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Prioritising food establishment inspections

Prioritising food establishment inspections

Populations who frequently eat fast food and live within close proximity of unhygienic food establishments may be at higher risk of contracting foodborne illness than those who do not eat takeaways regularly – but which food establishments are most likely to be unhygienic?

Recent research by CDRC PhD student Rachel Oldroyd uses logistic regression to identify ecological determinants of non-compliant food outlets in England and Wales.  Rachel’s recent paper in Health & Place highlighted:

  • A clear gradient of association is observed between increased deprivation and the probability of non-compliance.
  • Food outlets in the most deprived areas are 25% less likely (OR = 0.75) to meet hygiene standards than those in the least deprived areas.
  • Takeaways, sandwich shops (OR = 0.504) and small convenience retailers (OR = 0.905) are less likely to be compliant than restaurants.
  • Food outlets in large conurbation areas are less likely (OR = 0.678) to meet hygiene standards than those located in cities and towns.
  • Outlets in deprived and urban areas, especially takeaways, sandwich shops and convenience stores should be prioritised for inspection.

You can read the full paper here.

Demographic profile of pedestrian flows: linking Huq mobile geo-data and SmartStreetSensor footfall data

Birds eye view of a crowd of people on street

Demographic profile of pedestrian flows: linking Huq mobile geo-data and SmartStreetSensor footfall data

Recent research conducted by Consumer Data Research Centre (CDRC) at UCL discussed the potentialities and limitations of football data and mobile geo-data in the context of retail location analysis.

Footfall data derived through SmartStreetSensor project is collected using a network of sensors. This data provides information about the volumes of pedestrian flows, but lacks the contextual insights on the origins of the population.

The geo-data provided by Huq Industries captures around 6% of the average footfall, but maintains a continuous list of locations visited by the users over long periods of time. This information can be used to study the demographics and consumer behaviours of the population.

The preliminary research summarised in this case study sugges linking the contextual information derived from geo-data to footfall counts in order to create a comprehensive understanding of the magnitudes and demographic profile of the pedestrian flows in the retail centres.

Please find the full report here