DOI: 10.6084/m9.figshare.7796666
title: Area-level grocery purchases

description: For each geographic aggregation (LSOA, MSOA, Ward, Borough) we provide a file containing the aggregated information on food purchases, enriched with information coming from the census.<br>Files are comma-separated and contain 202 columns in total. Fields include:<br><b>area_id:</b> identifier of the area<br><br><b>weight: </b>Weight of the average food product, in grams<br><b>volume:</b> Volume of the average drink product, in liters<br><b>energy: </b>Nutritional energy of the average product, in kcals<br><b>energy_density:</b> Concentration of calories in the area's average product, in kcals/gram<br><b>{nutrient}:</b> Weight of {nutrient} in the average product, in grams. Possible nutrients are: carbs, sugar, fat, saturated fat, protein, fibre. The count of carbs include sugars and the count of fats includes saturated fats<br><br><b>energy_{nutrient}:</b> Amount of energy from {nutrient} in the average product, in kcals<br><b>h_nutrients_weight:</b> Diversity (entropy) of nutrients weight<br><b>h_nutrients_weight_norm:</b> Diversity (entropy) of nutrients weight, normalized in [0,1]<br><b>h_nutrients_calories:</b> Diversity (entropy) of energy from nutrients<br><b>h_nutrients_calories_norm.</b> Diversity (entropy) of energy from nutrients, normalized in [0,1]<br><b>f_{category}:</b> Fraction of products of type {category} purchased. Possible categories are: beer, dairy, eggs, fats & oils, fish, fruit & veg, grains, red meat, poultry, readymade, sauces, soft drinks, spirits, sweets, tea & coffee, water, and wine.<br><b>f_{category}_weight:</b> Fraction of total product weight given by products of type {category}<br><b>h_category:</b> Diversity (entropy) of food product categories <br><b>h_category_norm:</b> Diversity (entropy) of food product categories, normalized in [0,1]<br><b>h_category_weight:</b> Diversity (entropy) of weight of food product categories <br><b>h_category_weight_norm: </b>Diversity (entropy) of weight of food product categories, normalized in [0,1].<br><b>representativeness_norm:</b> The ratio between the number of unique customers in the area and the number of residents as measured by the census; values are min-max normalized in [0,1] across all areas<br><b>transaction_days:</b> Number of unique dates in which at least one purchase has been made by one of the residents in the area.<br><b>num_transactions:</b> Total number of products purchased by Clubcard owners who are resident in the area.<br><b>man_day: </b>Cumulative number of man-days of purchase (number of distinct days a customer has purchased something, summed all individual customers)<br><b>population:</b> Total population of residents in the area according to the 2015 census.<br><b>male:</b> Total male population in the area.<br><b>female:</b> Total female population in the area.<br><b>age_0_17:</b> Total number of residents between 0 and 17 years old<br><b>age_18_64:</b> Total number of residents between 18 and 64 years old.<br><br><b>age_65+:</b> Total number of residents aged 65 years or more.<br><b>avg_age:</b> Average age of residents according to the 2015 census<br><b>area_sq_km: </b>Surface of the area (km^2)<br><b>people_per_sq_km:</b> Population density per km^2<br>Where applicable, measures are accompanied with their standard deviation (fields with suffix <b>_std</b>), the 95% confidence interval for the mean (suffix <b>_ci95</b>), and the values of the 2.5th, 25th, 50th, 75th, and 97.5th percentiles (suffix <b>_perc{value}</b>)<br><br><br>