Review on Geographically Weighted Regression (GWR) approach in spatial analysis
TL;DR: Geographically Weighted Regression (GWR) is a technique in which it applied to capture the variation by calibrating a multiple regression model, which allows different relationships to exist at different points in space as mentioned in this paper.
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Abstract: In spatial analysis, it is important to identify the nature of the relationship that exists between variables. Normally, it is done by estimating parameters with observations which taken from different spatial units that across a study area where parameters are assumed to be constant across space. However, this is not so as the spatial non-stationarity is a condition in which a simple model cannot explain the relationship between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. Non-stationarity means that the relationship between variables under study varies from one location to another depending on physical factors of the environment that are spatially autocorrelated. Geographically Weighted Regression (GWR) is a technique in which it applied to capture the variation by calibrating a multiple regression model, which allows different relationships to exist at different points in space. A robust algorithm has been successfully used in spatial analysis. GWR can theoretically integrate geographical location, altitude, and other factors for spatial analysis estimations, and reflects the non-stationary spatial relationship between these variables. The main goal of this study is to review the potential of the GWR in modelling the spatial relationship between variables either dependent or independent and its used as the spatial prediction models. Based on the application of GWR such as house property indicates that GWR is the best model in estimating the parameters. Hence, from the GWR model, the significance of the variation can also be tested
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References
Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity
TL;DR: A technique is developed, termed geographically weighted regression, which attempts to capture variation by calibrating a multiple regression model which allows different relationships to exist at different points in space by using Monte Carlo methods.
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Testing the performance of spatial interpolation techniques for mapping soil properties
TL;DR: In this paper, the authors compare the performance of different interpolation methods for seasonally stable soil properties (pH, electric conductivity and organic matter) that have been demonstrated to affect yield production, and conclude that many parameters would be better identified from the RMSE statistic obtained from cross-validation after an exhaustive testing.
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Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model
TL;DR: Geographically weighted regression (GWR) as mentioned in this paper is a way of exploring spatial nonstationarity by calibrating a multiple regression model which allows different relationships to exist at different points i.e.
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