The past decade has experienced a staggering rise of data–aided analysis that facilitate understanding the impact of socio-economical flux and socially oriented activities towards the quality and liveability of space. Evaluating urban environments is not only important from the planners’ perspective, but has larger implications for the residents themselves. In this paper we argue that the liveability of a city or a neighbourhood is not necessarily described by conventional, authoritative data, such as income, crime, education level etc., rather ephemeral data layers, related to human perception, can be more effective in capturing the dynamics of space. Implementing methods that are considered disassociated with urban analytics, we attempt to go beyond the conventions in understanding the dynamics that drive socio-economical phenomena and construct lived space. Our objective is to create methods of anticipating and evaluating urban environment by re-patterning different datasets and taking advantage of their combinatory potential.