In general, we found that the spring and summer months had the highest consumption of meat and animal products, including poultry, and this decreased in autumn. We also combined the Tesco 1.0 dataset with datasets from the London Datastore (a free and open data-sharing portal that provides over a thousand datasets to understand the city and develop solutions to its problems), and identified several demographic factors as predictors for the meat consumption.

Contrary to our hypothesis, areas with older, lower education, and more conservative populations had a lower proportion of meat consumed. In line with our hypotheses, a lower proportion of meat consumed could be observed in areas with higher population density, better health, and more Hindus.

Our first analysis provides descriptive data of the percentage of the total weight of food sold belonging to five animal product categories: red meat, poultry, fish, dairy, and eggs. Since the total amount of food purchased differs significantly by month, we present both the percentage of food in each category (Fig. 1) and the amount in thousands of kilograms (Fig. 2).

Figure 3 shows the proportion of food from each category for each London borough.

• On the low end, the Borough of Harrow’s groceries by weight were 19.4% animal products, and just 10.6% from meat.
• Other low-animal-product boroughs included the lowest meat-eating Borough of Newham (19.5% animal products, 10.2% meat) and Sutton (19.5% animal products, 10.9% meat).
• On the high end, animal products made up 24.7% of groceries by weight in the Borough of Kensington and Chelsea, and meat made up 13.3%.
• Other high-animal-product-consumption boroughs were Hammersmith and Fulham (24.0% animal products, 13.7% meat) and the highest meat-eating Borough of Lambeth (24.0% animal products, 14.5% meat).

The results of the linear regression on the association between various demographic factors and meat consumption are shown in Table 3. Green rows indicate a significant association with less meat consumption; red rows indicate a significant association with more meat consumption.

Several significant predictors of higher meat consumption in an area were identified, some of which confirmed our hypotheses, but some which were directly counter to our hypotheses. The hypothesized and observed results are summarised in Table 4.

As shown, six out of nine hypotheses related to demographic predictors of meat consumption were rejected. Contrary to our expectations, there was an inverse statistical effect for the influence of age, education level and conservative political views on meat consumption.

Areas with older populations, lower education populations, and more conservative political records had a lower proportion of meat consumed. In addition, no statistical significance could be found for the influence of the proportion of males and income on meat consumption.

On the other hand, there was a statistically significant effect for population density and health on meat consumption. In alignment with our expectations, a lower proportion of meat consumed could be observed in areas with higher population density and better health. With regard to the religion variables, we did observe a lower proportion of meat sold in areas with more Hindus, but observed that the opposite was true for areas with more Buddhists.

This paper explored the Tesco 1.0 Dataset for trends relevant to meat and animal product consumption. By aggregating retail data at the area, month and broad food category level, the Tesco 1.0 Dataset provides a perspective on meat and animal product consumption that is not based on self-reported intentions or behaviours. It thus addresses a key gap in existing research on reducing animal product consumption (Mathur et al. 37).

Counter to our hypothesis—poultry consumption decreased rather than increased in autumn. In fact, there was an increase in poultry consumption between winter and spring, and then a decrease between summer and autumn. However, the results make sense when that the spring and summer months saw the highest consumption of meat products. With the most dominant livestock type being poultry (Ritchie and Roser 45), it is likely that poultry consumption is highly correlated with an individual’s general meat consumption of a person.

Consumption of meat and animal products is known to be associated with a range of health problems, including obesity, diabetes, heart disease, and cancer. This report provides further evidence that reducing meat consumption is a promising way to improve health outcomes.

The statistical analysis in this study could only partly support the hypotheses.

While most research shows that vegetarians are likely to be younger (Bryant 8), the statistical regression identified older average age as a predictor of lower meat consumption. This may be explained by older people tending to eat less overall (Morley 39; Pilgrim et al. 43). It may also be a case of meat-reducers (as opposed to strict vegetarians) being more likely to be older or middle-aged (Mohr and Slich 38; Neff et al. 41). Thus higher average age may still be compatible with lower average meat consumption despite a lower rate of strict vegetarianism. Although some have argued that older or middle-aged people are more likely to over-report vegetarianism (Mohr and Slich 38), this dataset supports the idea that older age groups do indeed have somewhat lower meat consumption. Advocates should consider this when planning which groups to target for meat reduction advocacy.

Again, these associations are contrary to existing research, which generally shows that vegetarians, vegans, and meat-reducers are more likely to be more educated and left-leaning (Bryant 8; Mohr and Slich 38). These discrepancies may be related to our narrow slice of the overall food sales; we only observed the food sold in one supermarket. It might be the case that older people, for example, are more inclined to buy their meat at a specialist butcher, and less inclined to buy it at Tesco. In such a case, the analysis presented here may be limited in the generalisability of its predictions. Further research, including further analysis of the Tesco 1.0 dataset, could elucidate these findings.

An explanation for this could point to a limitation of the study. While the gender variable refers to the Tesco shoppers rather than the Open Access LSOA Atlas dataset from the London Datastore, it is possible that the gender variable does not accurately depict potential consumption differences between males and females since females may still be responsible for the majority of grocery shopping. Also, it was found that the level of income did not significantly influence meat consumption. While this was contrary to the hypotheses of this study, it supports the results of Beardsworth and Bryman (4) that there is no clear link between meat reduction and demographics correlated with income. Thus, it does not seem that areas with higher income report lower meat consumption.

Areas with a higher proportion of Jews, Muslims, Hindus, Sikhs, and atheists all had lower meat consumption, while contrary to our expectations, areas with a higher proportion of Buddhists had higher meat consumption. Many of these religions have specific rules prohibiting consumption of some meat, and their adherents may come from cultures with a high proportion of traditional vegetarian dishes. As for Buddhists, the results might reflect a higher number of individuals identifying as Buddhists without necessarily adopting the practices. Religious involvement, personal religiosity, and spirituality are still viewed as highly desirable characteristics, therefore prone to social desirability and adherence may thus be over-reported (Jones and Elliott 30). While this explanation is very hypothetical, another explanation could be the low number of Buddhists overall. With only about 2.4% of Buddhists residents in each area on average, it might not be possible to draw a strong link here.