It is of interest to analyze urban spatial structure by identifying urban subcenters, for which published literature proposes many methods. Although these methods are widely applied, they demonstrate obvious shortcomings that restrict further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we introduce an alternative method. Unlike two-stage procedures and other arbitrary methods, our method is not based on arbitrary cutoff values and is entirely parameter free. We first calculate the commuting fluxes for each pair of census tracts and use the fluxes to represent a local density. After that, the census tracts are partitioned into several clusters using a clustering algorithm. Finally, subcenters are derived from the clusters through a circularly shaped spatial scan statistic. We apply this method to 2010 and 2015 census data sets for Wuhan, China. The identification and comparison results demonstrate that our proposed method is effective and can be applied toward future research.