What type of analysis connects data based on the spatial relationships between features?

The analysis that connects data based on the spatial relationships between features is known as spatial joins. Spatial joins combine two datasets by matching features based on their location and spatial relationships, such as intersection, containment, or proximity. This method allows for integrating attribute information from one feature dataset to another based on their geographic relationships.

In a spatial join, it is possible to determine how features in one layer relate to features in another layer without having to manually cross-reference the datasets. For instance, if you wanted to analyze how many parks fall within a particular city boundary, a spatial join could effectively relate park features to the city boundary feature based on their spatial locations.

In contrast, the other types of analyses have different focuses. Overlay analysis involves combining two datasets to create a new dataset that contains the attributes of both layers while considering how they overlap spatially. Proximity analysis looks specifically at the distances between features and can determine nearest neighbors or buffers, rather than relational connections. Network analysis is used for modeling and analyzing flow, routes, and connectivity within networks, such as transportation or utilities, focusing on the connections and pathways rather than direct spatial relationships between features.