Recent work by the author on methods of spatial density analysis for time series data with stochastic trends is reviewed. The methods are extended to include processes with deterministic trends, formulae for the mean spatial density are given, and the limits of sample moments of non-stationary data are shown to take the form of moments with respect to the underlying spatial density, analogous to population moments of a stationary process. The methods are illustrated in some empirical applications and simulations. The empirical applications include macroeconomic data on inflation, financial data on exchange rates and political opinion poll data. It is shown how the methods can be used to measure empirical hazard rates for inflation and deflation. Empirical estimates based on historical US data over the last 60 years indicate that the predominant inflation risks are at low levels (2-6%) and low two-digit levels (10-12%), and that there is also a significant risk of deflation around the ?1% level.