Energy Vulnerability Principal Components for LSOA in England

For a case study of England, global principal component analysis (PCA)
is applied to a suite of neighborhood-scale energy vulnerability
indicators.

PCA reduces a large multivariate set of vulnerability factors into a
reduced number of principal components, retaining key statistical information
and spatial patterns. The components have loading values associated with each
of the vulnerability indicators in the input data set. Loadings tell us about
the type (negative or positive) and strength of the relationship between an
indicator and a principal component, providing information about the patterns
of vulnerability within the data set that each component is likely to
represent. These global component loadings can be mapped to provide an
understanding of the spatial distribution of the vulnerability represented by
each principal component and the locales in which vulnerability is likely to be
enhanced as a result.

This dataset contains three principal components which account for
62.4 percent of the variance in the 21 energy vulnerability indicators identified. The
first component has strong positive association with precarious and transient
families but a strong inverse relationship with retirement and older age
groups. The second component has a strong positive relationship with
disability, illness, and the provision of care. The third component has a
positive relationship with the energy efficiency and availability of networked
and domestic energy infrastructures. The principal components are mapped at the
Lower Super Output Area (LSOA) scale, an administrative area unit with a mean
population of 1,500 persons.