Socio-Economic Deprivation Analysis: Diffusion Maps
June Moh Goo
This report proposes a model to predict the location of the most deprived
areas in a city using data from the census. A census data is very high
dimensional and needs to be simplified. We use a novel algorithm to reduce
dimensionality and find patterns: The diffusion map. Features are defined by
eigenvectors of the Laplacian matrix that defines the diffusion map.
Eigenvectors corresponding to the smallest eigenvalues indicate specific
population features. Previous work has found qualitatively that the second most
important dimension for describing the census data in Bristol is linked to
deprivation. In this report, we analyse how good this dimension is as a model
for predicting deprivation by comparing with the recognised measures. The
Pearson correlation coefficient was found to be over 0.7. The top 10 per cent
of deprived areas in the UK which also locate in Bristol are extracted to test
the accuracy of the model. There are 52 most deprived areas, and 38 areas are
correctly identified by comparing to the model. The influence of scores of IMD
domains that do not correlate with the models, Eigenvector 2 entries of
non-deprived OAs and orthogonality of Eigenvectors cause the model to fail the
prediction of 14 deprived areas.
  However, overall, the model shows a high performance to predict the future
deprivation of overall areas where the project considers. This project is
expected to support the government to allocate resources and funding.