For the last two decades, GIS technologies have increasingly been used to incorporate not only spatial relationships but also analyzing and visualizing space across time. Spatial-temporal GIS, or 4D GIS, has, in particular, become essential in areas where GIS is needed for predicting dimensions across time.
Relational databases present some limitations to scaling, preventing or limiting the applicability of big data and real-time data problems utilized within GIS. More and more GIS software companies and developers are adopting NoSQL formats where data retrieval is generally faster and easier to structure. NoSQL also facilitates analysis and integration within a variety of tools, which is why open source GIS has proven to be the most useful arena for NoSQL databases.
By integrating GIS with proportional hazard modeling, we are now beginning to see GIS increasing its analytical modeling repertoire for the sciences that leverage factors of spatial and time to better understand how emergence and evolution of given processes develops, even when uncertainty is persistent for areas of research.