While many people know Alteryx as an analytics automation software that aids many industries in solving a broad range of data analytics problems, the program was initially used for spatial analysis. I’ve always been curious about the spatial tools in Alteryx so I was excited to learn about the spatial match tool during training. Spatial match is a unique tool in Alteryx that allows users to define relationships between two spatial objects. Users can bring one set of spatial objects into an universe anchor (U anchor) and one set of spatial objects into a target anchor (T anchor) and create an output depending on the spatial relationship between the two objects in the matched anchor (M anchor). If there’s any records that don’t match the records in the universe, those records will be sorted into the unmatched anchor (U anchor).
To better understand the spatial match tool, we also have to know the spatial relationships that occur between spatial objects. Alteryx defines these relationships as spatial matches, which includes:
Target Intersects the Universe
Returns records where both spatial objects in the target and universe share areas in common
Target contains Universe
Returns records where the spatial objects in universe is contained entirely in spatial objects in the target output
Target Within Universe
Returns records where spatial objects in target is contained entirely in spatial objects in the universe input
Target Touches the Universe
Return records where spatial objects in the target input touch the exterior boundary of spatial objects in the universe input
Target Touches or Intersects the Universe
Return records where spatial objects in the target anchor intersect the same areas as spatial objects in the universe anchor or touches the boundary of objects in the universe anchor
Now that we went over the various types of spatial relationships, let’s see an example in Alteryx! In this example, we want to see the number of rat sightings within 0.1 miles of train stations. We can use a spatial match tool to return the number of spatial objects in the rat sightings dataset that exist in the area that’s 0.1 miles from the train stations.
When a spatial match tool is thrown into the canvas, the pane above will appear after connecting the rat sighting data to the target anchor and the train stations to the universe anchor. Rat sightings will the target and the train stations with a trade area of 0.1 miles will be the universe because we want to see the number of rat sightings dataset that are WITHIN the areas that are 0.1 miles from the train stations. The results of matches that fit this criteria will be displayed in the matched anchor in the result pane. In this example, the centroid above refers to the spatial objects in the rat sighting dataset while the field called spatialobject_trade area is referring to the spatial objects created from generating a trade area of 0.1 miles of the train station points.
Another neat thing about the spatial match tool is that users can rename their fields, change the size of data types, and unselect any fields they no longer want to use after configuring the universe and target inputs. In this way, spatial match can also function as a select tool.
In order to see the results of the spatial match on a map, users will have to attach a browse tool onto the matched anchor of the spatial match tool and go to the map pane in the browse tool. The results should look like the following:
Now we can see the rat sightings within 0.1 miles of train stations on a map! Spatial match is a super practical and necessary tool in starting any spatial analysis. I’m excited to use this tool in the future and see what insights I can uncover. I hope this blog will help you think of ways that you implement spatial match in furthering your spatial analysis!