This use case identifies hots spots, colds spots, and outliers of the median income in the city of Los Angeles according to the Los Angeles Income Census dataset.

Based on spatial analysis and statistics, it trains a clustering algorithm based on the median income that finds hot spots, cold spots, and outliers.

This example shows two ways to identify hot spots, cold spots, and outliers. The first consists of executing a series of spatial analysis tasks, while the other consists of using the LISAHotspotClustering class.

The following steps enable you to get started on this use case using OML notebook.