Until now, we have been interested in understanding the relationships between the
variables, but often the interest is on the similarity between neighborhoods or groups of
neighborhoods. Whilst it is possible to label and color the points on the scatter plots
relating to neighborhoods, it is not easy to interpret them when each neighborhood is
represented on 60 or more plots. It is easier to first reduce the dimensionality of the data
using principal components, and then use a biplot that simultaneously plots information on
the observations and the variables.
The classical biplot popularized by Gabriel represents
the variables using vectors and observations as points whereas a more recent innovation
developed by Gower & Hand represents the variables using calibrated axes allowing
the observations represented as points to be projected onto the axes and an
approximation made. A full monograph titled Understanding Biplots by Gower,
Gardener-Lubbe and LeRoux is an excellent book to learn more about biplots.
The biplot shows the two-dimensional approximation to the original
multidimensional space. It represents 70% of the original variation in the data.
Each point on the biplot represents a neighborhood and each axis represents a
The distance between points represents the similarity between them, points
close to each other are neighborhoods with similar profiles, and points far away
have dissimilar profiles.
Any point on the plot can be projected orthogonally
onto the axes to show the approximate value of that variable. For example, Bedford
Park (center right of the plot) scores around 90 on affordability, 65 on housing
quality, and 70 on food. The true values were 89, 60, and 62 respectively, so the
approximation is fairly accurate for these variables and this
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