As urban data expands, existing spatio-temporal models encounter challenges such as high context dependency, poor cross-scenario generalization, and inefficient computational performance. To address these issues, we propose UrbanPG, an efficient and scalable framework for spatio-temporal learning. UrbanPG separates task-specific personalized patterns from general patterns, enabling unified spatio-temporal modeling and efficient knowledge generalization across scenarios. The key innovations of UrbanPG include: the development of a lightweight, context-independent general backbone utilizing linear spatio-temporal attention for scalable cross-scenario deployment; a personalized context prompt mechanism designed to model heterogeneity through spatio-temporal embeddings and random perturbation regularization, interacting with the backbone to enhance pattern differentiation; the proposal of multi spatio-temporal learning paradigms for rapid knowledge transfer and generalization to downstream tasks through fine-tuning personalized context prompts while freezing the backbone. Experimental results demonstrate that UrbanPG achieves state-of-the-art performance in large-scale forecasting, few-shot transfer, and continual learning tasks across eight real-world datasets, showcasing exceptional performance, strong generalization, and significant reductions in computational overhead.