'Ris'covery: Spatial Planning from Recovery to Resilience

Research output: Contribution to conferencePosterScientific


Current approaches to spatial planning for resilience involves adding a ‘protective’ layer based on a ‘return period’ to shield the landscape from threat and/or rehabilitation to ‘bounce back’ to the pre-existing condition. Escalating manifestations of climate risk often surpass the level of protection offered by this type of approach leading to massive economic losses and reconstruction.

The shortsight in spatial decision making for climate resilience can be attributed to conflicting timelines of climate change research (100 years) ,urban planning practice (20 years) and political term (5 years) which makes long term consensus challenging. This paper looks at how we can sync the urban planning timeline with return periods of climate hazard to utilise land management as a tool to not just mitigate but better transition to adapt to intensifying risks. The principal objective is mainstreaming the role of spatial planning for risk reduction.

The method adopted is learning from urban crisis recovery patterns in space offered by critical infrastructure networks - transport, water, energy lines.

Utilising a spatial disaster event chain (informed by literature on causal chain effect, complexity theory and cascading effects), risks are projected in space. The test case is the East Bay of San Francisco which is at risk of a sea level rise by 2100 and is a hotbed of seismic activity. Risks from flooding and earthquake are projected iteratively in space to simulate obstruction in evacuation movement on road networks using ArcGIS Network Analyst. This informs the critical web of the region that forms the backbone to reconfigure land-use and densities. Understanding recovery patterns due to incremental risk on infrastructure helps derive the gradient of spatial vulnerability. A hierarchy of risk informs the adaptivity framework for resilient land parcels that absorbs intensive growth and vulnerable parcels that must transition to other functions. This defines a revised cohesive spatial morphology for the region.

In this paper, we develop a spatial planning approach to embrace uncertainties ‘proactively’ such that we allocate investment that are not solely focused on ‘reactively’ decoding the blind side of climate change. It provides the potential to bounce forward in space to better cope with a disaster which is essential for a robust environment that can contribute to resilient regional economic growth in the long run.

While the method utilises urban design as a qualitative tool , it draws from quantitative conclusions from transport, seismic and flood risk simulations. In doing so it highlights vital gaps between the two approaches and invites further interdisciplinary practitioners to refine the methodology for a more realistic implementation of long range urban planning for climate risk. It develops a ‘spatio-temporal’ scale in syncing decision making across different hierarchies of space, governance and risk. Thus, it wants to contribute towards a composite framework for better urban planning practices and a faster, more integrated decision making for implementation under deep uncertainty.
Original languageEnglish
Publication statusPublished - 2017
EventDecision Making Under Deep Uncertainty - University of Oxford, Oxford, United Kingdom
Duration: 13 Nov 201717 Nov 2017


WorkshopDecision Making Under Deep Uncertainty
Abbreviated titleDMDU
CountryUnited Kingdom
Internet address


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