Social spacing in human-robot interactions is among the main features that is useful when integrating human social intelligence into robotic perception and action skills. One of the main challenges, is to capture the transitions incurred by the human and further take into account robot constraints. Towards this goal, we introduce a novel methodology that can instantiate diverse social spacing models depending on the context and further as a function of uncertainty and robot perception capacity. Our method is based on the use of non-stationary, skew-normal probability density functions for the space of individuals and on treating multi-person space interactions through social mapping. We show the utility of our approach in practice using an indoor robot operating in the presence of humans, allowing it to exhibit socially intelligent responses.