieved through an objective function specifically designed to ensure that the selection of sampling locations in the second step is robust to potential inaccuracies or uncertainties in the initial predictions.
{"title":"A two-step sampling strategy to improve the prediction accuracy of contamination hotspots and identify hotspot boundaries","authors":"Joonmyoung Kim , Seonwoo Lee , Taekseon Ryu , Jonghyun Na , Taehyun Yun , Jeongho Lee , Hansuk Kim , Man Jae Kwon , Ho Young Jo , Yongsung Joo","doi":"10.1016/j.spasta.2025.100918","DOIUrl":"10.1016/j.spasta.2025.100918","url":null,"abstract":"<div><div>Efficient soil remediation, both economically and environmentally, depends on accurate mapping of contaminant concentrations and boundaries of hotspots (areas with concentrations exceeding a critical threshold) through an effective allocation of limited soil sampling sites. This paper introduces a novel two-step sampling location selection method, referred to as the weighted stepwise spatial sampling (WSSS) method. The WSSS method is specifically designed to provide accurate estimates of contaminant concentrations within hotspots and their boundaries. In the first step, dispersed sampling locations are selected for broad exploration, while in the second step, guided by the digital soil mapping results based on the first-step sampling data, sampling locations are selected to focus on identifying potential hotspots. A simulation study using total petroleum hydrocarbon soil data from South Korea demonstrates the superior accuracy and stability of the WSSS in identifying hotspot boundaries and predicting contaminant concentrations within hotspots, compared to other sampling location selection methods. This performance is achieved through an objective function specifically designed to ensure that the selection of sampling locations in the second step is robust to potential inaccuracies or uncertainties in the initial predictions.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100918"},"PeriodicalIF":2.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657303","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}
Pub Date : 2025-07-02DOI: 10.1016/j.spasta.2025.100914
Domenico Cangemi , Pierpaolo D’Urso , Livia De Giovanni , Lorenzo Federico , Vincenzina Vitale
A robust fuzzy clustering model for data with mixed features and spatial constraints is proposed to analyze the turnout and the preferences of the voters at the provincial level in the European elections. The 2024 European elections in Italy were held in June to elect the 76 members of the European Parliament due to Italy. The clustering model accommodates various types of variables or attributes by integrating dissimilarity measures for each one through a weighting approach. This method produces a composite distance (or dissimilarity) metric that captures multiple attribute types. The weights are determined objectively during the optimization process and indicate the importance of each attribute type. The model also incorporates robustness via the introduction of a Noise cluster, and accounts for a spatial component. The application shows consistency of the results both at the level of units’ attributes and at a spatial level.
{"title":"Spatial robust fuzzy clustering of mixed data with electoral study","authors":"Domenico Cangemi , Pierpaolo D’Urso , Livia De Giovanni , Lorenzo Federico , Vincenzina Vitale","doi":"10.1016/j.spasta.2025.100914","DOIUrl":"10.1016/j.spasta.2025.100914","url":null,"abstract":"<div><div>A robust fuzzy clustering model for data with mixed features and spatial constraints is proposed to analyze the turnout and the preferences of the voters at the provincial level in the European elections. The 2024 European elections in Italy were held in June to elect the 76 members of the European Parliament due to Italy. The clustering model accommodates various types of variables or attributes by integrating dissimilarity measures for each one through a weighting approach. This method produces a composite distance (or dissimilarity) metric that captures multiple attribute types. The weights are determined objectively during the optimization process and indicate the importance of each attribute type. The model also incorporates robustness via the introduction of a Noise cluster, and accounts for a spatial component. The application shows consistency of the results both at the level of units’ attributes and at a spatial level.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100914"},"PeriodicalIF":2.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657300","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}
Pub Date : 2025-06-28DOI: 10.1016/j.spasta.2025.100913
François d’Alayer, Edith Gabriel, Samuel Soubeyrand
Plant disease surveillance is essential for the management of disease outbreaks that pose significant threats to agricultural sustainability. In this study, we present a novel sequential point process model designed for disease surveillance. The model incorporates self-interaction mechanisms to account for the influence of the process’ history. To analyze the dynamics of the model, we propose new sequential summary statistics that extend traditional point process methods to scenarios where sequential interactions are critical. This model serves a dual purpose: it is employed both to propose novel and efficient sampling designs, and to characterize existing sampling schemes, implemented in real-world situations, through parameter inference.
{"title":"A marked sequential point process for disease surveillance: Modeling and optimization","authors":"François d’Alayer, Edith Gabriel, Samuel Soubeyrand","doi":"10.1016/j.spasta.2025.100913","DOIUrl":"10.1016/j.spasta.2025.100913","url":null,"abstract":"<div><div>Plant disease surveillance is essential for the management of disease outbreaks that pose significant threats to agricultural sustainability. In this study, we present a novel sequential point process model designed for disease surveillance. The model incorporates self-interaction mechanisms to account for the influence of the process’ history. To analyze the dynamics of the model, we propose new sequential summary statistics that extend traditional point process methods to scenarios where sequential interactions are critical. This model serves a dual purpose: it is employed both to propose novel and efficient sampling designs, and to characterize existing sampling schemes, implemented in real-world situations, through parameter inference.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100913"},"PeriodicalIF":2.1,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562979","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}
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