Identifying erratic or unstable time-series is an area of interest to many fields. Recently, there have been successful developments towards this goal. These newly developed methodologies however come from domains where it is typical to have several thousand or more temporal observations. This creates a challenge when attempting to apply these methodologies to time-series with much fewer temporal observations such as for spatio-temporal socio-cultural understanding, a domain where a typical time series of interest might only consist of 20-30 annual observations. Identifying instability in spatio-temporal trends is critical for understanding global dynamics and finding areas of potential concern or intervention. In the case of spatio-temporal socio-cultural understanding, instability is marked by two characteristics, 1) how widely varying the values are and 2) how predictable that variance is from one observation to the next. Approaches for simultaneously addressing both of these concerns have been limited. In this paper, we introduce an approximate entropy based method for characterizing the behaviour of a time series with limited temporal observations. For Geocomputation, this methodology represents a novel additional tool for researchers to use for exploring and understanding spatio-temporal data, specifically with limited temporal observations. As a case study, we look at national youth male unemployment across the world from 1991-2014.