Cities have been in continuous existence for over 10,000 years and their importance has only grown with the rapid urbanisation of the past few decades. Cities act as hubs that combine financial, real-estate, cultural, political, and knowledge services, as well as critical infrastructure systems. Understanding this complex interplay is therefore of great importance and mathematical models are an excellent way to tackle the challenge.
Explaining the science
There are numerous factors and approaches involved in urban analytics:
Resilience – The capacity of individuals, communities, institutions, businesses, and systems within a city to survive, adapt, and grow no matter what kinds of stresses and shocks they experience.
Entropy – The degree of disorder in a system. Entropy-based methodologies can model various aspects of urban complexity, including settlement patterns and morphology, and population migration and growth.
Scaling laws – There are many of these that apply to cities. Examples being: as cities grow, the number of ‘potential connections’ increases as the square of the population, and as cities grow, the ‘density’ in their central cores tends to increase whilst falling in their peripheries.
Community structure – The occurrence of groups of nodes in a network that are more densely connected internally than with the rest of the network, e.g. neighbourhoods.
Data-driven models of cities – Huge amounts of data are produced by the various activities and infrastructure in a city and this can be used to model and predict urban behaviour.
This interest group seeks to apply mathematical models to better understand the science of cities.
The application areas of interest to the group include, but are not limited to: prediction of urban conflict, terrorism and riots, urban economic trade projections, and urban planning.