Spatial Statistics最新文献_第10页

A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models 在多元空间区域模型中减轻空间混杂的简化空间+方法

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-30 DOI: 10.1016/j.spasta.2023.100804

Arantxa Urdangarin , Tomás Goicoa , Thomas Kneib , María Dolores Ugarte

{"title":"A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models","authors":"Arantxa Urdangarin , Tomás Goicoa , Thomas Kneib , María Dolores Ugarte","doi":"10.1016/j.spasta.2023.100804","DOIUrl":"10.1016/j.spasta.2023.100804","url":null,"abstract":"<div><p>Spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a simplified version of the spatial+ approach that involves dividing the covariate into two components. One component captures large-scale spatial dependence, while the other accounts for short-scale dependence. This approach eliminates the need to separately fit spatial models for the covariates. We apply this method to analyse two forms of crimes against women, namely rapes and dowry deaths, in Uttar Pradesh, India, exploring their relationship with socio-demographic covariates. To evaluate the performance of the new approach, we conduct extensive simulation studies under different spatial confounding scenarios. The results demonstrate that the proposed method provides reliable estimates of fixed effects and posterior correlations between different responses.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"59 ","pages":"Article 100804"},"PeriodicalIF":2.3,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675323000799/pdfft?md5=7d9a771b85f53f6ac2e328abaa8d8a37&pid=1-s2.0-S2211675323000799-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139072106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data 利用大空间数据的空间索引为美国毗连地区的湖泊电导率建模

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-29 DOI: 10.1016/j.spasta.2023.100808

Michael Dumelle , Jay M. Ver Hoef , Amalia Handler , Ryan A. Hill , Matt Higham , Anthony R. Olsen

{"title":"Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data","authors":"Michael Dumelle , Jay M. Ver Hoef , Amalia Handler , Ryan A. Hill , Matt Higham , Anthony R. Olsen","doi":"10.1016/j.spasta.2023.100808","DOIUrl":"10.1016/j.spasta.2023.100808","url":null,"abstract":"<div><p>Conductivity is an important indicator of the health of aquatic ecosystems. We model large amounts of lake conductivity data collected as part of the United States Environmental Protection Agency’s National Lakes Assessment using spatial indexing, a flexible and efficient approach to fitting spatial statistical models to big data sets. Spatial indexing is capable of accommodating various spatial covariance structures as well as features like random effects, geometric anisotropy, partition factors, and non-Euclidean topologies. We use spatial indexing to compare lake conductivity models and show that calcium oxide rock content, crop production, human development, precipitation, and temperature are strongly related to lake conductivity. We use this model to predict lake conductivity at hundreds of thousands of lakes distributed throughout the contiguous United States. We find that lake conductivity models fit using spatial indexing are nearly identical to lake conductivity models fit using traditional methods but are nearly 50 times faster (sample size 3,311). Spatial indexing is readily available in the <span>spmodel</span> <strong>R</strong> package.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"59 ","pages":"Article 100808"},"PeriodicalIF":2.3,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675323000830/pdfft?md5=63f37c018007a30ce9172196e4cc8bc1&pid=1-s2.0-S2211675323000830-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A generalized additive model (GAM) approach to principal component analysis of geographic data 地理数据主成分分析的广义加法模型（GAM）方法

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-29 DOI: 10.1016/j.spasta.2023.100806

Francisco de Asís López , Celestino Ordóñez , Javier Roca-Pardiñas

{"title":"A generalized additive model (GAM) approach to principal component analysis of geographic data","authors":"Francisco de Asís López , Celestino Ordóñez , Javier Roca-Pardiñas","doi":"10.1016/j.spasta.2023.100806","DOIUrl":"10.1016/j.spasta.2023.100806","url":null,"abstract":"<div><p>Geographically Weighted Principal Component Analysis (GWPCA) is an extension of classical PCA to deal with the spatial heterogeneity of geographical data. This heterogeneity results in a variance–covariance matrix that is not stationary but changes with the geographical location. Despite its usefulness, this method presents some unsolved issues, such as finding an appropriate bandwidth (size of the vicinity) as a function of the retained components. In this work, we address the problem of calculating principal components for geographical data from a new perspective that overcomes this problem. Specifically we propose a scale-location model which uses generalized additive models (GAMs) to calculate means for each variable and a correlation matrix that relates the variables, both depending on the spatial location. It should be noticed that although we deal with geographic data, our methodology cannot be considered strictly spatial since we assume that there is not a spatial correlation structure in the error term.</p><p>Our approach does not require to calculate an optimal bandwidth as a function of the number of components retained in the analysis. Instead, the covariance matrix is estimated using smooth functions adapted to the data, so the smoothness can be different for each element of the matrix. The proposed methodology was tested with simulated data and compared with GWPCA. The result was a better representation of the data structure in the proposed method. Finally, we show the possibilities of our method in a problem with real data regarding air pollution and socioeconomic factors.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"59 ","pages":"Article 100806"},"PeriodicalIF":2.3,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675323000817/pdfft?md5=e258c8c408f56930e791b8a9dc8c5206&pid=1-s2.0-S2211675323000817-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dealing with location uncertainty for modeling network-constrained lattice data 处理网络受限网格数据建模的位置不确定性

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-28 DOI: 10.1016/j.spasta.2023.100807

Álvaro Briz-Redón

{"title":"Dealing with location uncertainty for modeling network-constrained lattice data","authors":"Álvaro Briz-Redón","doi":"10.1016/j.spasta.2023.100807","DOIUrl":"10.1016/j.spasta.2023.100807","url":null,"abstract":"<div><p>The spatial analysis of traffic accidents has long been a useful tool for authorities to implement effective preventive measures. Initial studies were conducted at the areal level considering administrative or traffic-related units, but a more precise analysis at the street level is necessary for developing targeted interventions. In recent years, there has been a significant increase in studies conducted at the road network level, which require using new statistical techniques that are suitable for linear networks. However, modeling accident counts at the street level presents several challenges, primarily due to the need for accurate georeferenced data to correctly assign events to specific streets or road segments. Despite advancements in geocoding methods, discrepancies can still arise between the true event locations and the locations mapped by a geocoding method. In this paper, we propose a model to deal with the presence of location uncertainty and enable an analysis of accident intensity constrained to the road network. The model does not assume any specific mechanism for location uncertainty, as this reflects the most common practical scenario. By tackling this inherent problem, the proposed model aims to enhance the accuracy of accident analysis and contribute to the development of effective preventive measures for traffic safety. The model is evaluated with both a simulation study and a case study on the city of Valencia, Spain. For the latter, the proposed model reveals a greater association of road intersections with accident rates than that estimated by the standard model.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"59 ","pages":"Article 100807"},"PeriodicalIF":2.3,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675323000829/pdfft?md5=e1ac1580bb57377d191e0e7bd18fc587&pid=1-s2.0-S2211675323000829-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139072441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation for single-index spatial autoregressive model with covariate measurement errors 具有协变量测量误差的单指数空间自回归模型的估计

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-21 DOI: 10.1016/j.spasta.2023.100805

Ke Wang , Dehui Wang

引用次数: 0

Estimation for single-index spatial autoregressive model with covariate measurement errors 具有协变量测量误差的单指数空间自回归模型的估计

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-21 DOI: 10.1016/j.spasta.2023.100805

Ke Wang, Dehui Wang

引用次数: 0

A flexible likelihood-based neural network extension of the classic spatio-temporal model 经典时空模型的灵活似然神经网络扩展

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-19 DOI: 10.1016/j.spasta.2023.100801

Malte Jahn

{"title":"A flexible likelihood-based neural network extension of the classic spatio-temporal model","authors":"Malte Jahn","doi":"10.1016/j.spasta.2023.100801","DOIUrl":"10.1016/j.spasta.2023.100801","url":null,"abstract":"<div><p>The inclusion of the geographic information into regression models is becoming increasingly popular due to the increased availability of corresponding geo-referenced data. In this paper, a novel framework for combining spatio-temporal regression techniques and artificial neural network (ANN) regression models is presented. The key idea is to use the universal approximation property of the ANN function to account for an arbitrary spatial pattern in the dependent variable by including geographic coordinate variables as regressors. Moreover, the implicit location-specific effects are allowed to exhibit arbitrary interaction effects with other regressors such as a time variable. In contrast to other machine learning approaches for spatio-temporal data, the likelihood framework of the classic (linear) spatio-temporal regression model is preserved. This allows, inter alia, for inference regarding marginal effects and associated confidence. The framework also allows for non-normal conditional distributions, conditional spatial correlation, arbitrary trend and seasonality. These features are demonstrated in a simulation section and two data examples, using linear spatio-temporal models as a reference.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"59 ","pages":"Article 100801"},"PeriodicalIF":2.3,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675323000763/pdfft?md5=a82d70525172e0a009055015cff2b3aa&pid=1-s2.0-S2211675323000763-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138818749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust second-order stationary spatial blind source separation using generalized sign matrices 利用广义符号矩阵进行稳健的二阶静态空间盲源分离

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-16 DOI: 10.1016/j.spasta.2023.100803

Mika Sipilä , Christoph Muehlmann , Klaus Nordhausen , Sara Taskinen

引用次数: 0

Using spatial ordinal patterns for non-parametric testing of spatial dependence 利用空间序数模式对空间依赖性进行非参数检验

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-14 DOI: 10.1016/j.spasta.2023.100800

Christian H. Weiß , Hee-Young Kim

{"title":"Using spatial ordinal patterns for non-parametric testing of spatial dependence","authors":"Christian H. Weiß , Hee-Young Kim","doi":"10.1016/j.spasta.2023.100800","DOIUrl":"10.1016/j.spasta.2023.100800","url":null,"abstract":"<div><p>We analyze data occurring in a regular two-dimensional grid for spatial dependence based on spatial ordinal patterns (SOPs). After having derived the asymptotic distribution of the SOP frequencies under the null hypothesis of spatial independence, we use the concept of the type of SOPs to define the statistics to test for spatial dependence. The proposed tests are not only implemented for real-valued random variables, but a solution for discrete-valued spatial processes in the plane is provided as well. The performances of the spatial-dependence tests are comprehensively analyzed by simulations, considering various data-generating processes. The results show that SOP-based dependence tests have good size properties and constitute an important and valuable complement to the spatial autocorrelation function. To be more specific, SOP-based tests can detect spatial dependence in non-linear processes, and they are robust with respect to outliers and zero inflation. To illustrate their application in practice, two real-world data examples from agricultural sciences are analyzed.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"59 ","pages":"Article 100800"},"PeriodicalIF":2.3,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675323000751/pdfft?md5=509649b2dd645d53b18a5ac022b834c3&pid=1-s2.0-S2211675323000751-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138686989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Copula-Based Data-Driven Multiple-Point Simulation Method 基于 Copula 的数据驱动多点模拟法

IF 2.3 2区数学

Spatial Statistics Pub Date : 2023-12-10 DOI: 10.1016/j.spasta.2023.100802

Babak Sohrabian , Abdullah Erhan Tercan

{"title":"Copula-Based Data-Driven Multiple-Point Simulation Method","authors":"Babak Sohrabian , Abdullah Erhan Tercan","doi":"10.1016/j.spasta.2023.100802","DOIUrl":"10.1016/j.spasta.2023.100802","url":null,"abstract":"<div><p>Multiple-point simulation is a commonly used method in modeling complex curvilinear structures. The method is based on the application of training images that are open to manipulation. The present study introduces a new data-driven multiple-point simulation method that derives multiple point statistics directly from sparse data using copulas and applies them in simulation of complex mineral deposits. This method is based on simplification of N-dimensional copulas by its underlying two-dimensional copulas and taking advantage of conditional independence assumption to integrate information from different sources. The method was compared to Filtersim, a conventional multiple-point geostatistical method, through two synthetic data sets. Reproduction of cumulative distribution function, variogram, N-point connectivity, and visual patterns were considered in comparison. The copula-based multiple-point simulation (CMPS) method was implemented using trivial parts (almost 4%) of the synthetic data to extract required statistics while Filtersim was performed by giving the target image (100% data) as training image. Despite overwhelming data use in Filtersim, the CMPS showed compatible results to it. Application to synthetic data indicated that the method is a promising tool in the simulation of deposits with sparse data. The CMPS were applied in the simulation of two mineral deposits: (1) a porphyry copper deposit and (2) a magmatic iron deposit.</p></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"59 ","pages":"Article 100802"},"PeriodicalIF":2.3,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211675323000775/pdfft?md5=f3c30289a955eabe0dfa21b5ac6ce197&pid=1-s2.0-S2211675323000775-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138566458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0