Measuring hierarchical differentiation: connectivity and dominance in the European urban network

Abstract

To refer to or to cite this work, please use the citation to the published version:Van Nuffel, N., Saey, P., Derudder, B., Devriendt, L., Witlox, F. (2010 AbstractThis paper presents an examination of the empirical merits of a set of spatial interaction indices for measuring hierarchical differentiation (i.e. dominance and connectivity) in a spatial network. To allow for the comparison of the degree of hierarchical differentiation in networks with different number of nodes/links, we propose to normalize the ratio between the real measures and the corresponding values for a rank size distribution in order to obtain readily interpretable measures of hierarchical differentiation. When applied to data on 2 air passenger flows within Europe, the normalized indices, interpreted together, appear to give a good idea of the tendency towards hierarchical differentiation.The potential usefulness of this analytical framework is discussed in the context of studies on (transnational) inter-city relations and empirical assessments of changes in the spatial configuration of airline networks.

The impact of progressive liberalization on the spatiality of airline networks: a measurement framework based on the assessment of hierarchical differentiation

“…Furthermore, 'large' differences between RSI ij values again point to the presence of hierarchical differentiation. Before applying the analytical framework adapted from Limtanakool et al (2007) to our dataset, we modified it in two ways (see Van Nuffel et al, 2008). The first modification stems from the fact that intuitively clear notions such as 'small differences' or 'large differences' between the different DIT i and RSI ij values cannot be interpreted straightforwardly.…”

Assessing the Functional Polycentricity of the Mega-City-Region of Central Belgium Based on Advanced Producer Service Transaction Links

“…The SD is however still sensitive to the number of constituent FURs in a MCR. VAN NUFFEL et al (2010) demonstrate this by calculating the SD for a rank size distribution with an equal number of FURs/cities. In this rank size distribution, the relative centrality of FUR i (RC' i ) is 1/i of the relative centrality score of the largest FUR, which is 1.…”

“…To do so, we implement an alternative index that is both insensitive to the size of the MCR and that allows for a straightforward interpretation of individual values. The specification of this functional polycentricity index (FPI) is a variation on the specification of the indices developed in VAN NUFFEL et al (2010). The FPI is calculated in three steps.…”

“…As this is not the case, the third step is to normalize the SD in such a way that eliminates its sensitivity for the number of FURs, and that statements about individual values can be easily substantiated. The normalization formula is ( VAN NUFFEL et al, 2010):…”

Calculating load factors for the transatlantic airline market using supply and demand data – A note on the identification of gaps in the available airline statistics

“…2 The Association of European Airlines (AEA) database. The AEA is a non-profit-making organization that brings together 31 major European airlines and its database contains monthly information on the carrier, origin and destination (airport, city, country and region), number of passengers (subdivided into first class, business class and economy class), freight, mail, number of flights (subdivided into passenger flights and freight flights) and distance between origin and destination (Van Nuffel et al, 2008). The Europax database: a commercial database from the Route Development Company containing monthly passenger numbers and corresponding capacity and frequency supplydata between and within European countries at the city-pair and the country-pair level.…”