Cell signaling plays an important role in the normal functioning of cells and in health conditions such as cancers and immune diseases. Cell signaling models, capturing the transformations of many different molecular species, are one of the most successful applications of dynamical systems biology. Since they involve highly nonlinear dynamics, the predicting power of these models is often limited due to the difficulty in estimating the relevant kinetic parameters. Fluorescent labeling in conjunction with super-resolution microscopy provides in vivo trajectories of receptors and other bio-molecules of interest. This modality is possibly the most direct source of quantitative information on molecular processes in cells. Unfortunately, it is separated by a few orders of magnitude from the resolution of current quantitative models of cell signaling. Signaling models typically require properties (concentrations, reaction rates) averaged over the entire cell; single particle tracking looks at individual molecules in small fraction of the cell. In principle, the gap in resolution can be bridged using simulations: validate a fully spatial model at SPT resolution, then use a consistent abstraction / averaging procedure to infer effective kinetics on larger spatial scales. Extrapolating diffusion coefficients and dimerization rates observed at SPT resolution is complicated by the presence of spatial structure that interferes with the free movement of molecules of interest. Here we present a method used to identify such structures, based on deviations from ideal Brownian motion and outline a framework aimed at refinement using simulated motion in a known landscape.