Urban heat vulnerability mapping: working with a London Borough to translate research outputs
11 April 2018
UCL IEDE publish a report urban heat vulnerability for the Adaptation and Resilience in the Context of Change (ARCC) network.
With the frequency and severity of hot weather and heatwaves projected to increase in the UK due to climate change, there is an increased need to assess heat exposure risk and mitigation options, as well as to promote collaborations between academics, policy groups and the public that enable research outcomes to be translated into practice.
Several academic studies have investigated variations in vulnerability to heat in relation to characteristics of the individual, housing and the Urban Heat Island. However, there has been little work to date to convert these research outcomes into policy and action. Dr Anna Mavrogianni, Dr Jonathon Taylor and Dr Clive Shrubsole from UCL IEDE led a collaborative project funded by the Adaptation and Resilience in the Context of Change (ARCC) network between academics, Public Health England, the Greater London Authority and the London Borough of Hounslow. The project aimed to exchange knowledge and expertise with regard to the heat vulnerability of the population in relation to the Hounslow housing stock using outputs from existing research projects. It provided a unique opportunity to liaise with the Borough and other stakeholders to use research evidence to guide practice.
Using an existing housing stock model to predict indoor overheating, the IEDE team produced 2D and 3D animations demonstrating the temporal and spatial variation in overheating risk due to climate, housing and Urban Heat Island variations, and maps showing the spatial variation of populations vulnerable to heat, including the location of nursing homes within the Borough. A number of key lessons were learnt during the project, including the importance of engaging all key stakeholders from the outset, an understanding of the datasets and emergency planning tools held within the Borough, and how data might be best prepared to integrate into these systems.