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Layers of data

This was the first meeting in a series of three focusing on cities and we started to explore new ways to bring together data sets from different sources and to build models to assess both risk and resilience in cities and to formulate responses.

Our first witness was Professor James Jackson, Professor of Geophysics, Geodynamics and Tectonics and Head of the Department Earth Sciences.  He joined Dr Elisabete Silva, a Senior Lecturer in Planning in the Department of Land Economy, and Professor Danny Ralph, Professor of Operations Research and Academic Director of the Centre for Risk Studies (CRS) in the Judge Business School.

Wicked problems and questions generated by the open discussion

Society as a whole needs to decide in a transparent manner what is considered an acceptable or optimal level of risk, as not all risk can be mitigated. As things stand, redistribution after a disaster is favoured over mitigation beforehand, and this balance needs to be shifted. Engagement with public and private mechanisms is crucial for this process.

How can we make planning systems more adaptive? Big data combined with dynamic data means that modelling scenarios are constantly being updated, but often planning systems are not flexible enough to incorporate these changes without substantial delays. Perhaps a more flexible system incorporating certain milestones will allow dynamic models to be fully utilised.

How can knowledge about risk and resilience be shared at an urban planning level? Cities and institutions are not effective at learning from each other. Risks such as telecommunication issues after a disaster or air traffic control issues in cities with central airports are entirely predictable but experience is not effectively imparted to other decision makers.

What is the role of insurance in creating resilience? The population is generally not fully aware of risk which can lead to complacency when rebuilding or creating mitigation measures. Planning is crucial to increasing resilience, but integrated development plans are not always put into practice. The insurance industry and the development process need better cohesion at government and developer or constructor levels. Social insurance as opposed to private insurance is also a possibility that should be considered.  A related question is how can we overcome short-term timeframes? Insurance policy and modelling practices or government election cycles can mean long-term resilience is overlooked.

How can we increase levels of community trust and cohesion at all levels of society? A collective response helps a community cope with a disaster. Additionally, in terms of mitigation strategies, the public need to understand and trust decisions concerning when an area can or cannot be protected from disaster on account of cost or resources.

What is the relationship between different catastrophes? Having two successive 1 in 50 year events may increase or decrease the overall effect of the catastrophe and the relationship between events needs further modelling. 

How can we model direct and indirect effects of catastrophes on areas outside the original impact centre? For example, the Icelandic volcano, Eyjafjallajökull, affected air transport across Europe and a pandemic, war or economic crisis would have wide-ranging impacts. This introduces more complexity and uncertainty into a model.

How much redundancy or resilience should be built into infrastructure? There is a balance between added cost versus the reduction in risk. Unexpected shocks to a system can have an overwhelming effect as properly implemented engineering construction usually performs well when dealing with known risk, as opposed to unforeseen events. Where the consequences of disaster are high or functionality will be needed post-disaster, such as in a nuclear power plant or hospital, it is preferable to overdesign buildings. However, overdoing this approach can have unintended consequences; for example, too much rigidity in a building affected by an earthquake may cause such internal damage that the building is rendered dysfunctional.

How do we introduce redundancy into social systems? Redundancy in physical systems is relatively easy to model. But incorporating elasticity into socio-economic systems is more challenging and often overlooked. Expecting logical behaviour from individuals in a crisis is unrealistic. Thus, better preparation on the behalf of planners is needed so that physical resilience measures are used appropriately.

To find out more about this theme or the meetings, please e-mail Dr Konstantina Stamati (ks712@cam.ac.uk)

Using a new metric, ‘GDP @Risk’, the ‘Catastronomics’ techniques developed by researchers at the University of Cambridge reveal that major threats to the world’s most important cities could reduce economic output by some $4.56 trillion over the next decade as a result of natural or man-made catastrophes.

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