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geographically weighted principal components analysis

The application of a geographically weighted principal components analysis for exploring twenty-three years of goat population change across Mongolia

The application of a geographically weighted principal components analysis for exploring twenty-three years of goat population change across Mongolia

... Tsutsumida, N, Harris, P. and Comber, A. 2017. The application of a geographically weighted principal components analysis for exploring twenty-three years of goat population change ...

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Improving land cover classification using input variables derived from a geographically weighted principal components analysis

Improving land cover classification using input variables derived from a geographically weighted principal components analysis

... 26 27 Abstract 28 This study demonstrates the use of a geographically weighted principal components 29 analysis GWPCA of remote sensing imagery to improve land cover classification 30 ac[r] ...

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An extension of geographically weighted regression with flexible bandwidths

An extension of geographically weighted regression with flexible bandwidths

... the weighted design matrix of a GWR model to detect collinearity which, if indicated, can be relieved by geographically weighted ridge regression (Wheeler, 2007) and geographically ...

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An Empirical Study Based on Supply Chain Supplier Evaluation System

An Empirical Study Based on Supply Chain Supplier Evaluation System

... The construction of index system is of practical significance for enterprises to choose their own suppliers. We should further improve the evaluation index system, especially in the weight of the determination of ...

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A multi-criteria spatial deprivation index to support health inequality analyses

A multi-criteria spatial deprivation index to support health inequality analyses

... unemployment. Townsend and Carstairs indices also in- clude a very specific variable available in the British Census, namely the indicator of “ Non car ownership ” . More recent efforts have used other kinds of ...

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Correlations between meteorological parameters and prostate cancer

Correlations between meteorological parameters and prostate cancer

... Subsequently, all candidate models were fitted using GWR analyses [42]. These analyses use information from surrounding counties to build a model where the rela- tionship between the dependent variable and indepen- dent ...

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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

... achievement. Analysis reveals two key fi ndings: (a) the existing pattern of spatial inequality primarily favors academic achievement of students in the Middle and Southern Belt regions of Ghana; and (b) factors ...

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Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

... Acknowledgments The authors wish to thank the following col- leagues who have commented on the analysis or reviewed the final manuscript: Dr Sara Downs, Dr Tony Goodchild, Dr Jessica Parry, Jane Gibbens, Prof. ...

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Analysis of Socio Ecological Impacts of Built Environment at Dar es Salaam Metropolitan Coastline, Tanzania

Analysis of Socio Ecological Impacts of Built Environment at Dar es Salaam Metropolitan Coastline, Tanzania

... velopment Analysis, Simple Multi-Attribute Rating Technique, Goal Programming, ELECTRE, PROMETHEE, Simple Additive Weighting, and Technique for Order of Preference by Similarity to Ideal ...decision ...

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Evaluation of visualisations of geographically weighted regression, with perceptual stability

Evaluation of visualisations of geographically weighted regression, with perceptual stability

... Slocum et al. (2001) suggest when differences in visual analytic strategies are identified then two approaches are possible to address the question ‘what to do with them’; Integrate the insights from findings in ...

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The ‘not-so-strange’ body in the mirror: A principal components analysis of direct and mirror self-observation : A principal components analysis of direct and mirror self-observation

The ‘not-so-strange’ body in the mirror: A principal components analysis of direct and mirror self-observation : A principal components analysis of direct and mirror self-observation

... 21 items, to which participants first provide a yes/no response to indicate the presence (scored 1) or absence (scored 0) of a given delusional belief, and subsequently (for endorsed items only) ratings are provided for: ...

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Colon cancer diagnosis using NMR spectra of urine

Colon cancer diagnosis using NMR spectra of urine

... PCA is a transform that chooses a new coordinate system for a data set such that the greatest variance by any projection of the data set comes to lie on the first axis, the first principal component, the second ...

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Principal Components Analysis of Acacia burkei  and A. nigrescens in Natal

Principal Components Analysis of Acacia burkei and A. nigrescens in Natal

... Four principal component analyses were carried out to study the perplexing relations within Acacia burkei ...of principal components analysis was most useful in this ...

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Linkage analysis using principal components of gene expression data

Linkage analysis using principal components of gene expression data

... Figure 1 shows the relative weighting for the first five PCs. For each component, the bar height represents the relative influence of a particular probe. Also included is the gene and gene location associated with each ...

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The importance of scale in spatially varying coefficient modelling

The importance of scale in spatially varying coefficient modelling

... package for exploring spatial heterogeneity using geographically weighted models. Eigenfunction properties and approximations of selected[r] ...

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Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation

Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation

... It is expected that there are some cases, especially those with high eigenspreads, which require a very large number of samples to achieve very accurate eigenvector estimations, especially for the minor ...

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Principal Components Analysis a method Useful in Identification of Clusters of Variables

Principal Components Analysis a method Useful in Identification of Clusters of Variables

... the analysis the variance accounted by resultant 6 components was ...the principal components analysis. Thus the final analysis was carried out by removing attitudes At1 and ...

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Principal Components Analysis with Spline Optimal Transformations for Continuous Data

Principal Components Analysis with Spline Optimal Transformations for Continuous Data

... nonlinear principal components and the original variables can be ...nonlinear principal components’ space and reconstruction the original ...nonlinear principal components and ...

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Application of Principal Components Analysis Results in Visual Network Analysis

Application of Principal Components Analysis Results in Visual Network Analysis

... of principal components analysis application in economic and financial tasks demonstrates its efficiency in playing the role of a preprocessor in the process of network visual analysis ...

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Principal Components and Factor Analysis  A Comparative Study

Principal Components and Factor Analysis A Comparative Study

... In sum, FA is shown to largely outperform PCA on several grounds: i) when utilized as regressors and/or instruments, the FA scores produce more efficient slope estimators in instrumental variable estimation, notably in ...

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