Characterizing the spatial organization of urban systems is a challenge which points to the more general problem of describing marked point processes in spatial statistics. We propose a non-parametric method that goes beyond standard tools of point pattern analysis and which is based on a mapping between the points and a "dominance tree", constructed from a recursive analysis of their Voronoi tessellation. Using toy models, we show that the height of a node in this tree encodes both its mark and the structure of its neighborhood, reflecting its importance in the system. We use historical population data in France (1876-2018) and the US (1880-2010) and show that the method highlights multiscale urban dynamics experienced by these countries. These include non monotonous city trajectories in the US, as revealed by the evolution of their height in the tree. The method also captures the attraction basins of cities at successive scales, and while in both countries these basin sizes become more homogeneous at larger scales, they are also more heterogeneous in France than in the US. We introduce a simple graphical representation - the height clock - that monitors the evolution of the role of each city in its country. Finally, we show that the height of a city in the tree is less sensitive to different statistical definitions of cities than its rank in the urban hierarchy.