Pub Date : 2025-11-29DOI: 10.1016/j.spasta.2025.100947
S. Emili, F. Galli
In this paper, we develop a system of simultaneous stochastic frontier models with inefficiency determinants, spatio-temporal effects and correlated inefficiency as well as correlated random errors among frontiers. The dependence among the errors of the different equations can stem from either shocks external to the system, interrelated inefficiency mechanisms, or a combination of both. Estimation is performed using a copula-based quasi-maximum likelihood approach. Simulation results confirm the good finite sample properties of the proposed estimator. To demonstrate the effectiveness of the proposed model and estimation technique in empirical settings, we analyse the key role of some sustainability-related factors in determining the efficiency level of Italian cultural and creative sectors.
{"title":"A simultaneous system of dynamic spatial stochastic frontier models with dependent error components and inefficiency determinants","authors":"S. Emili, F. Galli","doi":"10.1016/j.spasta.2025.100947","DOIUrl":"10.1016/j.spasta.2025.100947","url":null,"abstract":"<div><div>In this paper, we develop a system of simultaneous stochastic frontier models with inefficiency determinants, spatio-temporal effects and correlated inefficiency as well as correlated random errors among frontiers. The dependence among the errors of the different equations can stem from either shocks external to the system, interrelated inefficiency mechanisms, or a combination of both. Estimation is performed using a copula-based quasi-maximum likelihood approach. Simulation results confirm the good finite sample properties of the proposed estimator. To demonstrate the effectiveness of the proposed model and estimation technique in empirical settings, we analyse the key role of some sustainability-related factors in determining the efficiency level of Italian cultural and creative sectors.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"71 ","pages":"Article 100947"},"PeriodicalIF":2.5,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694042","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 : 2025-11-21DOI: 10.1016/j.spasta.2025.100946
Karina Lilleborge , Sara Martino , Geir-Arne Fuglstad , Finn Lindgren , Rikke Ingebrigtsen
Metric graphs are useful tools for describing spatial domains like road and river networks, where spatial dependence act along the network. We take advantage of recent developments for such Gaussian Random Fields (GRFs), and consider joint spatial modeling of observations with different spatial supports. Motivated by an application to traffic state modeling in Trondheim, Norway, we consider line-referenced data, which can be described by an integral of the GRF along a line segment on the metric graph, and point-referenced data. Through a simulation study inspired by the application, we investigate the number of replicates that are needed to estimate parameters and to predict unobserved locations. The former is assessed using bias and variability, and the latter is assessed through root mean square error (RMSE), continuous rank probability scores (CRPSs), and coverage. Joint modeling is contrasted with a simplified approach that treat line-referenced observations as point-referenced observations. The results suggest joint modeling leads to strong improvements. The application to Trondheim, Norway, combines point-referenced induction loop data and line-referenced public transportation data. To ensure positive speeds, we use a non-linear link function, which requires integrals of non-linear combinations of the linear predictor. This is made computationally feasible by a combination of the R packages inlabru and MetricGraph, and new code for processing geographical line data to work with existing graph representations and fmesher methods for dealing with line support in inlabru on objects from MetricGraph. We fit the model to two datasets where we expect different spatial dependency and compare the results.
{"title":"Joint modeling of line and point data on metric graphs","authors":"Karina Lilleborge , Sara Martino , Geir-Arne Fuglstad , Finn Lindgren , Rikke Ingebrigtsen","doi":"10.1016/j.spasta.2025.100946","DOIUrl":"10.1016/j.spasta.2025.100946","url":null,"abstract":"<div><div>Metric graphs are useful tools for describing spatial domains like road and river networks, where spatial dependence act along the network. We take advantage of recent developments for such Gaussian Random Fields (GRFs), and consider joint spatial modeling of observations with different spatial supports. Motivated by an application to traffic state modeling in Trondheim, Norway, we consider line-referenced data, which can be described by an integral of the GRF along a line segment on the metric graph, and point-referenced data. Through a simulation study inspired by the application, we investigate the number of replicates that are needed to estimate parameters and to predict unobserved locations. The former is assessed using bias and variability, and the latter is assessed through root mean square error (RMSE), continuous rank probability scores (CRPSs), and coverage. Joint modeling is contrasted with a simplified approach that treat line-referenced observations as point-referenced observations. The results suggest joint modeling leads to strong improvements. The application to Trondheim, Norway, combines point-referenced induction loop data and line-referenced public transportation data. To ensure positive speeds, we use a non-linear link function, which requires integrals of non-linear combinations of the linear predictor. This is made computationally feasible by a combination of the R packages <span>inlabru</span> and <span>MetricGraph</span>, and new code for processing geographical line data to work with existing graph representations and <span>fmesher</span> methods for dealing with line support in <span>inlabru</span> on objects from <span>MetricGraph</span>. We fit the model to two datasets where we expect different spatial dependency and compare the results.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"71 ","pages":"Article 100946"},"PeriodicalIF":2.5,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624198","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 : 2025-11-21DOI: 10.1016/j.spasta.2025.100945
Qing Zhao , Yunyi Shen
Density dependence occurs at the individual level and thus is greatly influenced by spatial local heterogeneity in habitat conditions. However, density dependence is often evaluated at the population level, leading to difficulties or even controversies in detecting such a process. Bayesian individual-based models such as spatial capture-recapture (SCR) models provide opportunities to study density dependence at the individual level, but such an approach remains to be developed and evaluated. In this study, we developed a SCR model that links habitat use to apparent survival and recruitment through density dependent processes at the individual level. Using simulations, we found that the model can properly inform habitat use, but tends to underestimate the effect of density dependence on apparent survival and recruitment. The reason for such underestimations is likely due to the difficulties of the current model in identifying the locations of unobserved individuals without using environmental covariates to inform these locations. How to accurately estimate the locations of unobserved individuals, and thus density dependence, remains a challenging topic in spatial statistics and statistical ecology.
{"title":"Explicit modeling of density dependence in spatial capture-recapture models","authors":"Qing Zhao , Yunyi Shen","doi":"10.1016/j.spasta.2025.100945","DOIUrl":"10.1016/j.spasta.2025.100945","url":null,"abstract":"<div><div>Density dependence occurs at the individual level and thus is greatly influenced by spatial local heterogeneity in habitat conditions. However, density dependence is often evaluated at the population level, leading to difficulties or even controversies in detecting such a process. Bayesian individual-based models such as spatial capture-recapture (SCR) models provide opportunities to study density dependence at the individual level, but such an approach remains to be developed and evaluated. In this study, we developed a SCR model that links habitat use to apparent survival and recruitment through density dependent processes at the individual level. Using simulations, we found that the model can properly inform habitat use, but tends to underestimate the effect of density dependence on apparent survival and recruitment. The reason for such underestimations is likely due to the difficulties of the current model in identifying the locations of unobserved individuals without using environmental covariates to inform these locations. How to accurately estimate the locations of unobserved individuals, and thus density dependence, remains a challenging topic in spatial statistics and statistical ecology.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"71 ","pages":"Article 100945"},"PeriodicalIF":2.5,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580233","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 : 2025-11-20DOI: 10.1016/j.spasta.2025.100944
Garazi Retegui, Jaione Etxeberria, María Dolores Ugarte
Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly interested in methods for forecasting cancer incidence. However, in many countries there is an additional difficulty in projecting cancer incidence as regional registries are usually not established in the same year and therefore cancer incidence data series between different regions of a country are not harmonized over time. This study addresses the challenge of forecasting cancer incidence with incomplete data at both regional and national levels. To achieve this, we propose the use of multivariate spatio-temporal shared component models that jointly model mortality data and available cancer incidence data. We evaluate the performance of these multivariate models using lung cancer incidence data and the corresponding number of deaths reported in England for the period 2001–2019. Model performance was assessed using different predictive measures to select the best model.
{"title":"Multivariate spatio-temporal modelling for completing cancer registries and forecasting incidence","authors":"Garazi Retegui, Jaione Etxeberria, María Dolores Ugarte","doi":"10.1016/j.spasta.2025.100944","DOIUrl":"10.1016/j.spasta.2025.100944","url":null,"abstract":"<div><div>Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly interested in methods for forecasting cancer incidence. However, in many countries there is an additional difficulty in projecting cancer incidence as regional registries are usually not established in the same year and therefore cancer incidence data series between different regions of a country are not harmonized over time. This study addresses the challenge of forecasting cancer incidence with incomplete data at both regional and national levels. To achieve this, we propose the use of multivariate spatio-temporal shared component models that jointly model mortality data and available cancer incidence data. We evaluate the performance of these multivariate models using lung cancer incidence data and the corresponding number of deaths reported in England for the period 2001–2019. Model performance was assessed using different predictive measures to select the best model.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"71 ","pages":"Article 100944"},"PeriodicalIF":2.5,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624199","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 : 2025-11-15DOI: 10.1016/j.spasta.2025.100940
Alba Bernabeu , Claudio Fronterrè , Jorge Mateu
Spatio-temporal point pattern data often exhibit clustering, which may arise either from self-exciting mechanisms or from latent environmental heterogeneity. Hawkes processes and log-Gaussian Cox processes (LGCPs) represent two widely used but fundamentally different modelling approaches for capturing such patterns. While Hawkes processes assume event-driven triggering, LGCPs model clustering through a latent Gaussian random field acting on the intensity function. In practice, model selection between these alternatives is rarely conducted rigorously, and second-order characteristics are often insufficient to discriminate between them. We present a simulation-based comparative study that systematically evaluates estimation, second-order structure, and predictive performance under model misspecification. In particular, we assess the ability of each model to reproduce the dynamics of data generated under the competing framework. We also analyse real crime data to illustrate how inference and prediction are affected by model choice. Our results underscore the interpretive consequences of model misspecification and highlight key diagnostic limitations when disentangling clustering mechanisms in spatio-temporal processes.
{"title":"Misspecification issues between competitive spatio-temporal cluster point processes","authors":"Alba Bernabeu , Claudio Fronterrè , Jorge Mateu","doi":"10.1016/j.spasta.2025.100940","DOIUrl":"10.1016/j.spasta.2025.100940","url":null,"abstract":"<div><div>Spatio-temporal point pattern data often exhibit clustering, which may arise either from self-exciting mechanisms or from latent environmental heterogeneity. Hawkes processes and log-Gaussian Cox processes (LGCPs) represent two widely used but fundamentally different modelling approaches for capturing such patterns. While Hawkes processes assume event-driven triggering, LGCPs model clustering through a latent Gaussian random field acting on the intensity function. In practice, model selection between these alternatives is rarely conducted rigorously, and second-order characteristics are often insufficient to discriminate between them. We present a simulation-based comparative study that systematically evaluates estimation, second-order structure, and predictive performance under model misspecification. In particular, we assess the ability of each model to reproduce the dynamics of data generated under the competing framework. We also analyse real crime data to illustrate how inference and prediction are affected by model choice. Our results underscore the interpretive consequences of model misspecification and highlight key diagnostic limitations when disentangling clustering mechanisms in spatio-temporal processes.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"71 ","pages":"Article 100940"},"PeriodicalIF":2.5,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546713","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 : 2025-11-12DOI: 10.1016/j.spasta.2025.100942
Yining Han , Xiaobin Chen , Xingxing Huang , Zhongyin Liu
The integration of multi-source data often results in the occurrence of local over-densities at point locations. These over-densities can bias the estimation of spatial statistical parameters, such as mean and variance, compromise the quality of variogram fitting, and degrade the accuracy of interpolation results. However, a standardized approach for identifying local over-densities in spatial datasets is currently lacking. In this paper, we propose three parameters—self-sparsity, mutual sparsity, and small-distance variability—and construct a three-parameter comprehensive cross-plot to facilitate the visual identification of local over-densities within spatial data points, thereby enabling further processing. Using both synthetic datasets generated via stochastic process simulations and real-world datasets, we demonstrate that the proposed three-parameter comprehensive cross-plot, based on self-sparsity, mutual sparsity, and small-distance variability, effectively identifies local over-densities in spatial datasets. Furthermore, by appropriately processing these over-densities, the accuracy of spatial statistical parameter estimation can be enhanced, a more reliable theoretical variogram model can be established, and both spatial statistical analysis and interpolation results can ultimately be improved.
{"title":"A visual method for identifying local over-densities in spatial data","authors":"Yining Han , Xiaobin Chen , Xingxing Huang , Zhongyin Liu","doi":"10.1016/j.spasta.2025.100942","DOIUrl":"10.1016/j.spasta.2025.100942","url":null,"abstract":"<div><div>The integration of multi-source data often results in the occurrence of local over-densities at point locations. These over-densities can bias the estimation of spatial statistical parameters, such as mean and variance, compromise the quality of variogram fitting, and degrade the accuracy of interpolation results. However, a standardized approach for identifying local over-densities in spatial datasets is currently lacking. In this paper, we propose three parameters—self-sparsity, mutual sparsity, and small-distance variability—and construct a three-parameter comprehensive cross-plot to facilitate the visual identification of local over-densities within spatial data points, thereby enabling further processing. Using both synthetic datasets generated via stochastic process simulations and real-world datasets, we demonstrate that the proposed three-parameter comprehensive cross-plot, based on self-sparsity, mutual sparsity, and small-distance variability, effectively identifies local over-densities in spatial datasets. Furthermore, by appropriately processing these over-densities, the accuracy of spatial statistical parameter estimation can be enhanced, a more reliable theoretical variogram model can be established, and both spatial statistical analysis and interpolation results can ultimately be improved.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"71 ","pages":"Article 100942"},"PeriodicalIF":2.5,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546712","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 : 2025-11-07DOI: 10.1016/j.spasta.2025.100943
Christian Caamaño-Carrillo , Moreno Bevilacqua , Diego I. Gallardo
We propose a novel spatial survival model based on a Weibull random field, designed to overcome the limitations of existing copula-based approaches; particularly those relying on the Farlie–Gumbel–Morgenstern (FGM) copula. Although the FGM model only captures weak dependence and enforces reflection symmetry and a forced nugget effect, our model allows for stronger spatial dependence, reflection asymmetry, and mean-square continuity. These properties provide a more flexible and realistic framework for analyzing spatially correlated time-to-event data. The model unifies the proportional hazards (PH), the accelerated failure time (AFT), and the mean parameterizations of the Weibull distribution, allowing a clear interpretation of the effects of the covariates. Due to the analytical intractability of the full likelihood, parameter estimation is performed using a weighted pairwise composite likelihood method based on nearest neighbors. This method offers computational efficiency and robustness to right-censored data. Simulation studies confirm the effectiveness of the proposed model, and an application to real housing data illustrates its practical value.
{"title":"Spatial survival models based on Weibull random fields","authors":"Christian Caamaño-Carrillo , Moreno Bevilacqua , Diego I. Gallardo","doi":"10.1016/j.spasta.2025.100943","DOIUrl":"10.1016/j.spasta.2025.100943","url":null,"abstract":"<div><div>We propose a novel spatial survival model based on a Weibull random field, designed to overcome the limitations of existing copula-based approaches; particularly those relying on the Farlie–Gumbel–Morgenstern (FGM) copula. Although the FGM model only captures weak dependence and enforces reflection symmetry and a forced nugget effect, our model allows for stronger spatial dependence, reflection asymmetry, and mean-square continuity. These properties provide a more flexible and realistic framework for analyzing spatially correlated time-to-event data. The model unifies the proportional hazards (PH), the accelerated failure time (AFT), and the mean parameterizations of the Weibull distribution, allowing a clear interpretation of the effects of the covariates. Due to the analytical intractability of the full likelihood, parameter estimation is performed using a weighted pairwise composite likelihood method based on nearest neighbors. This method offers computational efficiency and robustness to right-censored data. Simulation studies confirm the effectiveness of the proposed model, and an application to real housing data illustrates its practical value.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100943"},"PeriodicalIF":2.5,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528796","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}
Small area estimation (SAE) addresses the estimation of parameters for population subsets when the sample itself is too small to produce reliable direct estimates. The standard method, empirical best linear unbiased prediction, uses a predictor under a linear mixed model that assumes normality of the variable of interest and independence among small areas. However, in practical studies, the distribution of the variable of interest tends to be positively skewed and there exists spatial dependence among the small areas. To address both of these, a previous study had proposed a spatial synthetic (SYNT) predictor that predicts non-sampled values of the variable of interest using its unconditional mean. The SYNT predictor is derived based on a unit-level spatial lognormal mixed model. Herein, we propose spatial empirical best predictor (SEBP) to improve the SYNT predictor by using its conditional mean to predict the non-sampled values of the variable of interest. We perform simulation studies to evaluate the performance of SEBP and compare them with those of the SYNT predictor and other existing methods. Our results reveal that the SEBP performs better in terms of the average relative bias and average relative root mean square error when the spatial correlation among small areas is small, medium or large. In an SAE application on the average monthly household per-capita expenditure for sub-districts in Bogor, Indonesia, the proposed SEBP provides better estimates than other established methods.
{"title":"Spatial empirical best predictor of small area linear parameter for positively skewed outcomes","authors":"Dian Handayani , Khairil Anwar Notodiputro , Asep Saefuddin , I Wayan Mangku , Anang Kurnia","doi":"10.1016/j.spasta.2025.100941","DOIUrl":"10.1016/j.spasta.2025.100941","url":null,"abstract":"<div><div>Small area estimation (SAE) addresses the estimation of parameters for population subsets when the sample itself is too small to produce reliable direct estimates. The standard method, empirical best linear unbiased prediction, uses a predictor under a linear mixed model that assumes normality of the variable of interest and independence among small areas. However, in practical studies, the distribution of the variable of interest tends to be positively skewed and there exists spatial dependence among the small areas. To address both of these, a previous study had proposed a spatial synthetic (SYNT) predictor that predicts non-sampled values of the variable of interest using its unconditional mean. The SYNT predictor is derived based on a unit-level spatial lognormal mixed model. Herein, we propose spatial empirical best predictor (SEBP) to improve the SYNT predictor by using its conditional mean to predict the non-sampled values of the variable of interest. We perform simulation studies to evaluate the performance of SEBP and compare them with those of the SYNT predictor and other existing methods. Our results reveal that the SEBP performs better in terms of the average relative bias and average relative root mean square error when the spatial correlation among small areas is small, medium or large. In an SAE application on the average monthly household per-capita expenditure for sub-districts in Bogor, Indonesia, the proposed SEBP provides better estimates than other established methods.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100941"},"PeriodicalIF":2.5,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528797","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 : 2025-11-01DOI: 10.1016/j.spasta.2025.100939
Anna Gloria Billé , Roberto Benedetti , Paolo Postiglione
Spatial heterogeneity in terms of spatially-varying coefficients is often not properly considered in modeling economic data. This neglect might cause serious problems in the estimation of the parameters of a model specification when group-wise heterogeneity is at work. In this paper we propose a two-step algorithm for the identification of endogenous (data-driven) spatial regimes by using an iterative procedure that is based on weighting functions updated dynamically over time. In the first step, clusters of spatial units (i.e. spatial regimes) are defined using both space and time information. In the second step, a spatial panel data model with random effects is estimated with the spatial regimes identified in the previous step. The additional random effects assumption on the model specification ensures the possibility of controlling also for individual effects as well as group-wise slope coefficients. The proposed method is applied to two real data sets to illustrate our procedure.
{"title":"Dynamic spatial regimes for spatial panel data","authors":"Anna Gloria Billé , Roberto Benedetti , Paolo Postiglione","doi":"10.1016/j.spasta.2025.100939","DOIUrl":"10.1016/j.spasta.2025.100939","url":null,"abstract":"<div><div>Spatial heterogeneity in terms of spatially-varying coefficients is often not properly considered in modeling economic data. This neglect might cause serious problems in the estimation of the parameters of a model specification when group-wise heterogeneity is at work. In this paper we propose a two-step algorithm for the identification of endogenous (data-driven) spatial regimes by using an iterative procedure that is based on weighting functions updated dynamically over time. In the first step, clusters of spatial units (i.e. spatial regimes) are defined using both space and time information. In the second step, a spatial panel data model with random effects is estimated with the spatial regimes identified in the previous step. The additional random effects assumption on the model specification ensures the possibility of controlling also for individual effects as well as group-wise slope coefficients. The proposed method is applied to two real data sets to illustrate our procedure.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100939"},"PeriodicalIF":2.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473635","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 : 2025-10-01DOI: 10.1016/j.spasta.2025.100938
Denis Allard
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 Spatial Statistics 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.
{"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}
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