Spatial mining refers to the process of discovering patterns, relationships, and trends from spatial data, which is data related to geographical locations and spaces. This data is often represented in forms such as maps, satellite imagery, or geographic information systems (GIS). The goal of spatial mining is to extract meaningful insights or knowledge from this data, typically to support decision-making in various fields like urban planning, environmental monitoring, and resource management.

Key types of spatial mining include:

1. Spatial Pattern Mining: Identifying regularities or patterns in spatial data, such as clusters of certain types of events or phenomena (e.g., areas with high crime rates, locations with high biodiversity).
2. Spatial Association Mining: Discovering relationships between spatial features and other data (e.g., areas with a higher occurrence of certain diseases might be correlated with environmental factors like pollution).
3. Spatial Outlier Detection: Identifying anomalies or unusual patterns in spatial data (e.g., unusual distribution of disease outbreaks or unusual changes in land use).
4. Spatial Classification and Clustering: Categorizing or grouping spatial data into different categories or clusters based on similarities (e.g., grouping land areas based on vegetation types, land use, etc.).

Spatial mining often uses techniques from data mining, machine learning, and statistical analysis, but it specifically considers the spatial context of the data.