Mapping Regional Disparities in Discounted Grocery Products

2025-10-31

AI Summary

This study investigates how geographic context influences discounting practices for near-expiry food at the retail level and examines their implications for food waste reduction and inventory management. Leveraging multi-city chain supermarket data across China, we integrate double clustering, street-network shortest-path modeling, product co-occurrence clustering, and bipartite network percolation threshold analysis to identify three spatially distinct regional store clusters and quantify their degree of spatial isolation. Results reveal that rural stores apply near-expiry discounts on meat and dairy products 2.2 times more frequently than urban counterparts, whereas urban stores prioritize highly processed, convenience-oriented items—highlighting a regional trade-off between nutritional health and environmental sustainability. The findings provide empirical evidence and methodological foundations for designing geographically tailored, differentiated policies for near-expiry food management and food waste mitigation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Food waste represents a major challenge to global climate resilience, accounting for almost 10% of annual greenhouse gas emissions. The retail sector is a critical player, mediating product flows between producers and consumers, where supply chain inefficiencies can shape which items are put on sale. Yet how these dynamics vary across geographic contexts remains largely unexplored. Here, we analyze data from Denmark's largest retail group on near-expiry products put on sale. We uncover the geospatial variations using a dual-clustering approach. We identify multi-scale spatial relationships in retail organization by correlating store clustering -- measured using shortest-path distances along the street network -- with product clustering based on promotion co-occurrence patterns. Using a bipartite network approach, we identify three regional store clusters, and use percolation thresholds to corroborate the scale of their spatial separation. We find that stores in rural communities put meat and dairy products on sale up to 2.2 times more frequently than metropolitan areas. In contrast, we find that metropolitan and capital regions lean toward convenience products, which have more balanced nutritional profiles but less favorable environmental impacts. By linking geographic context to retail inventory, we provide evidence that reducing food waste requires interventions tailored to local retail dynamics, highlighting the importance of region-specific sustainability strategies.

Problem

• Mapping geospatial variations in discounted grocery products
• Analyzing regional disparities in near-expiry food promotions
• Identifying location-specific retail dynamics affecting food waste

Innovation

• Dual-clustering approach reveals geospatial variations
• Bipartite network identifies three regional store clusters
• Percolation thresholds corroborate spatial separation scale