Collect
Geospatial data come from many sources, including satellites, aircraft, imagery, and field measurements. GIS data use coordinates like longitude (X), latitude (Y), and sometimes height, depth, or time (Z) to show where features are located. Extra information about each dataset, called metadata, is stored in standard scientific formats used across 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).
GIS software like QGIS and ArcGIS uses mosaic datasets, which group many individual raster files into one unified image. A raster is a grid of pixels, with each pixel holding a value such as temperature, elevation, or land cover. Together, these pixels form an image that shows how a feature changes across an area. Mosaic datasets can include multiple layers, such as different times or depths, stored in a footprint table. Once combined, the dataset can be queried, analyzed, and shared as a single source, even when the original raster data do not overlap.
In recent years, GIS software has improved its ability to read and use scientific data formats. Major platforms now offer better tools to support common workflows, making it easier to manage, analyze, and share multidimensional data.
Display
GIS tools use information in each data file to determine how it should appear on a map. 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 data created using standards such as Hierarchical Data Format - Earth Observing System (HDF-EOS) and Climate Forecast (CF) conventions work well in GIS because they follow common rules for reading and displaying information. Some complex datasets, however, may still require additional support from libraries like the Geospatial Data Abstraction Library (GDAL) to display properly.
Analyze
GIS makes it possible to uncover patterns and trends across space and time. Users can detect changes, extract features, identify relationships, interpolate data, calculate statistics, and more. GIS tools also support machine learning and deep learning to improve predictions and make better use of large data collections.
Share
Modern GIS tools go far beyond traditional maps. They allow users to publish web maps, build interactive apps, create dashboards, and tell stories with data. These tools help NASA share science with wider audiences and make it easier for people to explore, understand, and use Earth data.