Estimation of long time-series fine-grained asset wealth in Africa using publicly available remote sensing imagery
Year: 2024
Type: article
Abstract: • Extracting spatial neighborhood information for nighttime light to improve model accuracy in unlit areas. • Improved accuracy of asset wealth estimation models for countries without survey data. • The accuracy of the models for estimating asset wealth changes has improved significantly. • Mapped asset wealth maps in Africa from 2012 to 2022 at 500 m spatial resolution. 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 R 2 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 R 2 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.
Authors: Mengjie Wang, Xi Li
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,820
OpenAlex ID: W4404334008