The article presents a method for analyzing time series in the spatial domain. By looking at the local time and its expectation, researchers can study the spatial distributions of both stationary and nonstationary time series. The analysis can help forecast spatial distribution, test for structural breaks, check for equality in spatial distribution, and determine spatial dominance. The approach is nonparametric and can be applied to a wide range of stochastic processes, including diffusion models.