Spatial Statistics最新文献

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

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

Variational autoencoded multivariate spatial Fay–Herriot models 变分自编码多元空间费-赫里奥特模型

IF 2.5 2区数学

Spatial Statistics Pub Date : 2025-09-10 DOI: 10.1016/j.spasta.2025.100929

Zhenhua Wang , Paul A. Parker , Scott H. Holan

{"title":"Variational autoencoded multivariate spatial Fay–Herriot models","authors":"Zhenhua Wang , Paul A. Parker , Scott H. Holan","doi":"10.1016/j.spasta.2025.100929","DOIUrl":"10.1016/j.spasta.2025.100929","url":null,"abstract":"<div><div>Small area estimation models are essential for estimating population characteristics in regions with limited sample sizes, thereby supporting policy decisions, demographic studies, and resource allocation, among other use cases. The spatial Fay–Herriot model is one such approach that incorporates spatial dependence to improve estimation by borrowing strength from neighboring regions. However, this approach often requires substantial computational resources, limiting its scalability for high-dimensional datasets, especially when considering multiple (multivariate) responses. This paper proposes two methods that integrate the multivariate spatial Fay–Herriot model with spatial random effects, learned through variational autoencoders, to efficiently leverage spatial structure. Importantly, after training the variational autoencoder to represent spatial dependence for a given set of geographies, it may be used again in future modeling efforts, without the need for retraining. Additionally, the use of the variational autoencoder to represent spatial dependence results in extreme improvements in computational efficiency, even for massive datasets. We demonstrate the effectiveness of our approach using 5-year period estimates from the American Community Survey over all census tracts in California.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"70 ","pages":"Article 100929"},"PeriodicalIF":2.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050405","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

Regime changes and spatial dependence in the 2020 US presidential election polls 2020年美国总统大选民意调查中的政权更迭和空间依赖

IF 2.5 2区数学

Spatial Statistics Pub Date : 2025-08-26 DOI: 10.1016/j.spasta.2025.100927

Giampiero M. Gallo , Demetrio Lacava , Edoardo Otranto

{"title":"Regime changes and spatial dependence in the 2020 US presidential election polls","authors":"Giampiero M. Gallo , Demetrio Lacava , Edoardo Otranto","doi":"10.1016/j.spasta.2025.100927","DOIUrl":"10.1016/j.spasta.2025.100927","url":null,"abstract":"<div><div>This paper introduces a novel two-stage modeling framework that combines Markov Switching (MS) models with an autoregressive model augmented by spatial effects to analyze the dynamics and spatial interdependence of Biden’s polling percentages during the 2020 electoral campaign. In the first stage, we employ MS models to segment each state’s daily polling time series into distinct regimes — interpreted as phases of decline, stability, and growth. This segmentation captures abrupt changes and local trends in public opinion, enabling us to link regime shifts with key political events such as debates, party conventions, and milestone campaign achievements. The inherent nonlinearity of polling data would otherwise be lost by first differencing. By removing the regime-specific components, we generate stationary residuals modeled using an Autoregressive model with exogenous variables (ARX) that incorporates political spatial interactions through two complementary effects. The spillover effect captures lagged influences arising from politically influential states, while the contagion effect reflects the contemporaneous impact of neighboring states. A recursive algorithm based on partial correlations is implemented to select the most relevant spillover sources for each state. Empirical results, based on daily data from 13 swing states, reveal robust evidence of persistent regime structures and marked spatial dependencies. While contagion effects are uniformly significant across states, spillover dynamics exhibit considerable heterogeneity in both magnitude and direction. This integrated modeling approach enhances our understanding of the complex, nonlinear temporal evolution of polling trends and the spatial diffusion of political opinions that underpinned the 2020 electoral outcome.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100927"},"PeriodicalIF":2.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909070","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

GMM inference for the spatial autoregressive kink model with an unknown threshold 未知阈值空间自回归扭结模型的GMM推理

IF 2.5 2区数学

Spatial Statistics Pub Date : 2025-08-22 DOI: 10.1016/j.spasta.2025.100926

Wentao Wang , Dengkui Li

引用次数: 0

Conformal novelty detection for replicate point patterns with FDR or FWER control 用FDR或FWER控制复制点模式的保形新颖性检测

IF 2.5 2区数学

Spatial Statistics Pub Date : 2025-08-05 DOI: 10.1016/j.spasta.2025.100924

Christophe A.N. Biscio , Adrien Mazoyer , Martin V. Vejling

{"title":"Conformal novelty detection for replicate point patterns with FDR or FWER control","authors":"Christophe A.N. Biscio , Adrien Mazoyer , Martin V. Vejling","doi":"10.1016/j.spasta.2025.100924","DOIUrl":"10.1016/j.spasta.2025.100924","url":null,"abstract":"<div><div>Monte Carlo tests are widely used for computing valid <span><math><mi>p</mi></math></span>-values without requiring known distributions of test statistics. When performing multiple Monte Carlo tests, it is essential to maintain control of the type I error. Some techniques for multiplicity control pose requirements on the joint distribution of the <span><math><mi>p</mi></math></span>-values, for instance independence, which can be computationally intensive to achieve, as it requires simulating disjoint null samples for each test. We refer to this as naïve multiple Monte Carlo testing. We highlight in this work that multiple Monte Carlo testing is an instance of conformal novelty detection. Leveraging this insight enables a more efficient multiple Monte Carlo testing procedure, avoiding excessive simulations by using a single null sample for all the tests, while still ensuring exact control over the false discovery rate or the family-wise error rate. We call this approach conformal multiple Monte Carlo testing. The performance is investigated in the context of global envelope tests for point pattern data through a simulation study and an application to a sweat gland data set. Results reveal that with a fixed simulation budget, our proposed method yields substantial improvements in power of the testing procedure as compared to the naïve multiple Monte Carlo testing procedure.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100924"},"PeriodicalIF":2.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860831","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

Spatiotemporal dynamics of COVID-19 in Wuhan based on community notifications 基于社区通报的武汉市新冠肺炎疫情时空动态分析

IF 2.5 2区数学

Spatial Statistics Pub Date : 2025-07-29 DOI: 10.1016/j.spasta.2025.100925

Gang Xu , Qirui Zhang , Xinlei Xu , Yajie Zhang , Yansheng Li

{"title":"Spatiotemporal dynamics of COVID-19 in Wuhan based on community notifications","authors":"Gang Xu , Qirui Zhang , Xinlei Xu , Yajie Zhang , Yansheng Li","doi":"10.1016/j.spasta.2025.100925","DOIUrl":"10.1016/j.spasta.2025.100925","url":null,"abstract":"<div><div>Understanding the fine-scale spatial dynamics of infectious disease outbreaks is essential for effective urban epidemic response. This study leverages a novel dataset of over 2700 community-level epidemic notifications, shared publicly in residential areas and through social media during the early COVID-19 outbreak in Wuhan, China, to map the intra-urban spread of the virus from February 2 to March 4, 2020. After manually structuring and geocoding these notifications, we constructed a high-resolution spatiotemporal dataset of 13,346 confirmed cases across 1532 neighborhoods. Using spatial statistical techniques, we identified the evolution of spatial clustering, directional shifts in epidemic centers, and seven statistically significant spatio-temporal clusters with relative risks ranging from 1.21 to 12.48. Our results reveal the critical role of urban morphology, population density, and built environment characteristics in shaping transmission dynamics. Notably, Qingshan District emerged as a persistent hotspot due to its open neighborhood design and delayed compliance with containment measures. This research underscores the value of Volunteered Geographic Information (VGI) for early, fine-scale epidemic monitoring and demonstrates its utility as a complement to official surveillance systems in public emergencies.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100925"},"PeriodicalIF":2.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771829","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

Physics-driven dynamic interpolation with application to pollution satellite images 物理驱动的动态插值及其在污染卫星图像中的应用

IF 2.5 2区数学

Spatial Statistics Pub Date : 2025-07-28 DOI: 10.1016/j.spasta.2025.100923

Won Chang , Youngdeok Hwang , Hang J. Kim

引用次数: 0