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Identifying urban employment centers (UECs) is crucial for understanding urban spatial structures. Conventional identification methods use census-type aggregated data. The increasing pervasiveness of mobile phone data provides new possibilities to analyze UECs at finer spatial resolution but requires novel identification methods. This study proposes a new approach, the Locally Decaying Model (LDM), to fit the employment density locally and identify UECs based on statistically significant local peaks. We compared the proposed LDM with conventional methods by Monte-Carlo simulation and real data experiments on employment distributions generated from Shanghai mobile phone data. The Monte-Carlo simulation showed that the LDM performed significantly better. The Shanghai case study demonstrated greater stability of the LDM in identifying UEC numbers and locations on aggregated data than the conventional methods. Furthermore, UECs extracted by LDM from the employment density raster were more consistent with the existing local plan. This research contributed a new subcenter identification method that can be applied to recently available urban big data and a comprehensive analytical framework for evaluating such methods. [ABSTRACT FROM AUTHOR]