Detecting and Evaluating Change in the Built Environment

View of a building site in Liverpool, UK. Several different modern building frames of different shapes and a tower crane in the background. 1400 pixels

The UK has some of the best longitudinal data about people’s lives and their health in the world and these have proved incredibly useful in understanding how health and health inequalities are created over time, and how change in people’s individual circumstances can affect their chances of good health. However, in order to understand the role of environment in protecting or harming health, we need longitudinal data on environment which we could join to these data on individuals. Some environmental characteristics – for example air pollution - are well-captured over time, but there is a particular gap in data about the built environment, facilities and infrastructure.

Our workstream is focused on retrospectively creating longitudinal measures of neighbourhood built and social environment, for very large numbers of towns, cities and settlements. To begin this task we are working with Ordnance Survey map data from Digimap. These maps are regularly updated, so they are able to provide a picture of how our built environment changes year to year. Since these maps are digital, we can use computers to make these comparisons. However, the data sets are very large, and creating techniques which efficiently spot changes in built environment that might matter for health is a complex task. Our aim is to identify such changes in very large numbers of towns and cities, over the longest time period possible. We will then join this information on environmental change to data about health and behaviour, asking if and how they have been affected. 

We are working on changes over 5 to 10 years. Our colleagues at CRESH, who work with computer scans of old paper maps, or examining changes over longer periods of time.

We will use data from existing studies and projects to explore built environment change. Our projects include THAW, M74 and WIAT.

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