Estimating asset wealth using multidimensional luminous information in areas lacking nighttime light

ABSTRACT

Due to the difficulty of obtaining survey data, nighttime light (NTL) imagery has emerged as valuable alternative data for asset wealth estimation. However, the nighttime light values do not differentiate between levels of asset wealth in various unlit areas, as the nighttime light values in unlit areas are all 0. Here, NTL data and World Settlement Footprint (WSF) data were combined to extract multidimensional luminous features that are also differential in unlit areas to estimate asset wealth in nighttime light-poor areas at 500 m × 500 m spatial units. A random forest model was used to estimate asset wealth, based on the shortest distance of settlements to three categories of lighted areas, along with the brightness values derived from the nearest lighted area and the settlements themselves. This model achieved an explanation of 71% for the variation in settlement asset wealth and demonstrated effectiveness in estimating the asset wealth of unlit areas. The MAE and RMSE of asset wealth estimation in the unlit clusters were 4.03 and 5.28, respectively. Asset wealth is generally low across most African settlements, with clear two-tier differentiation in Africa. In summary, the proposed method can extensively explore the luminous information in unlit areas.

KEYWORDS:

• Asset wealth
• nighttime light
• sustainable development goals
• unlit areas

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this study are available by contacting the corresponding author.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China (grant number 2023YFB3906102), International Science and Technology Cooperation Project of Hubei Province (grant number 2023EHA001) and National Natural Science Foundation of China (grant number 42271371).