Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the development of policies to promote the early achievement of SDG target 15.3 of Land Degradation Neutrality (LDN). In this study, Northeast China was selected as the study area, and the progress of indicator 15.3.1 was assessed based on the perspective of Net Primary Productivity (NPP) calculated by the CASA model. WorldPop population spatial distribution data were used as a proxy for human activities, combined with climate data to calculate the effects of changes in temperature, precipitation and population spatial distribution on vegetation NPP based on the partial correlation coefficient method and the Geodetector method. The results showed that 92.81% of the areas that passed the test of significance showed an increasing trend in vegetation NPP from 2000 to 2020. The vegetation NPP was affected by a combination of temperature, precipitation and population. The effects of temperature and precipitation on spatial differences in NPP for various vegetation types were significantly greater than those of population, but in high-density population zones, the effects of population on spatial differences in NPP were generally greater than those of temperature and precipitation. Precipitation was the main driver for spatial variation in NPP in deciduous broad-leaved forests, cultivated vegetation and thickets, while temperature was the main driver for spatial variation in NPP in evergreen coniferous forests. Generally, the warming and wetting trend in Northeast China contributed to the accumulation of NPP in cultivated vegetation, thickets, steppes and grasslands. The sensitivity of NPP to temperature and precipitation in deciduous broad-leaved and deciduous coniferous forests varied according to geographical location.

Studying the response of urban functions to residents’ spatiotemporal activity patterns is essential for understanding urban functions and guiding resource allocation. Unlike previous studies constrained by fixed intervals and static functional spaces, this study has developed an analytical framework to examine urban functional responses to residents’ activity patterns under dynamic spatiotemporal combinations. Tensor decomposition was employed to identify key temporal activity patterns of residents and dynamic urban functional patterns, while a Random Forest model was used to evaluate the contributions of five POI (Points of Interest) groups—Transportation, Organizations, Leisure, Habitation, and Basic Facilities—derived from a reclassification of 17 original POI categories, and the Elasticity Index (EI) quantifies functional responsiveness to activity changes. Results indicated that (1) four temporal patterns (sleeping, commuting, daytime, and leisure) and four spatial function types (the basic living area, the residential areas with mixed functions, residential areas with commercial functions and bustling business districts) characterized Beijing’s urban dynamics; (2) the five types of urban function varied with spatiotemporal context, with basic living POIs dominating daytime activities in residential zones and transportation POIs prevailing during commuting in mixed-use areas; (3) EI revealed significant spatial heterogeneity in adaptive capacities to activity transitions, which helped to accurately identify the key areas for improving urban functions. These findings provide new methodological insights and scientific evidence for resilient urban planning and resource optimization, supporting data-driven decision-making for spatial planning, infrastructure allocation, and emergency response management.

Point-of-interest (POI) data are widely used to spatialize and predict socioeconomic variables, yet their consistency across regions and over time, as well as their cross-regional generalizability, remain insufficiently understood. This study examines these issues using county-level units in China for 2010 and 2020 from three perspectives: relationship structure, cross-regional generalization, and model improvement. First, a power-law model is applied to characterize the nonlinear relationship between POI density and population density and to assess its spatiotemporal heterogeneity. Second, generalizability is evaluated by comparing model parameters and predictive performance under random and spatially stratified sampling. Third, multi-source geospatial data, including nighttime lights, road networks, and land use, are integrated to compare linear, spatial, machine learning, and ensemble models. Results reveal a consistent sublinear relationship with strong spatial heterogeneity. Under spatially independent validation, predictive accuracy declines and becomes more variable, indicating limited cross-regional generalization. Integrating multi-source data with ensemble learning improves stability and reduces uncertainty. POI remains the dominant predictor, though its relative importance becomes more concentrated in 2020. Overall, the study highlights the limitations of POI-based population estimation and proposes strategies to enhance robustness and generalizability.