Level 2 – Initial grouping of the brands

Whilst the initial overview of meronym distribution amongst the brand content has allowed us to identify which attributes of place are the most heavily utilised in the branding attempts under analysis, it was necessary to develop the analysis further to facilitate grouping of the brands based on this constitution. To this end,

correspondence analysis was employed. Correspondence analysis is a perceptual mapping tool which allows us to illustrate visual relationships and differences in data.

Like many perceptual mapping techniques, correspondence analysis is an exploratory tool that focuses on exploring and representing data, as opposed to formal hypothesis testing (Ivy, 2001). Yavas and Shemwell (1996, in Ivy, 2001:277) suggest that the primary objective of correspondence analysis is to portray data geometrically as a set of row and column points in a low (i.e. two) dimensional

space. As Wels-Lips et al (1998:294) describe, “correspondence analysis produces a configuration of the categories of nominal variables in a n-dimensional space.

Categories of variables that co-occur frequently will appear close to each other, and categories of variables that co-occur infrequently will be distant from each other in the n-dimensional space.” It is important to note here that the correspondence analysis is performed with meronym counts, as opposed to means. Therefore the chi-square test of independence of variables is applicable in this case, rather than the Euclidean distance which would apply in an equivalent analysis of mean scores (Ivy, 2001).

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Table 8 – Correspondence analysis summary of dimensionality calculation

a. 81 degrees of freedom

Table 8 provides a summary of the dimensions which provide potential modes of analysis. The maximum number of dimensions in a correspondence analysis equals the smaller of the number of the rows minus one or the number of columns minus one (Yavas and Shemwell, 1996, in Ivy, 2001). Since the number of rows and

columns is ten (ten meronyms, ten boroughs), the omission of weather on account of 63

it not being employed in any of the brands brings the former down to nine, therefore the maximum number of dimensions applicable in this study is eight. The proportion of inertia columns are significant in allowing us to make a judgement on the number of dimensions to be employed. Indicating how much of the row variance

(representing the meronyms) is accounted for by each dimension; dimension one contributes 41% towards the variance, whilst the addition of dimension two brings this up to 70%. The addition of a third dimension adds just 12%, therefore a two dimensional approach is adequate for the purposes of this study. An additional consideration here is that a lower dimensional solution aids the display and

interpretability of the results. A final point to note is that the correspondence analysis operates with 81 degrees of freedom, indicating the number of parameters of the system that may vary independently (Wels-Lips et al, 1998).

Table 9 below is useful for placing the formation of the illustrative diagram into context, the contribution of the meronyms to the point of inertia in the first and second dimensions is displayed in the right hand columns. As expected, geography (28% first dimension, 12% second dimension) and leisure (6% first dimension, 31%

second dimension) produce the greatest contribution towards the location of points in the diagram, with the other seven meronyms (again, weather has been excluded as it does not contribute at all to the equation) contributing to a far lesser extent.

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Table 9 – Correspondence analysis – overview of row points

Of Point to Inertia of Dimension

Figure 10 illustrates the initial graphical output of the distribution of row points (meronyms) generated. This representation illustrates the relative positioning of the meronyms that form the brands of the Greater Manchester metropolitan area.

Straight away, we can see that two distinct groups of meronyms are visible. Leisure, the arts, people, and economy in one group; heritage, history, local governance and geography in the other. Architecture is the outlier here.

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Figure 10 – Two dimensional chart illustrating row points

The graphical illustration of the distribution of the column points (boroughs), shown in Figure 11, again points to groupings of boroughs in terms of the constitution of their brands. We can see that Manchester, Bolton and Bury are situated very closely together, whilst there is a slightly less closely situated group in the upper left area of the diagram, with Tameside, Wigan, Rochdale, Oldham, Stockport and Trafford sharing a similar brand constitution. In accordance with the positioning of

architecture on the diagram, Salford is the outlier of the group, explained by the heavy and unparalleled weighting of brand constitution towards the architecture meronym. The disparity between brands is also evident, with Manchester and Salford the most extreme example.

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Figure 11 – Two dimensional chart illustrating column points

If we take the row and column points and view them together in the diagram, we can see which boroughs’ brands are most closely associated with particular meronyms (Figure 12). As we have seen, Salford’s brand is highly influenced by architecture, this is the clear outlier in the group and has been heavily influenced by the

prevalence of architectural imagery within the brand content. We can see that the Manchester, Bolton and Bury brands share a similar association with people, the economy, the arts and leisure. The Wigan, Tameside, Rochdale and Oldham brands are more influenced by geography, history, heritage and local governance. The Stockport and Trafford brands fall somewhere between these two groups.

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Figure 12 – Two dimensional chart illustrating row and column points

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