Fine-scale poverty estimation by integrating SDGSAT-1 glimmer images and urban functional zoning data

Abstract

Poverty is a pervasive global issue that adversely affects human well-being. Traditional socioeconomic censuses are time-consuming and resource-intensive, suffering from temporal delays, while reliance on nighttime light data with low spatial resolution is insufficient for fine-scale identification of impoverished regions. Furthermore, the spatial heterogeneity of nighttime light in different urban functional zones has been overlooked. To address these shortcomings, we proposed a novel approach by integrating high-resolution SDGSAT-1 nighttime light data (10 m) with urban functional zoning data using a spatial overlay tool. A random forest model was then applied to predict county-level poverty identification in Guangdong, China. For comparative validation, traditional NPP-VIIRS nighttime light data (500 m) were also incorporated. This method effectively explored the nonlinear relationship between nighttime light, urban functional zones, and the multidimensional poverty index (MPI, serving as the dependent variable). Our experiments demonstrate that the integration of urban functional zoning with nighttime light moderately improves the accuracy of poverty estimates. Among the models tested, the one considering functional zoning-based indicators of “number of light pixels” and “sum of pixel light values” increased the correlation coefficient by 0.0158 compared to the model without considering these indicators. Additionally, comparative analysis revealed that high-resolution data from SDGSAT-1 exhibited a better fit with the MPI when integrated with functional zoning-based indicators. Specifically, the correlation coefficient of this combination was 0.0086 higher than that of traditional NPP-VIIRS data. This highlights that SDGSAT-1 can delineate the boundaries between dark and light regions more precisely, leading to a more accurate reflection of regional poverty levels. Our findings facilitate fine-scale poverty estimation across large regions. This approach can inform policy design, such as dynamic optimization of resource allocation based on poverty estimates, thus enabling timely and accurate poverty alleviation efforts.