用可解释AI分析城市文化设施分布不均，揭示核心与边缘差异
From Hubs to Deserts: Urban Cultural Accessibility Patterns with Explainable AI
- 基于指数衰减模型计算文化设施可达性得分
- 非图书馆设施集中于城区核心，图书馆覆盖更广但收入越高的区域越优
- 适合城市规划、公共政策研究者参考
文化基础设施（如图书馆、博物馆、剧院、美术馆）支撑学习、公民生活、健康和地方经济，但城市间可达性不均。本文提出一种新颖、可扩展且基于开放数据的框架，用于衡量文化设施的空间公平性。通过精细空间分辨率的指数距离衰减模型，映射文化设施并计算文化基础设施可达性评分（CIAS），再按人均聚合并整合社会人口指标。采用可解释的树集成模型与SHapley加性解释（SHAP）分析可达性与收入、密度及街区种族/族裔构成之间的关联。结果显示显著的核心-外围梯度：非图书馆类文化设施聚集于城市核心区，而图书馆随人口密度分布，覆盖范围更广。非图书馆可达性在高收入街区略高，图书馆可达性在人口密集、低收入区域稍优。
原文摘要 · Abstract (English)
Cultural infrastructures, such as libraries, museums, theaters, and galleries, support learning, civic life, health, and local economies, yet access is uneven across cities. We present a novel, scalable, and open-data framework to measure spatial equity in cultural access. We map cultural infrastructures and compute a metric called Cultural Infrastructure Accessibility Score (CIAS) using exponential distance decay at fine spatial resolution, then aggregate the score per capita and integrate socio-demographic indicators. Interpretable tree-ensemble models with SHapley Additive exPlanation (SHAP) are used to explain associations between accessibility, income, density, and tract-level racial/ethnic composition. Results show a pronounced core-periphery gradient, where non-library cultural infrastructures cluster near urban cores, while libraries track density and provide broader coverage. Non-library accessibility is modestly higher in higher-income tracts, and library accessibility is slightly higher in denser, lower-income areas.
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