用谷歌地图商铺数据估算圣保罗市微观收入，精度达65%。
Crowd-Sourced Geographies of Income: Using Google Maps Points of Interest as High-Frequency Proxies for Sub-Municipal Income Estimation in Sao Paulo, Brazil
- 用谷歌地图商铺类型统计构建收入预测特征。
- 模型在26,625个普查区上达到R²=0.65的预测精度。
- 适合关注城市贫困监测与政策制定的研究者。
准确、及时的次市级收入数据对中等收入国家的社会政策至关重要，但巴西依赖成本高昂的十年一次人口普查，最近一次普查间隔已超过十年。本文检验了谷歌地图地点信息（POI）的构成是否可作为圣保罗市26,625个普查区的高频、低成本收入代理指标。基于理论驱动的POI类别，我们提取各区域的POI数量，通过主成分分析（PCA）和非负矩阵分解（NMF）降维，并训练多种回归模型预测普查所得收入。在防范数据泄露的空间验证设计下，最优模型（NMF结合梯度提升）取得0.65的保留测试R²，性能在不同特征提取方法间稳定。可解释的分解揭示了哪些类型的POI携带收入信号。结果表明，商业性众包地理数据可在普查间隔期内补充传统收入统计，本文还讨论了向多维贫困与能力框架扩展的可能性。
原文摘要 · Abstract (English)
Accurate, up-to-date income data at the sub-municipal scale is essential for social policy in middle-income countries, yet in Brazil it depends on a costly decennial census whose intercensal gap recently exceeded a decade. We test whether the composition of crowd-sourced Google Maps Points of Interest (POIs) can serve as a high-frequency, low-cost proxy for household income across the 26,625 census sectors of the municipality of Sao Paulo. Using a theoretically motivated set of POI categories retrieved from Google Places, we represent each sector by its POI counts, decompose these high-dimensional, sparse features with principal component analysis (PCA) and non-negative matrix factorization (NMF), and train a sweep of regression models to predict census-derived income. Under a data leakage-aware spatial validation design the best model (NMF with gradient boosting) attains a held-out R^2 of 0.65, with performance stable across feature-extraction methods. Interpretable decompositions reveal which POI types carry the income signal. These results suggest that commercial, crowd-sourced geospatial data can complement conventional income statistics during intercensal periods, and we discuss extensions toward multidimensional poverty and the capabilities framework.
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