Scientific datasets from global-scale scientific models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. These data are frequently gridded measurements spanning two or three spatial dimensions, the time dimension, and often several data dimensions which vary by the specific dataset. These data are useful in many modeling and analysis applications across the geosciences. Unlike vector spatial datasets, raster spatial datasets lack widely adopted conventions in file formats, data organization, and dissemination mechanisms. Raster datasets are often saved using the Network Common Data Format (NetCDF), Gridded Binary (GRIB), Hierarchical Data Format (HDF), or Geographic Tagged Image File Format (GeoTIFF) file formats. Several of these are entirely or partially incompatible with common GIS software which introduces additional complexity in extracting values from these datasets. We present a method and companion Python package as a general-purpose tool for extracting time series subsets from these files using various spatial geometries. This method and tool enable efficient access to multidimensional data regardless of the format of the data. This research builds on existing file formats and software rather than suggesting new alternatives. We also present an analysis of optimizations and performance.
College and Department
Civil and Environmental Engineering
BYU ScholarsArchive Citation
Hales, Riley Chad, "A New Method and Python Toolkit for General Access to Spatiotemporal N-Dimensional Raster Data" (2021). Theses and Dissertations. 8882.
Spatiotemporal Data, Environmental Modelling, Multidimensional Data, Timeseries, Data Management