Oracle Spatial Relations provide a powerful way to enrich business data with geospatial context. In this notebook, we use Oracle AI Data Platform and Oracle Autonomous Database to associate point-based business assets with climate zones covering weather science data, creating an enriched dataset that can be seamlessly visualized and analyzed in Oracle Analytics Cloud. While this example uses supply chain assets, the same approach can be applied to customers, retail stores, utility infrastructure, telecom assets, healthcare facilities, and many other location-based datasets.
For example, imagine a company with warehouses, factories, retail stores, or service centers spread across the country. By performing a spatial relation between each asset location and NOAA climate polygons, every location can automatically be classified into a Low, Moderate, High, or Very High climate zone. This enriched information can then be visualized in Oracle Analytics Cloud to help identify operational patterns, regional trends, and areas that may require additional planning or monitoring.
What you'll learn
- Create Oracle Spatial point geometries from latitude and longitude
- Register geometry metadata and create spatial indexes
- Perform efficient point-in-polygon spatial relations
- Apply a nearest-polygon fallback for unmatched locations
- Preserve the relationship type and distance for transparency
- Publish the enriched dataset for visualization in Oracle Analytics Cloud
Resources
🎥 YouTube Walkthrough
Spatial Relations Calculations in Oracle AI Data Platform: Climate Risk Example
📒 AI Data Platform Notebook
📄 Step-by-Step PDF Guide
📄 SUPPLY_CHAIN_ASSETS database table contents to be blended with the climate polygons
📄 SUPPLY_CHAIN_ASSETS SQL file to create the database table
Note :
This notebook uses NOAA climate polygons that were created in a previous walkthrough. If you'd like to see how NOAA raster climate data was transformed into valid vector climate polygons, check out the earlier post below. Together, these notebooks demonstrate a complete end-to-end workflow, from generating climate polygons to enriching business datasets using Oracle Spatial Relations.
*NOAA is National Oceanic and Atmospheric Administration
🔗 Creating NOAA Climate Polygons from Raster Climate Data
👉 Too Much Geo Data? Aggregate It into Business-Friendly Polygons using AI Data Platform
I hope you find this walkthrough useful for building geospatial enrichment workflows with Oracle AI Data Platform, Oracle Spatial, Oracle Autonomous Database, and Oracle Analytics Cloud.
Disclaimer: This document/notebook is intended solely for community knowledge sharing and demonstration purposes. It is not official Oracle documentation and should not be considered as formal Oracle guidance or support documentation.
