Poverty maps are essential tools for governments and NGOs to track socioeconomic changes and adequately allocate infrastructure and services in places in need. Sensor and online crowd-sourced data combined with machine learning methods have provided a recent breakthrough in poverty map inference. However, these methods do not capture local wealth fluctuations, and are not optimized to produce accountable results that guarantee accurate predictions to all sub-populations. Here, we propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple geographically clustered populated places, and illustrate their performance in Sierra Leone and Uganda. These models leverage seven independent and freely available feature sources based on satellite images, and metadata collected via online crowd-sourcing and social media. Our models show that combined metadata features are the best predictors of wealth in rural areas, outperforming image-based models, which are the best for predicting the highest wealth quintiles. Our results recover the local mean and variation of wealth, and correctly capture the positive yet non-monotonous correlation between them. We further demonstrate the capabilities and limitations of model transfer across countries and the effects of data recency and other biases. Our methodology provides open tools to build towards more transparent and interpretable models to help governments and NGOs to make informed decisions based on data availability, urbanization level, and poverty thresholds.

翻译：贫困地图是政府和非政府组织追踪社会经济变化、在贫困地区合理配置基础设施和服务的关键工具。传感器数据、在线众包数据与机器学习方法的结合为贫困地图推断带来了突破性进展。然而，现有方法无法捕捉局部财富波动，且未经过优化以产生可解释的结果，从而保证对所有子群体的准确预测。本文提出了一套机器学习模型流程，用于推断多个地理聚集居住区域的财富均值与标准差，并在塞拉利昂和乌干达验证了其性能。这些模型利用基于卫星图像的七种独立且免费的特征源，以及通过在线众包和社交媒体收集的元数据。实验表明，在乡村地区，组合元数据特征是财富的最佳预测因子，其表现优于以图像为基础的模型；而后者在预测最高财富五分位数时表现最佳。研究结果恢复了财富的局部均值与变异程度，并准确捕捉了二者之间正向但非单调的相关性。我们进一步展示了模型跨国家迁移的能力与局限性，以及数据时效性及其他偏差的影响。该方法提供了开放工具，有助于构建更透明、更具可解释性的模型，从而帮助政府和非政府组织根据数据可用性、城市化水平及贫困阈值做出知情决策。