Frequently asked questions (FAQs)
Where does the food outlet data in Feat come from?
Feat uses food outlet data from Ordnance Survey's Points of Interest (POI) dataset. POI data contains information from over 170 suppliers, and is one of the most complete secondary sources of food outlet location data in England.1,2
Can I use Feat to see the precise locations of individual food outlets?
Can I download data from Feat?
How often is Feat updated?
After launch, Feat will be updated with new food outlet data every three months. This will ensure that Feat remains reliable and up-to-date. Furthermore, historic data will be retained within Feat, allowing you to explore change in the food environment over time.
At launch, the historic data included in Feat will be from June 2014, 2015 and 2016, with the most recent data shown from March 2017.
Why are Scotland, Wales and Northern Ireland not included in Feat?
It was not possible due to time constraints to include the rest of the United Kingdom in Feat at first launch. However, we plan to include Scotland, Wales and Northern Ireland in an updated version of Feat in the near future.
Why don’t the boundary polygon and food outlet data dates always match?
Food outlet data are updated every three months, however boundary data are usually refreshed much less frequently. For example, LSOA and MSOA data for England were last updated in 2011. However, we will always try to aggregate food outlet data within the most contemporaneous boundaries available.
Why is food outlet access calculated differently at the postcode level?
At all levels, with the exception of the postcode, food outlet access in Feat is calculated as the number of food outlets present per area. Such estimates at the postcode level would not be meaningful, as postcode boundaries are very small (typically containing only 15 addresses), are transgressed frequently, and are therefore unlikely to be related to food shopping behaviour, perceptions of neighbourhood or health outcomes. In Feat, food access at the postcode level is calculated as counts of food outlets within a 1 mile straight-line radius 'neighbourhood' buffer of the geographic centroid of each postcode boundary. In published academic research, this definition of neighbourhood has been associated with food shopping behaviours,3
diet and body weight,4-6
and this distance could be walked by an average adult in 15 minutes. Calculating food access at the postcode level in this way represents the state-of-the-art in public health science research
At postcode level, what are the small squares?
These are 'vertical streets'. Vertical streets occur where a single building contains many addresses, for example a block of flats or an office building, and more than one postcode is therefore assigned to this location. Vertical streets are represented in Feat using a small square polygon (see illustration below). If you hovering your cursor over a vertical street, postcodes contained are listed in the Current Level panel. Please note that where a vertical street contains many postcodes, not all will be listed.
Example of a vertical postcode (shown as a small square polygon), containing postcodes TF136JL and TF136QB.
Why can't I standardise by resident population at the postcode level?
Postcode level food outlet exposure in Feat are calculated as counts of food outlets within 1 mile of the geographic centroid of each postcode boundary. It is not possible using existing data to accurately estimate the resident population within these bespoke neighbourhood boundaries. It is therefore not possible to standardise food outlet counts by resident population at this level. You can still standardise by the total number of food outlets.
I'd like to be able to see postcode level data for the whole of England at one time. Is this possible?
Whichever geography you are viewing, clicking 'Lock level' will allow you to zoom out up to three zoom levels. Each time you zoom out you see more and more small areas (polygons), and therefore more and more data. For postcodes in particular (and especially in major cities), you will quickly be displaying many thousands of polygons at once, and you will probably have to wait while this information loads. At this time it is not feasible to transmit the amount of data that would be required to view any geographic level beyond three levels of zooming out.
How do I cite screenshots from Feat in a presentation and a report I'm writing?
Please use the following citation for Feat, which includes acknowledgement of our data suppliers:
Food environment assessment tool (Feat) insert year
, UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge, http://www.feat-tool.org.uk.
Leaflet | Map data © OpenStreetMap | © Crown Copyright and Database Right insert year
. OS (100059028) | Copyright and database right © 2017 CEDAR/MRC Epidemiology Unit. All rights reserved.
How can I export maps from Feat?
The best way is to enter full screen mode (F11), press print screen (PrtScn) on your keyboard, and paste into an application like Microsoft Word.
I have a question that isn't answered here, and a suggestion for how Feat could be improved, can I get in touch?
Yes. We'd really like to hear from you. We'd also love to hear how you've used Feat. Please email the Feat development team at email@example.com.
1. Ordnance Survey. Points of interest database: User guide and technical specification. 2016 [updated 18/8/16]; Available From: https://www.ordnancesurvey.co.uk/docs/user-guides/points-of-interest-user-guide.pdf.
2. Burgoine T, Harrison F. Comparing the accuracy of two secondary food environment data sources in the UK across socio-economic and urban/rural divides. International Journal of Health Geographics. 2013;12:2-8.
3. Smith G, Gidlow C, Davey R, Foster C. What is my walking neighbourhood? A pilot study of English adults' definitions of their local walking neighbourhoods. Int J Behav Nutr Phys Activ. 2010;7:DOI:10.1186/1479-5868-1187-1134.
4. Burgoine T, Forouhi NG, Griffin SJ, Wareham NJ, Monsivais P. Associations between exposure to takeaway food outlets, takeaway food consumption, and body weight in Cambridgeshire, UK: population based, cross sectional study. BMJ. 2014;348:1-10.
5. Burgoine T, Forouhi NG, Griffin SJ, Brage S, Wareham NJ, Monsivais P. Does neighborhood fast-food outlet exposure amplify inequalities in diet and obesity? A cross sectional study. Am J Clin Nutr. 2016;103:1-8.
6. Cobb LK, Appel LJ, Franco M, Jones-Smith JC, Nur A, Anderson AM. The relationship of the local food environment with obesity: a systematic review of methods, study quality, and results. Obesity. 2015;23:1331-1344.