Spatial patterns and typology of county-level population shrinkage in China: A multi-source data approach based on census, nighttime light, and POI data

Population shrinkage is an increasingly prominent spatial phenomenon in global urbanization, yet systematic empirical research at the county scale in China remains insufficient. This study integrates the sixth and seventh national censuses, calibrated DMSP-OLS/NPP-VIIRS nighttime light (NTL) data, and point of interest (POI) data to construct a multidimensional framework for identifying county-level population shrinkage. Based on panel data for 2,843 county-level administrative units in mainland China from 2010 to 2020, we use spatial clustering and the K-means algorithm to classify shrinking counties, and an exploratory multivariate regression model to examine associated factors. The results identify 1,267 shrinking county-level units, accounting for 44.6% of the total. According to the combined characteristics of population change, calibrated NTL change, and POI-density change, these counties are grouped into five types: economic decline, resource depletion, peripheral diffusion, agricultural transition, and comprehensive shrinkage. The exploratory regression indicates that urbanization rate, NTL change, and POI-density change are closely associated with county-level population shrinkage; because several explanatory variables are correlated, the coefficients are interpreted as associative evidence rather than independent causal contributions. This study provides a methodological framework for understanding the spatial heterogeneity of county-level population shrinkage in China and offers policy implications for differentiated governance.