world_cities — World city network of global service firms (2000)
- Description
A bipartite network of 100 global advanced producer service firms and the 315 cities in which they have offices, from the GaWC (Globalization and World Cities) research network, data set 11, produced by P. J. Taylor and G. Catalano and used as the empirical basis for measuring the world city network. The data were collected in 2000. All firms supply advanced producer services (accountancy, advertising, banking/finance, insurance, law and management consultancy) through offices in at least 15 cities, including at least one in each of Pacific Asia, western Europe and northern America. An edge connects a firm to a city in which it has an office, weighted by the service value, which indicates the importance of the city in the office network of the firm, on a scale from 1 to 5 (cities without an office of the firm, with value 0, have no edge). The data were converted into Pajek format by V. Batagelj (2006). The data were distributed in Pajek format on the Pajek datasets page of V. Batagelj and A. Mrvar, from which this version was obtained.
The network has the following properties:
- Nodes:
name, the name of the city or firm;is_city, whether the node is a city (otherwise a firm);sector, the sector of the firm (0 for cities, 1: accountancy, 2: advertising, 3: banking/finance, 4: insurance, 5: law, 6: management consultancy). - Edges:
weight, the service value of the city for the firm (1 to 5).
- Nodes:
- Tags
- Economic Commerce Weighted Spatial Metadata
- Citation
-
- P. J. Taylor, "World City Network: A Global Urban Analysis." Routledge, London (2004) 10.4324/9780203634059 Sci-Hub
- P. J. Taylor, G. Catalano, D. R. F. Walker, "Measurement of the World City Network." Urban Studies 39(13), 2367-2376 (2002) 10.1080/00420980220080011 Sci-Hub
- Upstream URL OK
- https://web.archive.org/web/20090301185253/http://vlado.fmf.uni-lj.si/pub/networks/data/mix/mixed.htm
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
- Ridiculograms*
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