Rapid indicators of deprivation using grocery shopping data

Area of Science:

• Socio-economic indicator measurement
• Urban deprivation analysis
• Consumer behavior data

Background:

• Traditional socio-economic indicator measurement is time-consuming and expensive, limiting policy development.
• High temporal frequency measurement of inequalities and quality of life is needed.
• Secondary data sources offer potential for rapid socio-economic assessments.

Purpose of the Study:

• To investigate the utility of grocery shopping data for rapid deprivation estimation in London.
• To establish a relationship between consumer purchasing habits and area deprivation levels.
• To explore the potential for timely socio-economic insights using novel data streams.

Main Methods:

• Utilized secondary data derived from grocery shopping habits.
• Analyzed the correlation between shopping data and socio-economic deprivation indicators across London areas.
• Developed a model for generating rapid deprivation estimates.

Main Results:

• A significant relationship was identified between grocery shopping data and area deprivation in London.
• Grocery shopping data can be used to generate quick estimates of deprivation.
• The method allows for rapid data generation, enabling early access to deprivation insights.

Conclusions:

• Secondary data from grocery shopping provides accurate information on collective behavior and societal conditions.
• Novel data streams, like consumer habits, can supplement traditional methods for socio-economic monitoring.
• This approach offers a faster, more frequent alternative for assessing urban deprivation and informing policy.