Scaling common geoprocessing tasks with Spatial Indexes
Last updated
Last updated
So, you've decided to start scaling your analysis using Spatial Indexes - great! When using these grid systems, some common spatial processing tasks require a slightly different approach to when using geometries.
To help you get started, we've created a reference guide below for how you can use Spatial Indexes to complete common geoprocessing tasks - from buffers to clips. Once you're up and running, you'll be amazed at how much more quickly - and cheaply - these operations can run! Remember - you can always revert back to geometries if needed.
All of these tasks are undertaken with CARTO Workflows - our low-code tool for automating spatial analyses. Find more tutorials on using Workflows here.
The humble buffer is one of the most basic - but most useful - forms of spatial analysis. It's used to create a fixed-distance ring around an input feature.
With geometries... use the ST Buffer tool.
With Spatial Indexes... convert the input geometry to a Spatial Index, then use a H3/Quadbin K-Ring component to approximate a buffer. Lookup H3 resolutions here and Quadbin resolutions here to work out the K-Ring size needed.
Where does geometry A overlap with geometry B? It’s one of the most common spatial tasks, but heavy geometries can make this straightforward task a pain.
With geometries... use the ST Intersection tool. This may look like a simple process, but it can be incredibly computationally expensive.
With Spatial Indexes... convert both input geometries to a Spatial Index, then use a Join (inner) to keep only cells which can be found in both inputs.
For a “difference” process, we want the result to be the opposite of the previous intersection, retaining all areas which do not intersect.
With geometries... use the ST Difference tool. Again, while this may look straightforward, it can be slow and computationally expensive.
With Spatial Indexes... again convert both input geometries to a Spatial Index, this time using a full outer Join. A Where component can then be used to filter only "different" cells (where h3 IS null AND h3_joined IS not null) - at a fraction of the calculation size.
Spatial Joins are the "bread and butter" of spatial analysis. They can be used to answer questions like "how many people live within a 10-minute drive of store X?" or "what is the total property value in this flooded area?"
Our Analytics Toolbox provides a series of Enrichment tools which make these types of analyses easy. Enrichment tools for both geometries and Spatial Indexes are available - but we've estimated the latter of these are up to 98% faster!
With geometries... use the Enrich Polygons component.
With Spatial Indexes... use the Enrich H3 / Quadbin Grid component.
Check out the full guide to enriching Spatial Indexes here.
Say you wanted to know the population within 30 miles of
For instance, in the example below we want to create a new column holding the number of stores in a 1km radius.
With Geometries... create a Buffer, run a Spatial Join and then use Group by to aggregate the results.
With Spatial Indexes... have the inputs stored as a H3 grid with both the source and target features in the same table. Like in the earlier Buffer example, use the H3 K-Ring component to create your "search area." Now, you can use the Group by component - grouping by the newly created H3 K-Ring ID - to sum the number of stores within the search area.
This is a fairly simple example, but let's imagine something more complex - say you wanted to calculate the population within 30 miles of a series of input features. Creating and enriching buffers of this size - particularly when you have tens of thousands of inputs - will be incredibly slow, particularly when your input data is very detailed. This type of calculation could take hours - or even days - without Spatial Indexes.
