Multidimensional data represents geophysical, environmental, climatological, or atmospheric phenomena that occur over space (two dimensional), time (another dimension), or height or depth (two more dimensions). These datasets can also contain multiple variables, such as precipitation, soil moisture, and temperature. Multidimensional raster data can be generated from satellite observations captured at different time intervals, or it can be generated from numerical models that aggregate, interpolate, or simulate data from other sources.

Datasets such as these are valuable for atmospheric, oceanographic, climate, and earth sciences. With multidimensional data, researchers can analyze changes through time, such as deforestation, and changes through space, such as changes in ocean salinity with depth. However, given the complex data structure, managing these datasets to analyze or visualize them quickly and effectively can be challenging. These datasets are also often very large, stored in a cloud environment, and frequently updated, introducing additional complications.

ArcGIS provides an intuitive experience to work with individual multidimensional datasets in their native format—netCDF, GRIB, and HDF. Collections of multidimensional datasets can be managed using mosaic datasets (the primary data model used in ArcGIS to manage imagery), or converted to CRF or transpose CRF to improve performance. Regardless of which strategy you choose, you can publish your data as a multidimensional image service to share with your organization or the public. Multidimensional data management tools and capabilities can be accessed through ArcGIS Pro or through ArcPy to automate processing.

Explore the following resources to learn more about managing multidimensional data in ArcGIS. (Not sure where to start? Look for the star by Esri's most helpful resources.)

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