Existing measurements and modeling of radio spectrum usage have shown that exclusive access leads to low efficiency. Thus, the next generation of wireless networks is adopting new paradigms of spectrum sharing and coexistence among heterogeneous networks. However, two significant limitations in current spectrum tenancy models hinder the development of essential functions in nonexclusive spectrum access. First, these models rely on data with much coarser resolutions than those required for wireless scheduling, rendering them ineffective for spectrum prediction or characterizing spectrum access behavior in a wireless coexistence setting. Second, due to a lack of detailed data, current models cannot describe the access dynamics of individual users, leading to unjustified adoption of simplistic traffic models, such as the on/off model and the M/G/1 queue, in spectrum access algorithm research. To address these limitations, we propose the Frame-Level spectrum Model (FLuMe), a data-driven model that characterizes individual spectrum usage based on high-resolution data. This lightweight model tracks the spectrum tenancy movements of individual users using four variables. The proposed model is applied to high-resolution LTE spectrum tenancy data, from which model parameters are extracted. Comprehensive validations demonstrate the goodness-of-fit of the model and its applicability to spectrum prediction.