Abstract
Critical infrastructure systems are essential components of a nation's assets. They support economic development, enable growth and help to protect against hazard. In recent years, the demands placed upon these systems have been rapidly increasing, partly due to the shift from rural to urban living and partly due to increasing wealth. For example, in 1960, only 34% of the world's population lived in cities whereas, in 2014, this figure had risen rapidly to 54%. This shift has also caused a massive explosion in urban infrastructure systems and therefore a proportionately greater risk to social cohesion through the potential loss of critical infrastructure due to natural or manmade hazard. While it is possible to model the performance of these systems, the complexity of them makes it difficult to assess their contribution to economic development or their resilience to hazard. This deficiency stems from our inability to identify key generic features that would enable us to simplify the task and hence conduct probabilistic assessments or to recognise the underlying drivers that govern their evolution and thus enable us to make robust future decisions. In this paper we present an algorithm that can generate spatial nodal layouts which share a number of non-trivial features common to several types of real world networks. The synthetic networks generated by the algorithm can be used in planning studies to see how infrastructure may evolve in the future, considering alternate planning or policy scenarios for example, or in other scenario based assessments, such as hazard tolerance studies.
Highlights
In our modern society, cities are one of the main the key factors in the development of a country’s social prosperity and economic growth
One approach to the analysis of these infrastructure systems has been through the application of complex networks, or network graph theory, models (Dunn, Fu, Wilkinson, & Dawson, 2013; Wilkinson, Dunn, & Ma, 2011)
Comparing the spatial distribution to that of the actual European air traffic network (EATN) and China air traffic network (CATN) shows that the generated nodal layouts are a good proxy for that of the real world networks
Summary
Cities are one of the main the key factors in the development of a country’s social prosperity and economic growth. The models require the initial input of a number of layers of data describing the initial conditions in the study area, which are updated as the model runs: (1) digital elevation of the study area, (2) the location of the initial settlements, (3) historical transportation layers (e.g. road network) and (4) a layer showing excluded areas (e.g. national parks, water bodies, etc.) This data is gathered from historical maps, air photos and digital maps; and as the data is obtained from a variety of sources there are often problems with assembling the dataset, including: inconsistent dimensions of features, generalisation in historical maps, different projections of the study area and different coordinate systems. We demonstrate how our model can be used in future research to consider how different drivers (e.g. population density, city location or policy) can impact upon the resulting spatial characteristics of real world systems
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