Spatial joins are an important aspect of working with spatial data in Alteryx. They allow you to combine two datasets based on their spatial relationships. There are different types of spatial joins in Alteryx, including intersect, contain, within, and so on. Each of these joins is useful in different situations and can help you gain insights into your data.

Universe and Target

In spatial join, “universe” and “target” are two terms used to describe the datasets being joined. The universe dataset refers to the spatial dataset that contains all the possible locations or areas that the data could be joined to. It is sometimes referred to as the reference or master dataset. The universe dataset provides the spatial framework or context for the analysis, and is typically the larger of the two datasets being joined. The target dataset refers to the dataset that is being joined to the universe dataset. It contains the data that is being matched to the spatial features in the universe dataset. The target dataset is often smaller than the universe dataset, and typically contains the attributes or data that you want to associate with the spatial features in the universe dataset.

Different types of spatial joins

       1.Intersect

The Intersect join type in Alteryx Spatial Join is used to identify all the features in both datasets that share a common area. This type of join returns only the areas where the two datasets overlap. This join type is useful when you want to identify the locations where the two datasets intersect. Suppose you have two datasets, one containing the locations of all the hospitals in a city and the other containing the locations of all the schools in the same city. You can use the Intersect join type to identify all the locations where a school and a hospital are present in the same area.

        2.Contains

The Contains join type in Alteryx Spatial Join is used to identify all the features in one dataset that contain features from the other dataset. This type of join returns only the areas in the first dataset that fully contain the features in the second dataset. This join type is useful when you want to identify the areas where one dataset fully contains the other dataset. Suppose you have two datasets, one containing the locations of all the neighborhoods in a city and the other containing the locations of all the parks in the same city. You can use the Contains join type to identify all the neighborhoods that contain a park within their boundaries.

        3.Within

The Within join type in Alteryx Spatial Join is used to identify all the features in one dataset that are completely contained within features from the other dataset. This type of join returns only the areas in the first dataset that are fully contained within the features in the second dataset. This join type is useful when you want to identify the areas that are fully contained within another dataset. Suppose you have two datasets, one containing the locations of all the stores in a city and the other containing the locations of all the shopping centers in the same city. You can use the Within join type to identify all the stores that are fully contained within a shopping center.

      4. Touch

This method selects records where the features in the input data and join data touch each other, but do not intersect. This can be useful when working with datasets that represent boundaries or boundaries of areas of interest. For example, you might use this method to join a set of census tracts to a set of school districts, where you only want to include the census tracts that touch the school districts, but do not overlap them.

    5. Bounding Rectangles Overlap

Bounding Rectangle Overlap is a spatial join method in Alteryx that matches two datasets based on the overlap between their bounding rectangles. Bounding rectangles are the smallest rectangular area that completely encloses each spatial object in a dataset. By matching datasets based on the overlap between their bounding rectangles, users can quickly identify and analyze areas where the datasets intersect.The Bounding Rectangle Overlap method is useful when users want to quickly identify areas of overlap between large datasets with complex geometries. This method is also useful when users want to limit the amount of processing required for the join, as it is often faster than more precise spatial join methods such as Intersect or Within.

The Data School
Author: The Data School