Preliminary analysis of factors affecting economic well-being based on SDGSAT-1 nighttime light remote sensing and household survey data - W. Jiang; J. Liu; J. Liu; T. Long; M. Liu; Z. Pang; G. Luo; E. Adam; X. Ding; S. Cui; C. Wen; L. Wang (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2025)
Economic well-being is an important indicator for measuring the happiness of national residents and is of great significance for poverty assessment in the United Nations 2030 Sustainable Development Goals. Since economic well-being is a multidimensional indicator involving multiple aspects of economic development level and individual residents' perception, its influencing factors are complex and lack empirical research. In order to explore the influencing factors of economic well-being, this study proposed an economic well-being factor analysis framework combining nighttime light remote sensing and household survey data. Taking Bazhou City, Hebei Province, China as an example, a household survey of economic well-being indicators was conducted in towns, and the spatial distribution feature of economic well-being in each town were statistically analyzed. Further, the Sustainable Development Satellite (SDGSAT-1) nighttime light remote sensing data and socioeconomic statistical data were used to conduct an analysis of the influencing factors of economic well-being. The results showed that: (1) there is spatial heterogeneity in economic well-being among towns, among which Dongduan has the highest economic well-being and Wangzhuangzi has the lowest economic well-being. The average economic well-being value of Bazhou City is 9.04; (2) A preliminary analysis of economic well-being and nighttime light remote sensing feature shows that the economic well-being of Dongduan and Tang'erli and other towns are consistent with the nighttime light remote sensing feature, but not in Bazhou and Shengfang. This indicates that the impact of economic well-being is multi-factorial, and there is no significant relationship between economic development level and economic well-being in local scale areas. This study is the first to use nighttime light remote sensing data and household survey data to analyze the factors affecting socioeconomic well-being, providing important support for subsequent large-scale global socioeconomic well-being modeling and poverty assessment.