Spatial Data Mining

Spatial Data Mining is the process of discovering interesting and useful patterns, relationships, and knowledge from large spatial databases. It applies data mining techniques specifically designed to handle the unique characteristics of spatial data, including spatial relationships, proximity, and geographic context.

This field combines traditional data mining methods with spatial analysis techniques to extract hidden patterns from geographic datasets. Spatial data mining considers the spatial properties of data, such as location, distance, and spatial relationships, to discover patterns that would not be apparent using conventional data mining approaches.

Practical Applications

• Customer behavior analysis using location-based transaction data
• Environmental pattern discovery from sensor networks and monitoring data
• Urban planning insights from demographic and land use data
• Transportation pattern analysis from GPS tracking and traffic data
• Public health surveillance using spatial disease occurrence data

Related Terms

• Knowledge Discovery
• Pattern Recognition
• Spatial Clustering
• Geographic Knowledge Discovery
• Spatial Association Rules