Rapid indicators of deprivation using grocery shopping data

Abstract

Measuring socio-economic indicators is a crucial task for policy makers who need to develop and implement policies aimed at reducing inequalities and improving the quality of life. However, traditionally this is a time-consuming and expensive task, which therefore cannot be carried out with high temporal frequency. Here, we investigate whether secondary data generated from our grocery shopping habits can be used to generate rapid estimates of deprivation in the city of London in the UK. We show the existence of a relationship between our grocery shopping data and the deprivation of different areas in London, and how we can use grocery shopping data to generate quick estimates of deprivation, albeit with some limitations. Crucially, our estimates can be generated very rapidly with the data used in our analysis, thus opening up the opportunity of having early access to estimates of deprivation. Our findings provide further evidence that new data streams contain accurate information about our collective behaviour and the current state of our society.

Changes in the time-space dimension of human mobility during the COVID-19 pandemic

“…Moreover, the type of occupation, among other socio-demographic characteristics, also influences those patterns 75 . In this sense, we use the English National Statistics Socio-economic classification (NS-SEC) 6 to gauge the response of employment relations and conditions of occupations to the pandemic. Using this classification, we also have insights into the connection between the spatio-temporal mobility and wealth level.…”

“…The places we visit [1][2][3] , the products we purchase [4][5][6] , the people we interact with [7][8][9] , among others activities, produce digital records of our daily activities. Once decoded and analysed, this digital fingerprint provides a new ground to portray urban dynamics 10,11 .…”

COVID-19 is linked to changes in the time–space dimension of human mobility

“…The places we visit [1][2][3] , the products we purchase [4][5][6] and the people we interact with [7][8][9] , among other activities, produce digital records of our daily activities. Once decoded and analysed, this digital fingerprint provides a new ground to portray urban dynamics 10,11 .…”