The increasing availability of experimental data has intensified interest in calibrating stochastic models, raising fundamental questions about parameter identifiability. Structural identifiability determines whether parameters can be uniquely recovered from idealised data, a prerequisite to allow for parameter estimation in real-world scenarios. However, there is a lack of methodologies generally applicable to analyse the structural identifiability of such stochastic processes. Therefore, we develop a methodology for a class of spatio-temporal stochastic processes. We investigate how structural identifiability depends on the type of available data, distinguishing between single-particle trajectories and total particle density measurements. For trajectory data, we use a particle-based description that explicitly represents single-particle dynamics. For population-level data, we derive a partial differential equation representation, that describes the evolution of total particle density, and apply a differential algebra approach, common to ordinary differential equation analysis. We further introduce Taylor-series-expansion and Fourier-transform approaches to study the initial condition. We apply our methodology to an example model with a specified class of initial conditions. We show that the model is globally structurally identifiable given infinitely many, infinitely long single-particle trajectories, but only locally structurally identifiable from total particle density data, demonstrating that parameter identifiability depends on the type of data available.

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