Understanding the patterns and dynamics of spatial origin-destination flow data has been a long-standing goal of spatial scientists. This study aims at developing a new flow clustering method called flowHDBSCAN, which has the potential to be applied to various urban dynamics issues such as spatial movement analysis and intelligent transportation systems. Flows entail origin and destinations pairs, at the exclusion of the actual path in-between. The method combines density-based clustering and hierarchical clustering approaches and extends them to the context of spatial flows. Not only can it extract flow clusters from various situations including varying flow densities, lengths, directions, and hierarchies, but it also provides an effective way to reveal the potentially hierarchical data structure of the clusters. Common issues such as the modifiable areal unit problem (MAUP) of flow endpoints, false positive errors on short flows, and loss of spatial information are well handled. Moreover, the sole-parameter design guarantees its ease of use and practicality. Experiments are conducted with both a synthetic dataset and an eBay online trade flow dataset in the contiguous U.S.