GaWC Data Set 7

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Inter-City Matrices

P.J. Taylor and D.R.F. Walker

These data are derived from Data Set 6 and consist of 55 x 55 matrices which show different ways of specifying connections between world cities.

All matrices are specified in detail in GaWC Research Bulletin 23 (hereafter referred to as RB23).

Dataset 7.1:
 Symmetrical relations between cities: This is the basic relational matrix defined as R in RB23 (equation 12). For every pair of cities, it is based on multiplying their service value scores across all firms and summing the products to create an aggregate city interlock link. These are divided by the maximum possible interlock link aggregate to produce proportional city interlinks.
Dataset 7.2: Service corporate distances between cities: This is a conversion of R into a distance matrix by subtracting the proportional city interlinks from unity for every pair of cities and id defined as D in RB23 (equation 13). These service corporate distances define a 'world service space' in terms of relations between cities.
Dataset 7.3:  Asymmetrical relations between cities: This is a conversion of the aggregate city interlock links from (i) above to produce proportional asymmetrical city interlinks and is defined as A in RB23 (equation 15). The conversion involves using totals specific to each link rather than the maximum possible interlink aggregate. Only firms located in a city are used to compute the new totals used to create proportions. 

As per our data protocol, the following acknowledgement should accompany any public use of the data:

ACKNOWLEDGEMENT: The data were created by P.J. Taylor and D.R.F. Walker as part of their project "World City Network: Data Matrix Construction and Analysis" and are based on primary data collected by J.V. Beaverstock, R.G. Smith and P.J. Taylor (ESRC project "The Geographical Scope of London as a World City" (R000222050)). They constitute Data Set 7 of the Globalization and World Cities (GaWC) Research Network (http://www.lboro.ac.uk/gawc/) publication of inter-city data.