The Problem
Child obesity is a growing issue in the United Kingdom. In order to tackle this issue, a proposition of banning fast food stores openings near schools has been voiced by the Mayor of London, Boris Johnson. This idea has been backed up by London Health Commission and a report created by the 2020health think-tank.
The correlation between proximity of fast food stores to schools is debated in the scientific research. It is estimated that children consume up to 50% of their daily food income in the vicinity of their schools. Most studies report that there is a strong relationship between the presence of food retailers near schools and students’ lunchtime eating behaviours. We set out to use the Open City Data Platform (OCDP) In order to enable parents and decision makers to observe such influence.
Data and Tools used
- Obesity levels in children calculated by Middle Super Output Areas (MSOAs).
Office of National Statistics
- Local fast food stores from Food Standards Agency
- Government Database on location of Schools – Edubase
- Adult Obesity levels from Public Health England
- Google Maps and OpenStreetMap
- Nquiringminds Open City Data Platform (OCDP)
Scenario
It was decided that enabling the user to duplicate map instances, in order to compare various areas and schools, would help in exploring the correlation of fast food locations and schools.
Using the Open City Data Platform we produced a highly visual map which identifies areas, where indeed high obesity rates correlate with high density of fast food stores near Primary Schools. The correlation is especially visible in towns and cities.
The correlation between proximity of fast food stores to schools is debated in the scientific research. It is estimated that children consume up to 50% of their daily food income in the vicinity of their schools. Most studies report that there is a strong relationship between the presence of food retailers near schools and students’ lunchtime eating behaviours.
In order to enable parents and decisionmakers to test and observe such influence, we created a map, visualising location of schools, fast food stores and obesity rates in their proximity.
The children obesity rates for Reception Year and Year 6 were used, because of their availability at the level of Middle Super Output Areas (MSOAs). These geographical regions, specified by Office for National Statistics, have an average population of 7200, enabling detailed comparison between obesity rate in a specified part of country and the number of fast food stores near schools in a given area. Because of the children obesity data obtained, Primary Schools were chosen to be presented. The relevant information about their location was obtained from EduBase. Additional information on adult obesity at the MSOA level was downloaded from Public Health England – Local Health mapping service. Finally, the information about location of all fast food stores was located at the Food Standards Agency website. In order to present schools and fast food stores on a map, their addresses had to be geocoded – converted into the standard latitude and longitude format, used by both the Google Maps and OpenStreetMap.
Further manipulation on the datasets was needed. An algorithm to locate fast food restaurants in close proximity to Primary Schools was created, using their latitude and longitude. The sheer number of schools (almost 18 thousands) and takeaway restaurants (36 thousands) would be overwhelming for the map users. Therefore, two levels of geographical boundaries were displayed.
The presented map enables visual identification of areas, where indeed high obesity rates in MSOAs correlate with high density of fast food stores near Primary Schools. The correlation is especially visible in towns and cities.
There is, however, a room for improvement. Firstly, currently the app demands the download of 9 megabytes of data. The alpha version was not fully optimised, hence further work on that area is advised. Furthermore, the correlation between obesity and nearby food stores availability may be more easily observable for Secondary Schools, as older pupils are allowed to leave the school during breaks and hence may be more susceptible to eat fast food. The panels, enabling display of multiple maps, could be made more functional by automatically adjusting width and height, so that up to four panels could fit on a one page. Finally, it would be interesting for Head Teachers and parents to see how schools in their neighbourhood compare both nationally and locally. A ranking depending on the ‘fast food density’ of each school, could be created and added to the presented information boxes
Outcome
The presented map enables visual identification of areas, where indeed high obesity rates in MSOAs correlate with high density of fast food stores near Primary Schools. The correlation is especially visible in towns and cities.