TimeSpatData: An R Package for Spatio-temporal DataProcessing, Analysis, and Visualization

This is the authors' abstract. We don't add key points for this paper.

With the rise of high-resolution climate products and a growing influx of observed hydrological data, the demand for efficient tools capable of managing extensive, multi-dimensional datasets has become crucial. Established tools such as Climate Data Operators (CDO), though valuable, are hindered by their command-line nature, limiting their adaptability to advanced spatial and statistical analysis requirements. Enter TimeSpatData, an R package designed to offer a comprehensive solution for the efficient processing, analysis, and visualization of NetCDF-based spatio-temporal multidimensional data. Tailored to handle large datasets, it integrates additional spatial and temporal information vital for indepth analysis and modeling. Noteworthy for its capability in spatial processing, the package provides functionalities including reprojection, clipping, and masking. Beyond fundamental operations, users can extract and reformat raster or vector-based time series to polygon-based time series, sample data to point-based time series, or interpolate missing values in both time and spatial dimensions. Other analytical applications include spatial and temporal statistics, time series analyses and their combination. The integration of Shiny-based web tools adds a dynamic and interactive dimension to the visualization of temporal, spatial, and statistical information. In conclusion, TimeSpatData emerges as a versatile, efficient, and user-friendly toolkit for researchers and analysts dealing with complex spatio-temporal datasets. Ongoing developments will focus on expanding functionalities, supporting additional data formats, and refining user experiences, thereby contributing to a dynamic landscape of climate research and hydrological modeling.