Geographically Weighted Regression (GWR)
Exploring Spatial Variability in the Relationship between Long Term Limiting Illness and Area Level Deprivation at the City Level Using Geographically Weighted Regression
... methods. Geographically weighted regression (GWR) is a spatial statistics tool that expands standard regression by allowing for spatial variance in ...
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GIS Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression
... squares regression (OLS), linear and other non- linear models cannot detect spatial variation and rela- tionships within geographic ...with geographically weighted regression (GWR)—a technique ...
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Modeling spatial variations in household disposable income with Geographically Weighted Regression
... The purpose of this paper is to analyze the spatially varying impacts of some classical regressors on per capita household income in Spanish provinces. The authors model this distribution following both a traditional ...
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Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization
... Geographically Weighted Regression (GWR), as first described in Brunsdon et ...local regression models are constructed at different locations using data under a moving window or kernel, which ...
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Calibrating a geographically weighted regression model with parameter-specific distance metrics
... Geographically Weighted Regression (GWR) is a local technique that models spatially varying relationships, where Euclidean distance is traditionally used as default in its ...
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Building a hybrid land cover map with crowdsourcing and geographically weighted regression
... uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an ...
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Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method
... Geographically Weighted Regression (GWR) is a local modeling approach that explicitly allows parameter estimates to vary over space (Brunsdon et ...
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Simulation and application of spatial autoregressive geographically weighted regression model (SAR GWR)
... standard regression analysis on spatial data are not appropriate because of the characteristics of the spatial ...linear regression model leads to bias, inconsistency and inefficient ...heterogeneity, ...
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Distance metric choice can both reduce and induce collinearity in geographically weighted regression
... Geographically weighted regression (GWR) is a technique used to explore spatially-varying data relationships (Brunsdon et ...For regression, and from a nonstationary relationship viewpoint, a ...
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Satellite and gauge rainfall merging using geographically weighted regression
... Although various schemes have been developed, rainfall merging is still a complex and important issue. The results of rainfall merging are influenced by the kind of merging scheme, the quality of satellite rainfall data, ...
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Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization
... Geographically Weighted Regression (GWR), as first described in Brunsdon et al [1], is a commonly used approach in spatial ...local regression models are constructed at different locations ...
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Mixed geographically weighted regression using adaptive bandwidth to modeling of air polluter standard index
... Spatial regression method frequently used is Geographically Weighted Regresssion (GWR), which is a regression method involving the effect of the location into the predictor (Fotheringham, et ...
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A modification to geographically weighted regression
... distinctness. One possible solution is to include only the locations of data with similar attributes (i.e., homo- geneity). However, it is difficult to decide the number of groups with different attributes and identify ...
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An extension of geographically weighted regression with flexible bandwidths
... Constructing mathematical models of processes is a common theme in analytical research in a wide range of disciplines. “Researchers search for variables to identify various dimensions of phenomena and for relationships ...
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Spatial Modeling of Residential Crowding in Alexandria Governorate, Egypt: A Geographically Weighted Regression (GWR) Technique
... a geographically weighted regression (GWR) was also employed using the same response variable and explanatory variables to capture spatial non-stationary of residential ...
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Using geographically weighted regression to explore spatial variation in survey data
... Nonresponse can undermine the quality of social survey data. Understanding who does/does not respond to surveys is important for those involved in the collection and analysis of these data. Levels of nonresponse are ...
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Estimating the Parameters Geographically Weighted Regression (GWR) with Measurement Error
... Geographically weighted regression models with the measurement error are a modeling method that combines the global regression models with the measurement error and the weighted ...
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Factors contributing to spatial inequality in academic achievement in Ghana: Analysis of district level factors using geographically weighted regression
... This study has limitations. First, the lack of data on the latest district demarcations means that this study could not offer insights into outcomes in the recently created districts. Related to the above data limitation ...
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Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression
... where there is usually high fire occurrence (check Fig. 1a). However, these maps are too oversmoothed in capturing lo- cal variations, especially in the logistic GWR model. The trend of the residuals of the logistic ...
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Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales
... with regression coefficients switching between negative and positive val- ues in different locations, which indicates that while focusing interventions on a particular risk factor might be beneficial in one area, ...
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