Traditional methods for measuring asset wealth face limitations due to data scarcity, making it challenging to apply them on a large scale and over long periods with fine granularity. Publicly available satellite images, such as nighttime light imagery, have become an important alternative data source for estimating asset wealth. This study thoroughly exploited the spatial neighborhood information of nighttime light, combined with other remote sensing features and the cross-national, temporally comparable International Wealth Index (IWI), to construct long-term asset wealth estimation models for African countries with and without sample data. Based on these models, it generates asset wealth estimates for African settlements at a 500 m spatial resolution from 2012 to 2022. The R2 values for the models of countries with and without sample data are 0.85 and 0.76, respectively, with mean absolute errors of 6.08 and 8.35, and root means square errors of 8.52 and 10.81, respectively. Additionally, the accuracy of the temporal variation estimates surpasses previous related studies, achieving an R2 of 0.60. From 2012 to 2022, the overall IWI increased from 28.80 to 30.80, representing an increase of 0.11 standard deviations. In addition to countries with household survey data, the proposed method can also accurately estimate asset wealth for countries without survey data and effectively track asset wealth changes over time.

中文翻译：

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使用公开可用的遥感影像估算非洲的长期时间序列细粒度资产财富


由于数据稀缺，衡量资产财富的传统方法面临局限性，这使得难以大规模、长期、精细地应用它们。公开可用的卫星图像（例如夜间灯光图像）已成为估算资产财富的重要替代数据源。本研究深入利用夜间光线的空间邻域信息，结合其他遥感特征和跨国时间可比的国际财富指数 （IWI），构建了有和没有样本数据的非洲国家长期资产财富估计模型。基于这些模型，它以 500 m 的空间分辨率生成了 2012 年至 2022 年非洲定居点的资产财富估计。有样本数据和无样本数据的国家模型的 R2 值分别为 0.85 和 0.76，平均绝对误差分别为 6.08 和 8.35，均方根误差分别为 8.52 和 10.81。此外，时间变化估计的准确性超过了以前的相关研究，达到了 0.60 的 R2。从 2012 年到 2022 年，整体 IWI 从 28.80 增加到 30.80，增加了 0.11 个标准差。除了有住户调查数据的国家外，所提出的方法还可以准确估计没有调查数据的国家的资产财富，并有效地跟踪资产财富随时间的变化。