Geographically weighted regression (GWR) is a local form of spatial analysis introduced in 1996 in the geographical literature drawing from statistical approaches for curve-fitting and smoothing applications. The method works based on the simple yet powerful idea of estimating local models using subsets...
In this study, Random Forest, eXtreme Gradient Boosting, and Deep Neural Network machine learning methods were used to investigate atmospheric pollutant concentration prediction. The performance was evaluated using R2, root mean square error, and mean absolute error matrics....
Geographically Weighted Regression (GWR) is a spatial statistical technique that models the relationship between a dependent variable and independent variables while allowing for spatial variation in the model parameters, making it sensitive to local geographic variations. What is a Geographically Weighted R...
The Multiscale Geographically Weighted Regression (MGWR) tool performs an advanced spatial regression technique that is used in geography, urban planning, and various other disciplines. It evolved from the Geographically Weighted Regression (GWR) model that uses explanatory and depend...
The model performance is significantly better than classical spatiotemporal regression methods such as geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), as well as machine learning methods such as neural networks and random forests. The STIR models have...
(AP), average wind speed (AW) and average rainfall (AR)), child population density (CPD) and Per capita GDP (GDP) in Inner Mongolia Autonomous Region, China, and to detect the variation of influence in different seasons and counties, geographically weighted regression (GWR) model was ...
A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon. regression analysis in FDD characterization has been criticized for failing to capture spatial variability; therefore, alternatives such as geographically weighted regression (GWR) have been ...
Geographically weighted regression (GWR) was adopted to explore the local spatial heterogeneity of the causal relationships between PM2.5concentrations and geographic factors. It is a powerful technique to examine geographically non-stationarity and varying relationships between dependent/response variable Y an...
free of all known apePlasmodiumspecies, despite the screening of multiple communities1,2. The seeming absence ofPlasmodiuminfections from wild bonobos has remained a mystery.Anophelesvectors, including forest species such asA. moucheti,A. marshalliiandA. vinckei, which are known to carry ape...
forest managementgeographically weighted modellitterlocal maximum likelihoodmixed logitpassive protectionspatial heterogeneity of preferencestourist infrastructureIn this paper, we investigate the use of geographically weighted choice models for modelling spatially clustered preferences. We argue that this is a ...