Understanding, modelling and visualizing regeneration and change

Industrial and urban regeneration policies often contain a heavy focus on promoting growth.

That growth will be defined in an overly simplistic, static or short-term way if the policy makers do not consider that industries and industrial regions are complex adaptive systems, much like any natural ecosystem.

Our aims

Digger at a rubbish tip, surrounded by seagulls.

Urban and industrial areas may be more like complex ecosystems than they are like machines, with interdependent economic, social and environmental costs and benefits. As a consequence, they may not respond well to the growth-centric regeneration policies derived from simple or mechanistic system. This project explores how the complexity and dynamics of urban and industrial ecosystems can be understood and embraced in order to create regeneration policies that are more likely to promote long-term resilience.

Within an ecosystem approach, regeneration policies would see change as both inevitable and as an opportunity for innovation, new industry growth and other forms of adaptation.

This project comprises several different innovative methodological approaches to two case studies the industrialised regions of Greater Manchester and Sheffield. Each of these case studies involves:

  • Statistical analysis of population, employment and air quality over the period of 1096 to 2010,
  • An in-depth analysis of academic literature covering regeneration associated with mega-events, transport infrastructure, shopping centres and more,
  • An automated, machine-learning analysis of news archives of the same mega-events, transport infrastructure, shopping centres and more, and
  • Two novel graphs modeling the events and event sequences, one derived from the literature analysis and the other from the automated news archive analysis.

The results are to be presented at a series of workshops, one focusing on the methods used and the other focusing on the insights derived from the analyses.

Project leaders