Published April 10, 2024 | Version v1
Conference paper Open

Identifying areas of poor health outcome risk due to food insecurity – A Geographically Weighted Regression approach

  • 1. Leeds Institute for Data Analytics, University of Leeds
  • 2. Consumer Data Research Center, University of Leeds
  • 3. School of Geography, University of Leeds

Description

Food insecurity is a growing issue in the UK. However, local drivers of food insecurity risk and the impact on health is poorly understood. This paper applies geographically weighted regression to open data to quantify the spatial association between food insecurity risk, as captured by the Priority Places for Food Index - PPFI (Consumer Data Research Center, 2022), and health outcomes in England, focusing on Oxfordshire as an area with high inequalities in health. These findings are used to identify ‘priority areas’, displayed on a dashboard that enables local government, charities, and policymakers to identify areas where food insecurity is straining NHS resources the most.

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