This paper investigates the role of spatial dependence, spatial heterogeneity and spatial scale in principal component analysis for geographically distributed data. It considers spatial heterogenei..
Understanding the spatial structure of regional economic development is of importance for regional p...
Principal component analysis denotes a popular algorithmic technique to dimension reduction and fact...
Well-being is a multidimensional concept that cannot be described using a single indicator. By the s...
Composite Indicators (CIs) recently earned popularity as decision-support tool in policy-making for ...
AbstractComposite indicators are often used to assess the structure of urban deprivation to promote ...
This article considers critically how one of the oldest and most widely applied statistical methods,...
Background: Reducing health inequalities involves the identification and characterization of social ...
Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicat...
Principal components analysis (PCA) is a widely used technique in the social and physical sciences....
Recent research advances in spatial statistic on spatial autocorrelation help to better understand ...
Principal components analysis (PCA) is a useful analytical tool to represent key characteristics of ...
In many physical geography settings, principal component analysis (PCA) is applied without consider...
AbstractPrincipal Component Analysis is a statistical instrument able to identify the variables expl...
We propose a method to evaluate the existence of spatial variability in the covariance structure in ...
Understanding the spatial structure of regional economic development is of importance for regional p...
Principal component analysis denotes a popular algorithmic technique to dimension reduction and fact...
Well-being is a multidimensional concept that cannot be described using a single indicator. By the s...
Composite Indicators (CIs) recently earned popularity as decision-support tool in policy-making for ...
AbstractComposite indicators are often used to assess the structure of urban deprivation to promote ...
This article considers critically how one of the oldest and most widely applied statistical methods,...
Background: Reducing health inequalities involves the identification and characterization of social ...
Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicat...
Principal components analysis (PCA) is a widely used technique in the social and physical sciences....
Recent research advances in spatial statistic on spatial autocorrelation help to better understand ...
Principal components analysis (PCA) is a useful analytical tool to represent key characteristics of ...
In many physical geography settings, principal component analysis (PCA) is applied without consider...
AbstractPrincipal Component Analysis is a statistical instrument able to identify the variables expl...
We propose a method to evaluate the existence of spatial variability in the covariance structure in ...
Understanding the spatial structure of regional economic development is of importance for regional p...
Principal component analysis denotes a popular algorithmic technique to dimension reduction and fact...
Well-being is a multidimensional concept that cannot be described using a single indicator. By the s...