Population-level measures of perceived food access reveal barriers beyond geographic proximity
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Abstract (arXiv)
Food access is multidimensional, but population-level measurement still relies heavily on geography because perceived dimensions of access are difficult to measure at scale. Here, we use 25,125 Google Maps reviews from 49 grocery stores in Raleigh, North Carolina, to measure five dimensions of food access: availability, accessibility, affordability, accommodation, and acceptability. We identify review topics with unsupervised topic modeling and assign them to access dimensions using zero-shot classification, with 85.4% agreement against manual coding. The resulting store-level measures capture distinct aspects of food access and reveal barriers that geographic proximity alone does not capture. Comparisons between nearby stores in the same chain further show that identical store policies can be perceived very differently across locations, consistent with food access reflecting the fit between residents and their food environment. Perceived food access also follows systematic socioeconomic and demographic patterns that broadly parallel, but do not replicate, those observed for geographic access. These results show that online grocery reviews can provide a scalable complement to geographic measures of food access.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 47 references that are in the map. First posted on arXiv 10 Sep 2026.
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6 of 47 references.
References · 6
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
- 2019Topic Modeling in Embedding Spacescited by 224
- 2024A Survey on Neural Topic Models: Methods, Applications, and Challengescited by 18
- 2023Zero-Shot Multi-Label Topic Inference with Sentence Encoderscited by 6
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