基于移动电话和卫星数据的机器学习贫困地图的决策中心评估
Decision-Centered Evaluation of Machine Learning Poverty Maps Using Mobile Phone and Satellite Data
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中文总结 AI 辅助
本研究对斯里兰卡贫困地图进行决策中心评估，发现随机森林结合多源数据在目标定位上优于夜间灯光，且空间分组验证更可靠，但空间隔离区误差更高。
中文摘要 AI 辅助
识别最贫困社区对于减贫至关重要，但家庭调查和人口普查成本高昂且频率低。机器学习提供了基于移动电话和卫星数据的替代性贫困估计，然而，仅凭平均预测精度并不能表明这些地图在有限预算下是否支持目标定位。我们对斯里兰卡的13,985个Grama Niladhari分区进行了决策中心评估，结合了通话详细记录（CDRs）、遥感（RS）和CNN派生的Landsat 8嵌入。我们评估了对最贫困行政单位的恢复情况，比较了随机验证和空间分组验证，并考察了社会经济非典型社区中的误差。与基于人口普查的资产指数（PC1）相比，随机森林实现了0.830的Recall@25%，而夜间灯光仅为0.450。相对于Divisional Secretariat Division（DSD）分组的留出验证，随机分割使召回率提高了4.1个百分点。组合模型恢复了按PC1计算的最贫困25个DSD中的86%，而仅RS模型为69%，仅CDR模型为67%。空间隔离的分区的预测误差高出10.8%。仅RS模型中更强的隔离-误差关联与空间平滑一致，但其原因尚未得到证实。这些发现支持根据目标定位性能和地理迁移性来评估贫困地图，同时认识到与资产指数的一致性并不能确立消费贫困的准确性。
英文摘要
Identifying the poorest communities is essential for poverty alleviation, but household surveys and censuses are costly and infrequent. Machine learning offers alternative poverty estimates from mobile phone and satellite data, yet average prediction accuracy alone does not show whether maps support targeting under limited budgets. We apply a decision-centered evaluation to 13,985 Grama Niladhari divisions in Sri Lanka, combining call detail records (CDRs), remote sensing (RS), and CNN-derived Landsat 8 embeddings. We assess recovery of the poorest administrative units, compare random and spatially grouped validation, and examine errors in socioeconomically atypical communities. Against a census-derived asset index (PC1), Random Forest achieves Recall@25\% of 0.830, compared with 0.450 for nighttime lights. Random splitting raises recall by 4.1 percentage points relative to Divisional Secretariat Division (DSD)-grouped holdouts. The combined model recovers 86\% of the 25 poorest DSDs by PC1, versus 69\% for RS-only and 67\% for CDR-only models. Spatially isolated divisions have 10.8\% higher prediction error. Stronger isolation--error association in RS-only models is consistent with spatial smoothing, although its cause remains unconfirmed. These findings support evaluating poverty maps by targeting performance and geographic transfer, while recognising that agreement with an asset index does not establish consumption-poverty accuracy.
