用多模态大模型分析街景图，揭示1930年代红线政策的长期影响
From Pixels to Urban Policy-Intelligence: Recovering Legacy Effects of Redlining with a Multimodal LLM
- 先推理后估算，用GPT-4o分析街景图提取贫困与树冠覆盖率
- 结果与权威数据一致，证实红线政策导致长期社会环境不公
- 比传统像素分割方法更优，适合政策评估场景
本文展示多模态大语言模型（MLLM）如何扩展城市测量能力，支持地方政策干预效果追踪。通过在街景图像上采用结构化‘推理-估测’流程，GPT-4o 推断出社区贫困水平与树冠覆盖情况，并嵌入准实验设计以评估1930年代红线政策的遗留影响。结果表明，该方法恢复了预期的负面社会环境遗留效应，其估计值与权威数据无统计差异；同时优于基于像素的分割基线，印证了整体场景推理可提取超越物体计数的高阶信息。这些成果将MLLM定位为具备政策级精度的邻里测量工具，并推动其在更多政策评估场景中的验证。
原文摘要 · Abstract (English)
This paper shows how a multimodal large language model (MLLM) can expand urban measurement capacity and support tracking of place-based policy interventions. Using a structured, reason-then-estimate pipeline on street-view imagery, GPT-4o infers neighborhood poverty and tree canopy, which we embed in a quasi-experimental design evaluating the legacy of 1930s redlining. GPT-4o recovers the expected adverse socio-environmental legacy effects of redlining, with estimates statistically indistinguishable from authoritative sources, and it outperforms a conventional pixel-based segmentation baseline-consistent with the idea that holistic scene reasoning extracts higher-order information beyond object counts alone. These results position MLLMs as policy-grade instruments for neighborhood measurement and motivate broader validation across policy-evaluation settings.
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