Monitoring Fine-Scale Urban Shrinkage Space with NPP-VIIRS Imagery

Urban shrinkage is a significant challenge to sustainable urban development. To date, the existing research has yet to analyze urban shrinkage at a fine-scale level. This study addresses this gap by employing nighttime light (NTL) data, which, due to its strong correlation with human activity and high spatial–temporal resolution, offers a robust approach for micro-scale population estimation. This paper aims to explore the characteristics and formation mechanisms of urban shrinkage spaces in Guangzhou, using NTL data and applying ordinary least squares (OLS) and geographically weighted regression (GWR) models. The correlational analysis reveals a marked improvement in model fit with GWR (R2 = 0.91) compared with OLS (R2 = 0.63), confirming the predictive power of NTL-based GWR for population mapping and the spatial delineation of urban shrinkage. We demonstrate that urban shrinkage spaces in Guangzhou are predominantly distributed in the outer suburbs, while urban growth is concentrated within the urban core area and inner suburbs. The formation of urban shrinkage in Liwan District examined as a case study, is primarily influenced by market factors, government actions, and regulatory constraints—a constellation of factors likely generalizable with other contexts of urban shrinkage. A comprehensive understanding of urban shrinkage at a fine-scale level is imperative for policy makers to optimize urban land use planning and mitigate the factors contributing to shrinkage space, thereby promoting sustainable urban development.