Introduction to Spatial Indexes
Scale your analysis with Spatial Indexes
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Scale your analysis with Spatial Indexes
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Spatial Indexes - sometimes referred to as Data Cubes or Discrete Global Grid Systems (DGGs) - are global grid systems which tessellate the world into regular, evenly-shaped grid cells to encode location. They are available at multiple resolutions and are hierarchical, with resolutions ranging from feet to miles, and with direct relationships between “parent”, “child” and “neighbor” cells.
They are gaining in popularity as a support geography as they are designed for extremely fast and performant analysis of big data. This is because they are geolocated by a short reference string, rather than a long geometry description which is much larger to store and slower to analyze.
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So far, we’ve spoken about Spatial Indexes as a general term. However, within this there are a number of index types. In this section, will cover three main types of Spatial Indexes:
H3 is a hexagonal Spatial Index, availaIble at 16 different resolutions, with the smallest covering an average area of 0.9m2, reaching up to and 4.3 million km2 at the largest resolution. Unlike standard hexagonal grids, H3 maps the spherical earth rather than being limited to a smaller plan of an area.
H3 has a number of advantages for spatial analysis over other Spatial Indexes, primarily due to its hexagonal shape - which is the closest of the three to a circle:
The distance between the centroid of a hexagon to all neighboring centroids is the same in all directions.
The lack of acute angles in a regular hexagon means that no areas of the shape are outliers in any direction.
All neighboring hexagons have the same spatial relationship with the central hexagon, making spatial querying and joining a more straightforward process.
Unlike square-based grids, the geometry of hexagons is well-structured to represent curves of geographic features which are rarely perpendicular in shape, such as rivers and roads.
The “softer” shape of a hexagon compared to a square means it performs better at representing gradual spatial changes and movement in particular.
Moreover, the widespread adoption of H3 is making it a great choice for collaboration.
However, there may be some cases where an alternative approach is optimal.
Quadbin is an encoding format for Quadkey, and is a square-based hierarchy with 26 resolutions.
At the most coarse level, the world is split into four quadkey cells, each with an index reference such as “48a2d06affffffff.” At the next level down, each of these is further reaching the most detailed resolution which measures less than 1m2 at the equator. This system is known as a quadtree key. The rectangular nature of the Quadbin system makes it particularly suited for modeling perpendicular geographies, such as gridded street systems.
Finally, we have S2; a hierarchy of quadrilaterals ranging from 0 to 30, the smallest of which has a resolution of just 1cm2. The key differentiator of S2 is that it represents data on a three-dimensional sphere. In contrast, both H3 and Quadbin represent data using the Mercator coordinate system which is a cylindrical coordinate system. The cylindrical technique is a way of representing the bumpy and spherical (ish!) world on a 2D computer screen as if a sheet of paper were wrapped around the earth in a cylinder. This means that there is less distortion in S2 (compared to H3 and Quadbin) around the extreme latitudes. S2 is also not affected by the “break” at 180° longitude.
As we mentioned earlier, H3 has a number of advantages over the other index types and because of this, it is fairly ubiquitous. However, before you decide to move ahead with H3, it’s important to ask yourself the following questions which may affect your decision.
What is the geography of what I’m modeling? This is particularly pertinent if you’re modeling networks. In some cases, the geometry of hexagons is less appropriate for modeling perpendicular grids, particularly where lines are perpendicular with longitude as there is no “flat” horizontal line. If this sounds like your use case, consider using Quadbin or S2.
Where are you modeling? As mentioned earlier, due to being based on a cylindrical coordinate system, both H3 and Quadbin cells experience greater area distortion at more extreme latitudes. However, H3 does have the lowest shape-based distortion at different latitudes. If you are undertaking analytics near the poles, consider instead working with the S2 index which does not suffer from this. Similarly, if your analysis needs to cross the International date Line (180° longitude) then you should also consider working with S2, as both H3 and Quadbin “break” here.
What index type are your collaborators using? It’s worth researching which index your data providers, partners, and clients are using to ensure smooth data sharing, transparency and alignment of results.
The resolution that you work with should be linked to the spatial problems that you’re trying to solve. You can’t answer neighborhood-level questions with cells a few feet wide, and you can’t deal with hyperlocal issues if your cells are a mile across.
For example, if you are investigating what might be causing food delivery delays, you probably need a resolution with cells of around 100-200 yards/meters wide in order to identify problem infrastructure or services.
It’s also important to consider the scale of your source data when making this decision. For example, if you want to know the total population within each index cell but you only have this data available at county level, then transforming this to a grid with a resolution 100 yards wide isn’t going to be very illuminating or representative.
Just remember - the whole point of Spatial Indexes is that it’s easy to convert between resolutions. If in doubt, go for a more detailed resolution than you think you need. It’s easier to move “up” a resolution level and take away detail than it is to move “down” and add detail in.
Learn more about working with Spatial Index "parent" and "children" resolutions in these tutorials.
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