Geographically weighted
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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Geographically weighted correspondence matrices for local error reporting and change analyses: mapping the spatial distribution of errors and change
... approaches in this letter extend earlier work that considered the measures derived from correspondence matrices in the context of generalized linear models and probability. Here the methods compute local, ...
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Simulation and application of spatial autoregressive geographically weighted regression model (SAR GWR)
... heterogeneity, geographically weighted regression (GWR) or varying coefficient model (VCM) is usually ...Autoregressive Geographically Weighted Regression ...
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Comparison of Nelder Mead and BFGS Algorithms on Geographically Weighted Multivariate Negative Binomial
... Geographically Weighted Negative Binomial Regression (GWNBR) was proposed related to univariate spatial count data with overdispersion using MLE via Newton Raphson ...overdispersion, Geographically ...
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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 ...1972), weighted spatial adaptive filtering model (Gorr and Olligschlaeger ...
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Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information
... The geographically weighted evidence combination methods (Dempster-Shafer, Bayesian Probability, Fuzzy Sets and Possibility Theory, GW Average) provide a suite of approaches for assessing belief and for ...
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Crowdsourcing indicators for cultural ecosystem services : a geographically weighted approach for mountain landscapes
... local Geographically Weighted Poisson Regression (GWPR) model in order to see if there exists spatial variability in the relationships between landscape settings and ...
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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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Factors contributing to spatial inequality in academic achievement in Ghana: Analysis of district level factors using geographically weighted regression
... Factors contributing to spatial inequality in academic achievement in Ghana Analysis of district level factors using geographically weighted regression lable at ScienceDirect Applied Geography 62 (201[.] ...
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GIS Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression
... Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness ...
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Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana
... Methods: Data on incidence of P. falciparum parasitaemia were recorded by active and passive follow-up over two years. Nine LULC types were identified through remote sensing and ground-truthing. Spatial associations of ...
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Mixed geographically weighted regression using adaptive bandwidth to modeling of air polluter standard index
... Air pollution is one of the most concerned problems on earth today. It is closely related with and mostly generated from the transportation and industrialization sectors, as well as from the environmentally degrading ...
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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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Downscaling AMSR 2 Soil Moisture Data With Geographically Weighted Area to Area Regression Kriging
... integrates geographically weighted regression and area-to-area kriging, is proposed for down- scaling microwave SM ...proposed geographically weighted area-to-area regression kriging (GWATARK) ...
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Geographically weighted visualization: Interactive graphics for scale-varying exploratory analysis
... Abstract — We introduce a series of geographically weighted (GW) interactive graphics, or geowigs, and use them to explore spatial relationships at a range of scales. We visually encode information about ...
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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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Geographically weighted elastic net logistic regression
... new geographically weighted elastic net logistic regression (GW-ENLR) model), which are themselves extensions of the classic GW regres- sion (GWR) (Brunsdon et ...
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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 regression ...the ...
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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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Using geographically weighted choice models to account for the spatial heterogeneity of preferences
... Using Geographically Weighted Choice Models to Account for the Spatial Heterogeneity of Preferences.. Wiktor Budziński, Danny Campbell, Mikołaj Czajkowski, Urška Demšar.[r] ...
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