About Spatial Regression

Spatial regression consists of predicting the value of a continuous variable based on input data that is derived by identifying relationships between independent variables and a target variable while considering a geographical context.

For example, a house price is impacted by the prices of nearby houses, and so including this spatial effect in a regression model can help make more accurate predictions on the house price. Spatial regression is essential in geographic applications and the following lists a few more scenarios where it can be applied:

• Predict house prices based on census data and location information.
• Choose a house, considering its proximity to economic opportunities, schools, health care, and roadways for daily commutes.
• Predict the median income of a specific region based on neighboring locations.

Depending on the nature of the data and the task, you should choose one of the algorithms that works best for you. Oracle Spatial AI also provides tools to decide which algorithm to use and suggests the resulting machine learning algorithm that better fits the data.