About the Business Value of the Geometry Data Type

You can easily use Geometry columns from Oracle databases and CSV files, so that you can easily import and connect to spatial data.

Accelerate Map Design and Visualization

You can quickly visualize geographic information by dragging and dropping geometry columns onto a canvas where the shapes render automatically as a map visualization, without having to manually set up any map layers. Spatial data objects, whether sourced directly from an Oracle database or a CSV file, are effortlessly rendered in Oracle Analytics as maps, allowing you to quickly create dynamic spatial insights. This native feature boosts visual performance and ensures the smooth and responsive rendering of spatial data objects.

Optimize Map Layer Maintenance

In scenarios where geometry definitions frequently change—such as delivery routes, flood zones, or custom sales regions—using the geometry data type ensures that maps in Oracle Analytics are automatically updated with the latest shape information. There’s no need to preconfigure or ensure a specific map layer exists in your Oracle Analytics environment, the geometry data automatically shows in a map visualization. There’s also no need to manually update any data or map layers to reflect changes, as the maps dynamically adjust by querying the updated geometry data. This eliminates extra maintenance of data layers or map layers and ensures that maps always display the most current data.

Support Spatial Calculations

You can compute calculations to perform spatial measurements using the geometry data type. Spatial calculations enable you to calculate the area and length of geometry shapes, measure distances between two geographic data types (assuming proper joining is defined), determine spatial relationships, and perform condition-based calculations that return true or false. These calculations help you analyze geographic data more effectively, leading to powerful spatial data-driven decisions.