Collect
Geospatial data are collected in a variety of ways. They can be remotely sensed from instruments aboard airplanes and satellites, created from imagery, or acquired in the field. GIS data contain spatial coordinates to represent where features are located. This is typically done using X (longitude) and Y (latitude) coordinates. Multidimensional data represent data that are acquired at different dimensions such as depths, heights, and times (Z). GIS data contain spatial coordinates to represent where features are located. Metadata associated with this data are stored in scientific data formats used by the Earth science community.
Store
Multidimensional data formats share common structures for storing multiple variables, with each variable being a multidimensional array in a raster format. The most common specialized formats include, Network Common Data Form (NetCDG), Hierarchical Data Format (HDF), and General Regularly-distributed Information in Binary (GRIB). Some of the more common cloud-ready formats include Cloud Optimized GeoTIFF (COG), Meta Raster Format (MRF), and Cloud Raster Format (CRF).
In tools such as QGIS and ArcGIS, support for raster data is provided using a mosaic dataset. A mosaic dataset is a data model that acts as a shell to input a collection of multiple raster files that include different file formats and is viewed as a single image. A multidimensional mosaic dataset stores information about the dimensions and variables as fields in the mosaic dataset footprint table. The underlying raster data do not have to be connecting or overlapping but instead can be isolated or intermittent datasets. In some GIS software platforms, a single mosaic dataset can then be used to query, process, analyze, and serve data. For more information about mosaic datasets, see the Esri article What are mosaic datasets?
In recent years, GIS software has increased support for scientific data formats in their platforms. In addition to correctly ingesting and reading these files, primary software platforms have developed new tools to aid in common workflows along with the management, analysis, and distribution of multidimensional data.
Display
GIS tools rely on information stored in data files to determine the correct method of visualization. Often, this relies on Coordinate Systems (the values used to define a position on Earth’s surface within a spatial reference to represent location when capturing the data) as well as Projections (how the values are located and displayed on a map).
NASA scientific data work well in GIS tools if they are created using standards such as the Hierarchical Data Format - Earth Observing System (HDF-EOS) and conventions such as Climate Forecast (CF). These help to ensure the data are formatted in a way for tools to read and write. However, not all complex scientific datasets can be easily read or understood by libraries such as the Geospatial Data Abstraction Library (GDAL), which tells software what information to parse from data files and how to display them properly.
Analyze
Using GIS tools, users can perform a multitude of spatial analyses to determine patterns or trends across space and time. GIS analysis is used to detect change, extract features, identify relationships, interpolate data, calculate statistics, determine viewsheds, and find optimal routes. GIS tools also help researchers employ machine learning and deep learning models to enhance the use of Big Data collections and make more accurate predictions.
Share
GIS tools enable the user to do more than create maps. Although cartography is the foundation for GIS technology, modern technology allows GIS practitioners to create and publish web mapping services, develop powerful user-intuitive applications, create interactive dashboards, and tell effective stories with data alongside narration and media. These methods allow content to be distributed to broad audiences in ways that are easy to digest, explore, and utilize as well as integrate with content of their own. Expanding beyond traditional maps allows NASA to increase awareness and exposure within new communities and aids in the understanding and use of science data.