面向宽带接入差距的可解释机器学习：普查 tract 级预测与基于 SHAP 的因素分析
Explainable Machine Learning for Broadband Adoption Disparities: Tract-Level Prediction and SHAP-Based Factor Profiling
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中文总结 AI 辅助
该研究提出可解释机器学习框架，以普查 tract 为单位预测美国宽带接入差距，通过LightGBM与SHAP分析识别关键影响因素，筛选精度优于仅用收入的方法，时间稳定性良好。
中文摘要 AI 辅助
美国已通过《基础设施投资和就业法案》拨款约650亿美元用于宽带扩展，但针对这些投资的循证方法仍有待开发。本文提出一种可解释机器学习框架，用于分析美国全国83359个普查 tract 层面的宽带接入差距。利用2022年美国社区调查（ACS）的65个社会经济、人口统计和基础设施特征，我们在空间五折交叉验证下训练LightGBM模型，获得R²=0.533、斯皮尔曼相关系数rho=0.763；州保留交叉验证（51折）证实模型具有泛化性（R²=0.525）。TreeSHAP分析确定收入和教育是主导因素组（工程化交互项吸收了其组成特征的归因），基于SHAP的聚类揭示了三种探索性因素概况：连接良好的中等水平（约4.9万个tract）、可负担性受限的严重水平（约2.1万个tract）、农村老年群体（约1.3万个tract）。作为筛选工具，基于机器学习的tract选择在前10%的tract中捕获了38.0%的总接入差距，而仅使用收入的启发式方法为35.2%（提升2.8个百分点，p<0.002，县-块自举检验）；在遗憾减少方面，该模型缩小了仅使用收入与最优选择之间剩余差距的19%。主要贡献是tract层面的因素分解：SHAP确定哪些特征组（收入/教育、农村性、年龄）与每个tract的预测差距最密切相关，并为差异化调查提供依据。时间稳定性检验（用2017年ACS训练，预测2022年ACS且无调查年份重叠）证实排名稳定性（rho=0.784，注意超参数在2022年数据上调优）。
英文摘要
The United States has allocated approximately $65 billion through the Infrastructure Investment and Jobs Act for broadband expansion, yet evidence-based methods for targeting these investments remain underdeveloped. This paper presents an explainable machine learning framework for profiling broadband adoption disparities at census-tract granularity across 83,359 tracts nationwide. Using 65 socioeconomic, demographic, and infrastructure features derived from the American Community Survey 2022, we train a LightGBM model under spatial five-fold cross-validation, achieving R^2 = 0.533 and Spearman rho = 0.763; state-held-out cross-validation (51 folds) confirms generalization (R^2 = 0.525). TreeSHAP analysis identifies income and education as the dominant factor group (with the engineered interaction term absorbing attribution from its constituent features), and SHAP-based clustering reveals three exploratory factor profiles: Well-Connected Moderate (~49K tracts), Affordability-Limited Severe (~21K tracts), and Rural-Elderly (~13K tracts). As a screening tool, ML-based tract selection captures 38.0% of the total adoption gap within the top 10% of tracts versus 35.2% for income-only heuristics (+2.8 pp, p < 0.002, county-block bootstrap); in regret-reduction terms, the model closes 19% of the remaining gap between income-only and oracle selection. The primary contribution is the per-tract factor decomposition: SHAP identifies which feature groups (income/education, rurality, age) are most strongly associated with each tract's predicted gap, and informs differentiated investigation. A temporal stability check, training on ACS 2017 and predicting ACS 2022 with zero survey-year overlap, confirms ranking stability (rho = 0.784, noting hyperparameters tuned on 2022 data).
