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Food Desert Metrics
Like their physical counterparts, food deserts may be said to have both extent and intensity.
Their extent is the physical area they cover – that is, the area of urban or rural settlement
where difficulties of access to fresh fruit and vegetables may be experienced.
Their intensity is a measure of how many people within the food desert area actually face
major difficulties in accessing a healthy diet. Some food desert areas may be quite extensive,
in that large tracts of residential land in cities, or large urban areas, are devoid of fresh fruit
and vegetable shops. However, for some of these extensive food desert areas, the majority of
the population may be relatively affluent and have cars, and possess good knowledge of what
constitutes a healthy diet, they have the time to cook and prepare such foods. In other areas,
quite a small area, such as a single edge of town housing estate, may constitute a ‘food
desert’; however if a large majority of the households on that estate face barriers to healthy
eating this is an intense food desert.
Measuring the extent and intensity of food deserts can be done in several ways.
The first type of analysis involves measuring the distance between fresh fruit and vegetable
outlets and residential areas.
The second type of analysis extends the study of food deserts to include ‘psychological
distance’, ‘financial distance’, and ‘psychological distance’..
The third type of analysis extends the basic spatial quantification to include socio-economic
factors that have an impact on healthy eating even though these factors are not of themselves
distance-dependent.
The fourth type of analysis involves a more sophisticated spatial breakdown of the sociodemographic factors, investigating the effects of these factors across selected neighbourhood
types, using a kind of ‘social tomography’, rather than the whole study area.
These four analysis methodologies are more fully described below.
1) Distance between shops and residential areas
The density of all retail food shops, including fresh fruit and vegetable shops, tends to decline
as one moves outwards from the historic town or city centre, beyond the early suburbs built
ca. 1800 – 1914. In large conurbations, former village centres now swallowed up by
suburban sprawl will generally retain a neighbourhood shopping centre in the former village
centre. The large supermarkets will often prefer a location out on the urban periphery, where
main roads and the urban ring road offer good transport links for bringing in both retail goods
and customers. At fooddeserts,org we have developed a methodology based on the 250 x
250 metre grid squares of the food retailing maps hosted on this website that facilitates the
classification of an urban residential area, square by square, according to how far each square
is from its nearest fresh fruit and vegetable retailer.
The results for Greater Birmingham are shown here.
Click here for a map of distance to fruit and vegetable shops in Nantes, France
Note that whilst each square is 250 x 250 metres, shoppers do not travel in straight lines to
the shops but must use the local street pattern. Therefore each square distance represents
some 300 metres average travel distance house-to-shops for that square. Areas 7 squares out
are then some 2,100 metres from the nearest fruit and vegetable shop.
Note that this does not mean that residents of such ‘distant’ areas will have difficulties
accessing a healthy diet as they may well have access to a private household car. Equally,
residents of areas close to fruit and vegetable shops do not necessarily find it easy to access
the fruit and vegetables they would like to eat; if an older White less affluent pensioner is
living in an area with a large south Asian ethnic monitory, e.g. Sparkhill in Birmingham, this
pensioner may be faced with most local stores selling fruit and vegetables she is unfamiliar
with and has no idea how to prepare.
2) Changes over time
Food retailing does not remain static over time. Supermarkets may open, and some close.
Closures may occur because the chain has gone into liquidation (e.g. Kwik Save). In other
cases a smaller store is closed because a new,bigger, store, with more customer choice and
more economies of sale, has been built nearby. ‘Nearby’ may mean a few hunderd metres
away, meaning that some customers of the old store,if they don’t have access to a private car,
see a major reduction in their food acessibility.
Smaller independent shops may start, or stop, stocking fresh fruit and vegetables, depending
on the level of demand. Such shops cannot afford to see profits ‘rot away’ as fresh produce
goes off and become unsaleable. On the other hand, some small shops keep a minimal level
of hard, longer-lasting, vegetables such as carrots in stock, and of they deteriorate too much
to sell, will ‘take them upstairs’ (to the flat above the shop) and use for personal
consumption, e.g. in stews and casseroles.
Click here for a map of changing accessibility to fresh fruit and vegetable retailing in
Scunthorpe, UK.
3) Psychological, financial, and physiological distance to shops
Just because a certain food store is, say, 600 metres from a residential area does not mean that
all shoppers from this residential area will experience the same effort in getting there. For
example the store may be located in a valley and the houses are further uphill. Younger, less
affluent, and car-less shoppers on foot may have little difficulty carrying their shopping home
but this could be a daunting task for a pensioner. We can say that the presence of the uphill
section on the way home has increased the physiological distance the pensioner must travel
to/from the shop, as opposed to if the journey was all on the level. Effectively, a store that is
600 metres away but 30 metres below the level of the houses might be the equivalent of 900
metres flat-travel distance for a frail pensioner. Other such physiological barriers might
include a busy road crossing for a mother with children, or a main road that can only be
crossed by a pedestrian subway where there is a risk of mugging. Effective psychological
distance may even vary with the time of day and season; shops that are 500 metres away
across a park may be easy to get to for a woman in the daytime, but on dark winter evenings
she may be cautious of using this route and prefer a longer route via well-lit roads.. At
fooddeserts.org we have developed a methodology to indicate, once such physiological
barriers have been determined by shopper-interviews or other research, the ‘true-
physiological-distance’ to the shops, as opposed to the 2-dimensional flat distance as shown
on a map.
Some features actually reduce physiological distances. The presence of a frequent bus route
may bring stores effectively closer for car-less households. Certain stores, or store brands,
may also attract or repel shoppers, which effectively reduces or increases the psychological
distance of the store. Take for example a thrifty pensioner, who dislikes very large
hypermarkets because they find them confusing and it’s a long walk to the tills; however this
pensioner likes the cheaper discount stores Aldi, Lidl, and Netto. For this pensioner, the
actual (map) distance to their nearest Tesco Extra and Lidl could be 700 metres and 1,000
metres respectively. But the psychological preferences might make the pensioner shop as if
the Tesco was 1,200 metres away and the Lidl, 500 metres.
However for the unemployed a return bus journey of around £3.00 represents a large slice of
their disposable income and in the absence of subsidised fares for the jobless this cost will
increase the ‘financial distance ‘they face in travelling to the shops and predispose them to
shop at more local shops, where often only unhealthy foods such as burgers and takeaways
are available.
At fooddeserts.org we have developed an algorithm that can modify actual-map distances
for different store types, or for features such as bus routes and hills, to produce
psychological-distance maps for various consumer types, with various shop preferences or
travel capabilities.
4) Socio-economic factors
A range of socio-economic factors have been linked to the propensity to consume a poor diet,
and to become obese. These indicators range from a low level of educational achievement
and suffering ill health / being a carer, to unemployment and lack of a household car.
Research for fooddeserts.org in Birmingham, Leeds, London, and other cities suggests that
level of educational achievement is a strong indicator of dietary quality and obesity. Some
socio-demographic factors are linked in complex ways. For example obesity tends to
increase with age, especially for poorer people; there is also an ethnic link in that younger
less-affluent Asians tend to be less obese than young less-affluent Whites, and there is also a
positive correlation between poverty and obesity.
When distance-to-shops measures are combined with socio-economic factors some
interesting relationships emerge. There is evidence, at least for some large urban areas, that
the tendency to be obese increases with distance from shops in less-affluent areas, but falls
off with increasing distance from shops in better off areas. Conversely, and perhaps even
more counter-intuitively, whilst higher levels of unemployment are positively associated with
obesity and poor diet in more affluent areas, being unemployed in a less-affluent area may
actually predispose to a healthier diet. The reason for this may be that in affluent areas, the
wealthier tend to live in large houses distant from shops (and have cars); the poor may live
closer to the shops but have less income to spend on healthy food, and in these affluent areas
do not have access to cheap markets. In less affluent areas the working poor have less time to
prepare healthy food, whereas the unemployed (on not so much less disposable income than
that possessed by a working poor person on the Minimum Wage) have time to seek out
cheaper food bargains and to cook.
By combining spatial shop-distance data with census area or postcode-based data a full
picture can be built up of the interplays between spatial and socio-economic factors in
influencing quality of diet and obesity.
5) Spatial breakdown of socio-economic factors affecting food access, diet, and obesity
A more complex level of analysis involves a systematic analysis of partial areas selected for
by degree of social deprivation. There are a large number of ways in which urban or rural
neighbourhoods can be selected in order to deliberately obtain ‘biased samples’ of such areas.
Systematic analysis of a series of such areas as done at fooddeserts.org is used to produce a
kind of ‘social tomography’ along multiple dimensions of social and economic deprivation.
More information can be extracted from the manner in which the closeness of fit between
various indicators changes as one moves across the social spectrum, from e.g. rich to poor. N
example of this is that in poorer parts of Birmingham, the propensity to obesity associated
with having no car is similar to that associated with being on Income Support; however in
wealthier areas the factor ‘Income Support’ is still associated with obesity whereas ‘no car’ is
not. A possible explanation may be that in wealthy areas many older people have no car due
to health, not poverty, reasons; these people can still afford a healthy diet, and can pay for a
taxi to the nearest Waitrose to get these foods.
Further ‘dimensions ‘may be added by comparison with different areas, or with the same area
as it changes over time. Areas such as Hall Green in Birmingham and Armley in Leeds have
seen a shift in ethnicity over the past decade, and a change in the type of fresh fruit and
vegetable retailing on offer; this will alter the interactions between age, ethnicity, poverty,
and obesity.
Social tomography of food deserts produces a shifting pattern of links between social
deprivation and diet; underlying social principles are overlaid by a set of social circumstances
distinctive to each rural or urban area. These factors determine the most appropriate food
policies for an area. For example a poor area with many low skilled and low paid jobs may
require an intervention aimed at introducing easy and quick to prepare healthier foods, e.g.
pasts, fish, and vegetables, rather than more complex nutritious dishes that require
considerable time to cook. At fooddeserts.org our socio-spatial analysis not only elicits the
set of dietary deprivation circumstances that are unique to each neighbourhood, but can also
suggest the most cost-effective social intervention that would improve diet and reduce obesity
for that area.
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