Analyzing Multidimensional Scientific Data using Python
Scientific data such as climate, weather, and ocean data are multidimensional in nature and commonly stored in multidimensional formats such as netCDF, HDF, GRIB, and Zarr. This session will present the multidimensional data models designed to manage and analyze the multidimensional data, such as multidimensional CRF, DSG feature, and trajectory dataset. We will show you the workflow on how to create a multidimensional raster and features from these formats and demonstrate the multidimensional analysis capabilities such as aggregation, detecting anomalies, identifying patterns, and performing trend and predictive analysis. You will see examples using ArcPy and ArcGIS API for Python.