World City Network by Taylor Peter J. & Derudder Ben

Author:Taylor, Peter J. & Derudder, Ben
Language: eng
Format: epub
ISBN: 9781317550525
Publisher: Taylor and Francis
Published: 2015-08-16T16:00:00+00:00

Measuring hinterworlds

To compute a city’s hinterworld it is necessary to specify its external relations with all other cities. For this exercise we deal with our operational roster of 152 world cities with at least one fifth of the connectivity of London. In principle, the hinterworld of a given city is simply a representation of its connectivity with every other city. But these initial city-dyad measures have limited utility for comparing urban hinterworlds. The basic problem can easily be seen in Table 4.2: London and Paris appear with very strong inter-city relations for all of the other cities, just as Boston and Maputo provide uniformly low levels of city-dyad connectivities. This is obviously reflecting the network position of these cities. Extrapolating this result to the entire data set, it is found that every city has its strongest connection with either London or New York.

In general, the problem is that city-dyad connectivities tend to closely follow the level of global network connectivities of the cities involved. This means that simply mapping hinterworlds based on city-dyad connectivity measures would produce results that largely replicate the global network connectivity distribution: all hinterworlds, although not exactly the same, look quite similar. Small differences are discernible: in the pedagogic example in Chapter 4 (pp. 61–2), Shanghai is more strongly connected with Chicago than with Paris in spite of the latter having a larger global network connectivity. Nonetheless, intense scrutiny of absolute hinterlands is not the sensible way forward here.

What is required is a measure of hinterworlds from which the global network connectivity of cities is removed. This is actually a relatively simple task. Scatter diagrams of city-dyad connectivity scores against global network connectivity scores show imperfect, yet strong positive linear relationships in every case. Thus city a’s connections can be regressed against the global network connectivity of the other 151 cities using a simple regression equation:

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