This study investigates the spatial directionality of urban mobility using vector computation to quantify anisotropy and centripetality. By analyzing travel data from 90 million mobile users across 60 Chinese cities, the researchers introduce a two-dimensional metric to distinguish between strong monocentric, weak monocentric, and polycentric mobility patterns. The findings reveal that monocentric cities exhibit increasing commuting distances as cities expand, while polycentric cities maintain consistent commuting patterns. Anisotropy intensifies in the outskirts of monocentric cities, whereas it remains uniform in polycentric settings. Centripetality decreases as one moves from the urban core, with a steeper decline in polycentric cities. The study highlights the importance of employment attraction strength and commuting distance scaling in explaining these patterns. The results provide insights for shaping policies to alleviate congestion and guide suburban housing development. The research also explores the spatial variations of anisotropy and centripetality across different urban levels, revealing that anisotropy increases with spatial level in all city types, while centripetality decreases. Temporal analysis shows significant differences in mobility patterns during peak hours, with strong monocentric cities exhibiting the most pronounced fluctuations. A microscopic model is developed to simulate mobility patterns, demonstrating how employment attraction strength and commuting distance scale influence urban mobility. The study underscores the importance of spatial directionality in understanding urban mobility dynamics and offers a framework for urban planning and management. The findings suggest that polycentric cities exhibit greater mobility efficiency and challenge traditional classifications of urban forms. The study also highlights the need for further research to integrate additional factors such as traffic congestion and socioeconomic status into mobility models. The results contribute to a deeper understanding of urban mobility patterns and support sustainable urban development.This study investigates the spatial directionality of urban mobility using vector computation to quantify anisotropy and centripetality. By analyzing travel data from 90 million mobile users across 60 Chinese cities, the researchers introduce a two-dimensional metric to distinguish between strong monocentric, weak monocentric, and polycentric mobility patterns. The findings reveal that monocentric cities exhibit increasing commuting distances as cities expand, while polycentric cities maintain consistent commuting patterns. Anisotropy intensifies in the outskirts of monocentric cities, whereas it remains uniform in polycentric settings. Centripetality decreases as one moves from the urban core, with a steeper decline in polycentric cities. The study highlights the importance of employment attraction strength and commuting distance scaling in explaining these patterns. The results provide insights for shaping policies to alleviate congestion and guide suburban housing development. The research also explores the spatial variations of anisotropy and centripetality across different urban levels, revealing that anisotropy increases with spatial level in all city types, while centripetality decreases. Temporal analysis shows significant differences in mobility patterns during peak hours, with strong monocentric cities exhibiting the most pronounced fluctuations. A microscopic model is developed to simulate mobility patterns, demonstrating how employment attraction strength and commuting distance scale influence urban mobility. The study underscores the importance of spatial directionality in understanding urban mobility dynamics and offers a framework for urban planning and management. The findings suggest that polycentric cities exhibit greater mobility efficiency and challenge traditional classifications of urban forms. The study also highlights the need for further research to integrate additional factors such as traffic congestion and socioeconomic status into mobility models. The results contribute to a deeper understanding of urban mobility patterns and support sustainable urban development.