Spacetime: A Library for Cleaning and Combining Disparate Spatiotemporal Datasets in Python

Abstract

Abstract

As climate change accelerates, spatiotemporal climate and weather data rasters are increasingly used in academic research, agriculture, infrastructure, and other disci-plines across public and private domains. Unfortunately, these raster data are often stored under numerous file types with different spatial and temporal boundaries, spa-tial reference systems, and spatial and temporal resolutions. The tools and workflows that we have are often hindered by coordinate reference system mismatches, data type mismatches, data aggregation mismatches, and other fundamental challenges. To fully harness the impacts of these data, software tools are needed that can be leveraged effi-ciently and intuitively and without a steep learning curve for the new user. In this pa-per, we introduce Spacetime, a novel programming library in Python that enables effi-cient spatiotemporal data processing, management, alignment, and analysis. Spacetime is an open-source project written in Python that provides a set of powerful functions to manipulate spatiotemporal data in a single, unified library. Spacetime’s overarching themes are simplicity and usability. However, the library does not com-promise processing speed. In our comparison, it was as fast or faster than other popu-lar Python libraries, while requiring many fewer lines of code and no external loops or control functions. We hope this library will lower the barrier of entry to scientists in fields that have not traditionally used spatiotemporal raster data but are doing so in-creasingly frequently in the current climate.