The UK has some of the best longitudinal data about people’s lives and their health in the world (e.g. https://www.ukdataservice.ac.uk/get-data/key-data/cohort-and-longitudinal-studies ) 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 really need longitudinal data on environment as well which we could join to these data on individuals. Some environmental characteristics such as air pollution, are quite well captured over time but there is a particular gap in data about the built environment, facilities and infrastructure. The data displayed in this app are a product of this project which focused on retrospectively creating longitudinal measures of neighbourhood built and social environment, for very large numbers of towns, cities and settlements. To undertake this task we have been working with Ordnance Survey map data from Digimap . These maps are regularly updated, so can provide a picture of how our built environment changes year to year by comparing one year’s map to the previous years. We have identified changes in very large numbers of towns and cities, over a relatively short period of time (1 year), and hope to look at change over longer periods (5 to 10 years). We will then join this information on environmental change to data about health and behaviour, asking the extent to which they have been affected. Our colleagues at CRESH are doing something similar, but over much longer periods of time.
OS Open Map Local (OML) data, and Integrated Transport Network (ITN) Urban Paths data were downloaded from EDINA Digimap for Scotland for October 2016 and October 2017. OML is a detailed street-level open data vector mapping product including buildings, roads, woodland etc.. A grid was created for Scotland containing 500m2 cells. For built environment change between time points, grid cells were categorised according to loss, gain, or no change, and by low or high level change for each of the chosen features (i.e. buildings, roads and woodland).
For more information about the project please contact email@example.com
Several option boxes are located to the left – 'Local Authorities', 'Feature', and 'Change'. In the 'Local Authorities' option box you can choose from one to five of the 32 local authorities, or the whole of Scotland. If you do not know your local authority, supply your postcode here. By specifying the location of the map in this way you will then be presented with the default options for the data- Feature="Buildings", Change="Loss/no change/gain". You are then able to change these options to dynamically display the data in different ways (e.g. view other features cycle paths, roads, woodland in terms of loss/no change/gain or levels of change (i.e. low or high)). For full information on the options see the 'About' tab. If you get stuck whilst using the application then hover over the option boxes with your cursor, to get some helpful tips instantly.
Understanding the map
When you have selected the geography and data type then you can explore the map using the map options in the corners of the map. In the bottom left hand corner is the scale bar which will change dynamically as you zoom. In the right hand corner the legend shows the colour coding for change in terms of loss and gain in the chosen feature. In the top left hand corner is the + and - zoom options, so that you can pan around the map to different areas. Below these is the full screen option, which we recommend using for taking screenshots of the maps. On the top right of the map is the basemap options, this includes some well-known layers including CartoDB Positron designed by Stamen, Toner designed by Stamen and Open Street Map (OSM). Hovering over a cell will turn the boundary black. By clicking a cell a text box will appear containing information on feature counts for the two time points.
Feedback from the CRESH team and input from Laura Macdonald (MRC/CSO SPHSU) facilitated the development of the app. The app was built in Rstudio (0.98.507) with support from the R user community. In particular the guides written by Dean Attali were invaluable. We acknowledge that the data on features of the built environment was supplied by EDINA Digimap. We would also like to acknowledge the support from The University of Glasgow, and The University of Edinburgh.