Announcing New Spatial Machine Learning Algorithms in OML4Py on Autonomous Database Serverless

We are pleased to announce the general availability of a new feature of Oracle Machine Learning for Python (OML4Py), Spatial AI. OML4Py Spatial AI provides specialized machine learning algorithms that incorporate the effects of location. Using machine learning with spatial algorithms can improve model quality and prediction accuracy by accounting for the effects of location. For example, spatial regression algorithms are able to enhance home value predictions by incorporating the influence of neighboring home values. Spatial algorithms also allow you to detect location patterns, like spatial clustering of traffic accidents. As part of OML4Py on Autonomous Database Serverless, Spatial AI provides  a single environment for spatial ML workflows that minimizes data movement to external ML engines, simplifies your architecture, and accelerates time to value.

Sample notebooks are provided to help get you started. From the Oracle Machine Learning user interface, navigate to Templates > Examples and filter for "spatial". Clicking on the title of a sample notebook opens it in read-only mode to review the content. To create your own editable/runnable copy of a sample notebook, first select the sample notebook by clicking on the tile, click "Create Notebook", and then select your project. Begin with the notebook "OML4Py Spatial AI Run Me First" to seed sample data, and then try other notebooks based on your areas of interest.

After the data are seeded, sample notebooks can be run in any order. For example, in the sample notebook OML4Py Spatial AI Agglomerative Clustering and Regionalization, you apply a spatial ML algorithm that combines sets of Census Block Groups into broad regions based on similar demographics. Such regions are useful for regional marketing, where strategies are adjusted based localized demographics and associated buying patterns. The following image shows the results of a non-spatial clustering based solely on similar feature values (left), and results of clustering with a spatial ML algorithm that combines areas into regions based on similarity of both feature values and location.

Source: oracle.com