Detecting regional spatial structures based on spatial interactions is crucial in applications ranging from urban planning to traffic control. In the big data era, various movement trajectories are available for studying spatial structures. This research uses large scale Shanghai taxi trip data extracted from GPS-enabled taxi trajectories to reveal traffic flow patterns and urban structure of the city. Using the network science methods, 15 temporally stable regions reflecting the scope of people's daily travels are found using community detection method on the network built from short trips, which represent residents' daily intra-urban travels and exhibit a clear pattern. In each region, taxi traffic flows are dominated by a few 'hubs' and 'hubs' in suburbs impact more trips than 'hubs' in urban areas. Land use conditions in urban regions are different from those in suburban areas. Additionally, 'hubs' in urban area associate with office buildings and commercial areas more, whereas residential land use is more common in suburban hubs. The taxi flow structures and land uses reveal the polycentric and layered concentric structure of Shanghai. Finally, according to the temporal variations of taxi flows and the diversity levels of taxi trip lengths, we explore the total taxi traffic properties of each region and proved the city structure we find. External trips across regions also take large proportion of the total traffic in each region, especially in suburbs. The results could help transportation policy making and shed light on the way to reveal urban structures with big data.