Analysis of Economic Development Patterns and Driving Factors of Dianchi Lake Basin Based on Space–Time Cubes and Interpretable Machine Learning

Regional economic development serves as a crucial indicator of societal vitality and the efficiency of resource allocation. Nighttime light (NL) remote sensing data is a reliable reflection of regional economic activities, making it essential to analyze its spatiotemporal variations and influencing factors for economic growth. This study employs space–time cubes, incorporating hotspot and outlier analysis, to explore the dynamics of NL in the Dianchi Lake basin between 2000 and 2022, focusing on shifts in centroids, temporal patterns, and spatial clustering. Various machine learning models were tested, with the most effective model utilizing the SHAP algorithm to uncover the nonlinear relationships between explanatory variables and NL. The findings reveal that economic hotspots are predominantly concentrated around Dianchi Lake, exhibiting high–high spatial clustering, whereas cold spots are mainly distributed in the northern and southern regions and are characterized by low–low clustering. In addition, human activity indicators (GDP, road density, and population) and climatic factors (temperature and precipitation) are positively associated with economic development, while topographic factors (DEM and slope) show negative associations.