Page 371 - 360.revista de Alta Velocidad - Nº 6

The configurations of Chinese national urban systems in both high-speed railway and airline networks

Where we define as the city centrality, indicating the relative strength of city i in the

national transportation network. is the total number of passenger volumes associated with

city i, and i≠j. Cities with values above 1 are considered dominant because they are

more important than the average of the other cities in the network. To compare the different
positions of cities in the two transportation networks, we further categorized 3 classes of
dominant cities (the first class with a DIT value larger than 10 as national dominant cities, the
second class with a DIT value between 5 and 10 as regional dominant cities, and the third class
with a DIT value between 1 and 5 as local dominant cities (Wang and Jing, 2017).

Where we define as the connectivity of a city pair, indicating the relative strength of a

link connected by the national transportation network. is the total number of passengers

travelling between cities i and j, and i≠j. is the value for all links in the network sum to

unity, while individual values range from 0 to 1. A value of 1 represents the highest strength of
a link. Since some RSL values will be rather small, to clear understand their strength values, the
RSL value is multiplied by 1000 (Derudder and Witlox, 2009). According to the multiplied value
of link strength, we categorized 5 classes of dominant city links (the first class with a RSL value
larger than 20, the second class with a RSL value between 10 and 20, and the third class with a
between 5 and 10, the fourth class between 1 and 5.
We further perform a multiple linear regression to investigate the differential impacts of
attributes of urban systems on the city and link strength in both transportation networks.
Following the existing literature, in Table 1 we considered a mix of geographic, social, economic
and political attributes related to cities as potential covariates.

Table 1. Independent variables for regression analysis

Variables Information Source Mean_HSR SD_HSR Mean_Airline SD_Airline
City strength
GDP per (mi- Gross domestic product per Chinese urban 3712.4 3989.6 2549.6 3431.5
llion yuan) capita for a city statistical
Population yearbooks 578.5 428.7 452.2 402.9
(inhabitants) Population for a city 2014
The average distance of one Calculated by
Average city to other cities connected 551.4 175.2 914.2 320.8
distance (km) authors from
by HSR or airline networks GIS
Administrative Hierarchical administrative (Ma, 2005) 2.7 0.5 2.8 0.5
level level (scored)
3=Municipality level city 2=
Sub-provincial/ regional capital
level city 1 = Prefecture city
Link strength
Summed gross domestic
Summed GDP product per capita for
per (million 9361.4 6604.1 12145.1 6923.8
yuan) each city pair of origin and
destination
Summed Summed population for Calculated by
population each city pair of origin and 1264.0 607.5 1489.3 854.0
(inhabitants) destination authors
The geographical distance
Distance (km) 605.3 363.3 1086.2 583.1
between a city pair
Summed administrative level
Summed admi- for each city pair of origin and 5.3 0.8 4.5 0.7
nistrative level
destination

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