用街景图像预测城市食物不安全，为规划者提供新工具
Modeling Urban Food Insecurity with Google Street View Images
- 通过街景图提取特征并加权聚合，构建城市食物不安全模型
- 在普查区层级上实现可解释的预测，虽精度略低但具实用潜力
- 适合城市规划、公共政策制定者参考，补充传统调查方法
食物不安全是全球众多城市面临的重大社会与公共健康问题。现有识别方法主要依赖难于扩展的定性与定量调查数据。本研究探索使用街景图像在普查区层级建模食物不安全的可行性。提出两步法：特征提取与门控注意力图像聚合。通过对比不同模型架构、分析学习权重及案例研究评估模型效果。尽管预测性能略逊于基准，但该方法仍具备补充现有识别手段的潜力，可为城市规划者与政策制定者提供支持。
原文摘要 · Abstract (English)
Food insecurity is a significant social and public health issue that plagues many urban metropolitan areas around the world. Existing approaches to identifying food insecurity rely primarily on qualitative and quantitative survey data, which is difficult to scale. This project seeks to explore the effectiveness of using street-level images in modeling food insecurity at the census tract level. To do so, we propose a two-step process of feature extraction and gated attention for image aggregation. We evaluate the effectiveness of our model by comparing against other model architectures, interpreting our learned weights, and performing a case study. While our model falls slightly short in terms of its predictive power, we believe our approach still has the potential to supplement existing methods of identifying food insecurity for urban planners and policymakers.
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