Exploration of spatially varying relationships between Pb and Al in urban soils of London at the regional scale using geographically weighted regression (GWR)

2020 ◽  
Vol 393 ◽  
pp. 122377 ◽  
Author(s):  
Yumin Yuan ◽  
Mark Cave ◽  
Haofan Xu ◽  
Chaosheng Zhang
Keyword(s):  
Geographically Weighted Regression ◽  
Urban Soils ◽  
Regional Scale ◽  
Weighted Regression ◽  
Spatially Varying

A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships

2011 ◽  
Vol 43 (12) ◽  
pp. 2992-3010 ◽  
Author(s):  
Antonio Páez ◽  
Steven Farber ◽  
David Wheeler
Keyword(s):  
Geographically Weighted Regression ◽  
Numerical Experiments ◽  
Cross Validation ◽  
Weighted Regression ◽  
Small Samples ◽  
Large Variability ◽  
Underlying Process ◽  
Simulation Based ◽  
Multivariate Relationships ◽  
Spatially Varying

Large variability and correlations among the coefficients obtained from the method of geographically weighted regression (GWR) have been identified in previous research. This is an issue that poses a serious challenge for the utility of the method as a tool to investigate multivariate relationships. The objectives of this paper are to assess: (1) the ability of GWR to discriminate between a spatially constant processes and one with spatially varying relationships; and (2) to accurately retrieve spatially varying relationships. Extensive numerical experiments are used to investigate situations where the underlying process is stationary and nonstationary, and to assess the degree to which spurious intercoefficient correlations are introduced. Two different implementations of GWR and cross-validation approaches are assessed. Results suggest that judicious application of GWR can be used to discern whether the underlying process is nonstationary. Furthermore, evidence of spurious correlations indicates that caution must be exercised when drawing conclusions regarding spatial relationships retrieved using this approach, particularly when working with small samples.

scholarly journals Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7350
Author(s):  
Changdong Liu ◽  
Junchao Liu ◽  
Yan Jiao ◽  
Yanli Tang ◽  
Kevin B. Reid
Keyword(s):  
Environmental Effects ◽  
Geographically Weighted Regression ◽  
Regression Models ◽  
Yellow Perch ◽  
Lake Erie ◽  
Regression Coefficients ◽  
Weighted Regression ◽  
Local Regression ◽  
Spatially Varying ◽  
Spatial Locations

Background Global regression models under an implicit assumption of spatial stationarity were commonly applied to estimate the environmental effects on aquatic species distribution. However, the relationships between species distribution and environmental variables may change among spatial locations, especially at large spatial scales with complicated habitat. Local regression models are appropriate supplementary tools to explore species-environment relationships at finer scales. Method We applied geographically weighted regression (GWR) models on Yellow Perch in Lake Erie to estimate spatially-varying environmental effects on the presence probabilities of this species. Outputs from GWR were compared with those from generalized additive models (GAMs) in exploring the Yellow Perch distribution. Local regression coefficients from the GWR were mapped to visualize spatially-varying species-environment relationships. K-means cluster analyses based on the t-values of GWR local regression coefficients were used to characterize the distinct zones of ecological relationships. Results Geographically weighted regression resulted in a significant improvement over the GAM in goodness-of-fit and accuracy of model prediction. Results from the GWR revealed the magnitude and direction of environmental effects on Yellow Perch distribution changed among spatial locations. Consistent species-environment relationships were found in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit (MU) implied the variation of relationships at a scale finer than the MU. Conclusions This study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The GWR results can provide support for identification of unique stocks and potential refinement of the current jurisdictional MU structure toward more ecologically relevant MUs for the sustainable management of Yellow Perch in Lake Erie.

scholarly journals A Comparison of Nonergodic Ground-Motion Models based on Geographically Weighted Regression and the Integrated Nested Laplace Approximation

2021 ◽  
Author(s):  
Nicolas Kuehn
Keyword(s):  
Ground Motion ◽  
Geographically Weighted Regression ◽  
Seismic Hazard Assessment ◽  
Predictive Performance ◽  
Laplace Approximation ◽  
Weighted Regression ◽  
Integrated Nested Laplace Approximation ◽  
Motion Models ◽  
Spatially Varying ◽  
Ground Motion Models

Different nonergodic Ground-Motion Models based on spatially varying coefficient models are compared for ground-motion data in Italy. The models are based different methodologies: Multi-source geographically weighted regression (Caramenti et al., 2020), and Bayesian hierarchical models estimated with the integrated nested Laplace approximation (Rue et al., 2009). The different models are compared in terms of their predictive performance, their spatial coefficients, and their predictions. Models that include spatial terms perform slightly better than a simple base model that includes only event and station terms, in terms of out-of sample error based on cross-validation. The Bayesian spatial models have slightly lower generalization error, which can be attributed to the fact that they can include random effects for events and stations. The different methodologies give rise to different dependencies of the spatially varying terms on event and station locations, leading to between-model uncertainty in their predictions, which should be accommodated in a nonergodic seismic hazard assessment.

scholarly journals Spatial Analysis Of Foreign Migration In Poland In 2012 Using Geographically Weighted Regression

2014 ◽  
Vol 17 (4) ◽  
pp. 137-154 ◽  
Author(s):  
Karolina Lewandowska-Gwarda
Keyword(s):  
Geographically Weighted Regression ◽  
Graphical Representation ◽  
Emigration And Immigration ◽  
Weighted Regression ◽  
Model Parameters ◽  
Population Changes ◽  
Principal Influence ◽  
Gis Tools ◽  
Spatially Varying ◽  
The Impact

Migration has a principal influence on countries’ population changes. Thus, the issues connected with the causes, effects and directions of people’s movements are a common topic of political and academic discussions. The aim of this paper is to analyse the spatial distribution of officially registered foreign migration in Poland in 2012. GIS tools are implemented for data visualization and statistical analysis. Geographically weighted regression (GWR) is used to estimate the impact of unemployment, wages and other socioeconomic variables on the foreign emigration and immigration measure. GWR provides spatially varying estimates of model parameters that can be presented on a map, giving a useful graphical representation of spatially varying relationships.  

Sign in / Sign up

Export Citation Format

Share Document