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The Dog of the North: The Annals of Mondia
I have thoroughly enjoyed everything written by Tim Stretton. This novel is no exception.
A tightly-knit society where etiquette and tradition rule, and tradition seal the acts of most of the characters--most, except the rebel protagonist Todarko who goes up against the grain; his emotional convictions won't let him sit back and stay complaisant, nor the disgruntled second-in-line descendant, Malvazan, who is constantly seeking recognition. Stretton is attempting to bring life to a world which defies change.
Its petty politics and machinations of the ruling class clamp individuals down, heroes and villains alike. I admire Stretton for the purity of his attack. No, they must fend for themselves and use their own wits. This is somewhat artful and to be admired in today's world of ever-growing adventure and pseudo-magical tales. The first level is the city as a restricted functional urban area, which is generally defined according to the daily commuters converging to the city or to its suburbs. For other parts of the world, a comparable definition was applied city by city.
The second level is an extended functional urban region. In our case, we considered the influence area of the airports: In this second level, 12 cities were aggregated in new delineated urban regions Table 1. Important aggregation criteria include spatial proximity as well as belonging to a common urbanized area e. One example is the urban region of London Thames, which is comprised of five ports i. London, Thamesport, Tilbury, Sheerness, and Felixstowe and four airports i. Heathrow, Gatwick, Stansted, and London City.
It does not include Southampton, which is located less than kilometers from London but constitutes an urban area on its own of no fewer than , inhabitants, with its own international airport. The third level takes inspiration from the concepts of the megalopolis proposed by Gottman and the corridor proposed by Whebell , which are both defined by large regions where several urban regions are close. Examples of large gateways include areas extending from Boston to Washington Northeast American megalopolis , from Tokyo to Osaka Japanese megalopolis , and from Amsterdam to Brussels Ruhr-Flanders , all of which are marked by high density corridors.
Here, we aggregated significantly the urban regions of level 2, decreasing the number of units from urban regions to megalopolises Table 1. Nodes cities and links flows: In order to measure the correlation or the similarity between the two networks, which must have the same set of nodes, even with null values, we instead treat them using a weighted approach. For the second case of articulated weighted networks, we keep the linkages inside each aggregated unit reflexive links. It is thus useful to compare hierarchies of graphs section 4.
These three approaches are only possible using the comparable databases described previously. These macro approaches of the graphs constitute a first step in order to underline common features as well as differences between the two networks. In extent, this means that if we plot the degree distribution of the nodes in our network, we will obtain a function which approximates a power law: As stated before, maritime and airline transportation networks reflect in part the development of urbanization patterns in different areas of the world. The general idea is quite simple: The city size has a similar distribution, which better fits a power law when considering corridors i.
This pattern describing the Zipf law goes further in the scale-free assumption Newman, The growth process of the network follows a preferential attachment that characterizes the development of networks: In reality, we can find earlier explanations of the Zipf law proposed by Simon , referring to the Yule process Yule, In Figures 2 and 3 represent the frequency distribution of the airline and sea network, respectively, comparing the distribution of the degree for the three levels city, urban region and megalopolis described above.
In both networks, levels 1 and 2 are quite similar as a consequence of the small number of cities aggregated; both levels approximate the power law distribution with a typical slope.
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Scale-free dimension of air networks for the three levels 6. Scale free dimension of sea networks for the three levels 7. The new nodes are more likely to be connected with the existing nodes, which already have a large number of connections, with reference to the preferential attachment process. This process is shown clearly by the form of the distribution for levels 1 and 2 Figures 2 and 3. Here, the distributions plotted in Figures 2 and 3 do not consider nodes with degrees smaller than In fact, when processing the aggregations as described in Section 3, metropolises have been formed by the addition of cities to the largest metropolitan areas, considering spatial proximity as a crucial criterion.
The group of nodes with a small degree is largely localized in geographically isolated areas, and their size does not allow them to be classified as metropolises.
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The function for level 3 has a lower slope, and the right part of the graph is less dense: In the metropolis level, the scale-free property is not confirmed. Nevertheless, in order to better outline the features of the level 3 networks, we compare the degree distribution obtained from our geographical aggregation based on proximity of cities with the degree distribution of a randomly defined aggregation from levels 1 and 3. The result is plotted in the charts presented in Figures 4 and 5.
It is now evident that the peculiar slope of the distribution for level 3 depends mainly on the aggregation process. The result confirms that our third level aggregation tackles some of the constituting features of the hierarchical structure inherent in the urban network described by air and maritime transportation. Also in Figures 4 and 5, the nodes with small degree i. The two networks are structured in different ways, because flights develop more on long-range distances, compared to the short distances of other transport modes.
Sea transportation develops as well short- and long-distance linkages by circuits. The issue that we want to address is whether cities occupy the same relative position in the two networks and if some important links of one network could influence the strength of the same links in the other network. The higher these indexes are, the more we could consider that the two networks become interdependent in the formation of the system of cities. As a construction effect, correlations and similarities increase with further aggregation. In fact, urban regions and megalopolises exhibit better spatial coherence with aggregated weights by city in both air and sea networks.
At the world scale, an aggregated approach at the megalopolis level is useful to catch the mass effect of large dense urban areas. Although such result was expectable, the increase of correlation indices from one level to another confirms the quality of the proposed delineations, notably at the second level urban region , in which only few cities have been aggregated.
Thus, we can conclude that the second level is effective in identifying some cohesive aggregations of cities in the networks. Of course, node correlation requires less similarity than correlation in links and network similarities. However, significant similarities between networks show in particular that the networks between main cities are similar, whatever the transportation mode considered.
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In fact, we could have obtained similar results by considering communication or financial networks this relation remains to be tested. This is a very important result, showing common features of very different networks although they have been created by the same city-based factors of economic development. It confirms our hypothesis that combined transport networks are good reflections of hierarchical tendencies among cities. It is not so basic because with deregulation and new transport nodes created by private companies and governments, one could think that they develop strategies countering the dominant processes.
But in fact, these actors follow the main streams of urban development. However, there are also differences in the respective position of cities in both networks, on which we focus in the next section. One important issue is how to better understand whether cities with a balanced mix of networks perform better than cities specialized in a particular network.
A large body of literature exists on the relation between city size and functional diversity in urban geography and economics Quigley, ; Taylor et al. Such framework has not been applied to air and sea transport although it is acknowledged that many cities strive for sustaining their position in both international container shipping and airline flights Jauhiainen, Nowadays, urban policies may give priority to air transport over sea transport for higher impacts in terms of value-added logistics and raising overall urban attractiveness Thayer and Whelan, Other important questions are related to the hierarchical relations among cities: Degree centrality, the number of cities connected, acts as a local measure; betweenness centrality, the number of positions on the shortest possible paths within the entire graph, indicates overall accessibility in the network; the weighted degree, the sum of traffic of all valued edges of a given city, is equivalent to its total traffic.
Measured in different metric units passengers and containers , air and sea traffics have been standardized z-scores in order to be added by city and by edge, as suggested by Dumolard , p. In these representations, the positions of cities are defined by their relative position in the network thanks to a GEM-Frick calculation of topologic proximity developed in the TULIP software Auber, In accordance with our previous results, the distribution of air-sea centrality and traffic becomes less concentrated from the level of cities to the level of megalopolises see also Table 3.
The major hubs are dominantly located in Europe and Asia. We find at level of cities, the airline hubs e. Paris, London, Frankfurt, Madrid, and Amsterdam and the maritime hubs e. Singapore, Hong Kong, Rotterdam , which correspond to well-known rankings provided by official traffic statistics. Some cities, in turn, have seen their position slightly reduced e.
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Houston, Rotterdam, Istanbul, and Kuala Lumpur , probably due to their strong modal specialization in either network compared with larger and more diversified gateways. London and Paris, despite their limited port function, remain at the top of the hierarchy because of their global air hub function. Hong Kong, Shenzhen, and Guangzhou and the two large U. Vizualisation of the combined air-sea networks 3 levels.
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Concentration Gini of centrality and traffic among cities in the combined air-sea network 3 levels. The first method consists in comparing betweenness centrality scores at single and combined networks. Ratios are calculated for each city, dividing the percentage of centrality in a single network air or sea by the percentage of centrality in the combined air-sea network.