Spacetime: Lowering the barrier of entry to working with spatiotemporal data through a new Python library (Virtual Lunch & Learn)

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Presenter: Alex Burnham, Ph.D., Data Scientist & Computer Programmer, University of Vermont

Abstract: Although tools that address rescaling spatial and temporal datasets separately do exist in Python and R, there is currently a gap in this space for users in the life and social sciences who are not computer programmers. Here we focus on developing improved tools to assist users in incorporating spatiotemporal datasets into their research programs through a new piece of software called Spacetime. The main objective of the spacetime package is to make tasks like loading, rescaling, merging, and conducting mathematical operations on spatiotemporal (or other D-dimensional data sets) easier for the user by providing a set of concise yet powerful functions. Spacetime operations utilize a cube-like structure for all data sets that makes storing and manipulating large D-dimensional datasets more efficient. For scientists working with spatiotemporal data (such as climate, weather, or land cover datasets) spacetime is an ideal platform that allows the user to focus on the science rather than the coding. Spacetime is available in its beta stage as both an R package and a Python library.