Python: Working with Multidimensional Scientific Data

Python: Working with Multidimensional Scientific Data
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The availability and scale of scientific data is increasing exponentially. Fortunately, ArcGIS provides functionality for reading, managing, analyzing and visualizing scientific data stored in three formats widely used in the scientific community – netCDF, HDF and GRIB. Using satellite and model derived earth science data, this session will present examples of data management, and global scale spatial and temporal analyses in ArcGIS. Finally, the session will discuss and demonstrate how to extend the data management and analytical capabilities of multidimensional data in ArcGIS using python packages.

Presented by Deng Ding and Nawajish Noman

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