With the advent of the mobile era in the last decade and the evolution of the app economy in smartphones and other smart devices, there is an abundance of location data available. Traditional spatial analysis techniques are locked away in databases (such as DB2 Spatial, ESRI ArcGIS server, Oracle Spatial and Graph) that only enable basic analytics and do not scale very well to societal scale data. Moreover, these approaches tend to deal with only static objects, where time is not treated as a first class citizen. This paper introduces the idea of discretizing space-time as a first order primitive to significantly alter downstream algorithms ranging from simple spatial indexing to complex deep learning that operate on such space-time data. We coin the term space time box (STB) and propose this as a fundamental primitive of thinking about trajectories of moving objects. We substantiate and validate the concept of STB through various pieces of our past work. Finally, we show that 3D STBs can be used for efficiently tracking very fast moving objects (asteroids), which was never before been done.