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.
Dunn, S., Wilkinson, S., & Ford, A. (2016). Spatial structure and evolution of infrastructure networks. Sustainable Cities and Society, 27, 23–31. https://doi.org/10.1016/j.scs.2016.08.011