Publishing the plot

Once the plot is constructed, there are many additional options that can be used to tweak its appearance.

The quality of the representation of the axis (known as the predictivity and shown in brackets at the end of each axis label) indicates how much of the variance in the original variable is explained in the plot. Some of the variables – such as green space, schools and diversity – are poorly represented in a two-dimensional PCA biplot, which means projections of points onto those axes aren’t very accurate. Likewise, some neighborhoods may not be well represented by the two-dimensional approximation.

Another issue is that the axes cross over the data points, which makes them difficult to see.

In the Rotate edit box, enter -.
The plot will be rotated so the axis that is best represented will be horizontal.
Select Transparency.
Points that are poorly approximated will be more transparent.
Select Visibility and in the Predictivity threshold edit box, enter 0.6.
Axes which are poorly represented will be hidden.
Select Adjust axes.
In the Offset X edit boxes, enter Crime = 4, Wellness = -4.5, Nightlife = 4.1
In the Offset Y edit boxes, enter Transit = 4, Shopping & Services = 4, Food = -3.3, Creative = -3.6, Housing Quality = -4.3, Rank = -4.
Click Recalculate.
The results are calculated and the analysis report opens.

The plot is rotated so that the axis that is best represented (affordability) is horizontal. Other axes are offset to the edge of the plot, which makes the points easier to see.

Points are filled with a lighter transparency the poorer the representation. Axes that represent less than 60% of the variation in the variable are hidden, which eliminates the risk of interpreting the projections onto them.