What analysis examines local patterns of spatial association between two categories of point features?

Colocation analysis investigates the local patterns of spatial association between two different categories of point features. This analysis is particularly useful in identifying whether certain types of features are located near each other more often than would be expected by chance. For instance, it can reveal whether retail stores and public transport stops tend to cluster in certain areas, indicating a potential relationship or influence between them.

This approach utilizes statistical methods to measure the degree of spatial association, often through techniques like spatial autocorrelation and other spatial statistics. By pinpointing where different categories of features are frequently located together, colocation analysis helps in understanding the underlying spatial dynamics, consequently offering insights into phenomena such as land use, population density effects, or environmental factors.

In contrast, the other options focus on different aspects of spatial analysis. For example, cluster analysis is aimed at identifying groups or clusters of similar features within a single category rather than examining relationships between distinct categories. Spatial joins involve combining attribute data from different datasets based on their spatial relationships but do not specifically assess the strength of association between different categories. Proximity analysis, on the other hand, measures distances to understand how features relate in space but does not focus specifically on the association of two distinct categories.