21 papers · ranked by Valyu relevance
Michael Dumelle, Matt Higham, Jay M. Ver Hoef, A. K. M. Anisur Rahman
'A. K. M. Anisur Rahman'] spmodel is an R package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms.…
Sean C. Anderson, Eric J. Ward, Philina A. English, Lewis A. K. Barnett
Geostatistical data—spatially referenced observations related to some continuous spatial phenomenon— are ubiquitous in ecology and can reveal ecological processes and inform management decisions. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian…
Aaron Osgood-Zimmerman, Micha Sam Brickman Raredon
Tissues are shaped by extracellular signaling fields which convey information between cells. The cellular composition of tissues, and the extracellular signaling within the tissue, are innately spatially structured. Modern spatialomics data provide unprecedented measurement of ligand and receptor expressivity in situ…
Emiko Dupont, Simon N. Wood, Nicole H. Augustin
In spatial regression models, collinearity between covariates and spatial effects can lead to significant bias in effect estimates. This problem, known as spatial confounding, is encountered modeling forestry data to assess the effect of temperature on tree health. Reliable inference is difficult as results depend on…
Jennifer F. Bobb, Maricela Cruz, Stephen J. Mooney, Adam Drewnowski + 2 more
'David Arterburn' 'Andrea J. Cook'] In the presence of unmeasured spatial confounding, spatial models may actually increase (rather than decrease) bias, leading to uncertainty as to how they should be applied in practice. We evaluated spatial modeling approaches through simulation and application to a big data…
Jay M. Ver Hoef, Michael Dumelle, Matt Higham, Erin E. Peterson + 2 more
'Daniel J. Isaak' 'Mohamed R. Abonazel'] We consider four main goals when fitting spatial linear models: 1) estimating covariance parameters, 2) estimating fixed effects, 3) kriging (making point predictions), and 4) block-kriging (predicting the average value over a region). Each of these goals can present different…
Holger Engleitner, Ashwani Jha, Marta Suárez‐Pinilla, Amy Nelson + 5 more
'Daniel M. Herron' 'Geraint Rees' 'Karl Friston' 'Martin N. Rossor' 'Parashkev Nachev'] The characteristics and determinants of health and disease are often organised in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations.…
Xinyu Zhou, Pengtao Dang, Xiao Wang, Laura Xianlu Peng + 7 more
Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general statistical framework that…
HaiXiang Guo, CongLe Hu, MeiYi Xin, Qi Wu
Constructing a basin-level ecological security early-warning (ESEW) system is critical for overcoming the fragmentation of administrative management and achieving holistic watershed governance. Focusing on the Poyang Lake Basin, this study develops a spatial ESEW assessment framework based on the…
Xinyu Zhou, Pengtao Dang, Haixu Tang, Laura Xianlu Peng + 6 more
Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general framework that estimates…
Posada, Alejandro Rozo, Gressani, Oswaldo + 7 more
Traditional spatio-temporal models for areal data typically begin with spatial structure imposed at the level of random effects and later extend to include temporal dynamics. We propose an alternative hierarchical modeling framework that captures temporal trends in areal data through Gaussian processes that share…
Getayeneh Antehunegn Tesema, Zemenu Tadesse Tessema, Stephane Heritier, Rob G. Stirling + 2 more
'Stephane Heritier' 'Rob G. Stirling' 'Arul Earnest' 'Jahar Bhowmik'] With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several…
Shuqi Lin, Joshua L. Warren
Although spatial models for areal data are widely used in multilevel settings, the conditions under which spatial and nonspatial random effects yield equivalent posterior inference for regression coefficients have never been formally characterized. We address this question within a hierarchical Bayesian framework for…
Holger Engleitner, Ashwani Jha, Marta Suarez Pinilla, Amy Nelson + 5 more
'Daniel Herron' 'Geraint Rees' 'Karl Friston' 'Martin Rossor' 'Parashkev Nachev'] Title: Summary The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial…
Erick Orozco‐Acosta, Aritz Adin, M. D. Ugarte
Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal count data, with special emphasis on…
Mei Tessum, Susan Anenberg, Zoe Chafe, Daven Henze + 4 more
To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside…
Diogenis A. Kiziridis, Anna Mastrogianni, Magdalini Pleniou, Spyros Tsiftsis + 2 more
The CLUE-S model is a popular choice for modelling land use and land cover change from local to regional scales, but it spatially allocates the demand for only the total cover of each land class in the predicted map. In the present work, we introduce a CLUE-S variant that allocates demand at the more detailed level of…
Fabian R. Ketwaroo, Matia H. Muller, James F. Saracco, Michael Schaub
Demographic processes in populations are inherently heterogeneous across both space and time. Many ecological models explicitly account for temporal heterogeneity in the demographic rates that govern these processes, but assume spatial homogeneity. Ignoring spatial heterogeneity can bias inference, limit predictive…
Philipp Otto, Rick Steinert
In this paper, we propose a two-step lasso estimation approach to estimate the full spatial weights matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial locations. The proposed approach jointly estimates the spatial…
Authors not listed
Atmospheric dispersion models are a key component for characterizing methane emissions on oil and gas sites. While some model implementations with varying degrees of complexity are available, existing regulatory-grade dispersion models are cumbersome to apply within an inversion framework on a routine operational level…
Authors not listed
In the United States, people of color are disproportionately and unjustly exposed to air pollution. Historically, environmental policy has emphasized aggregate emission reductions; yet major emission reduction scenarios do not sufficiently mitigate relative exposure disparities. Here, we show that without focusing on…