Geohash vs H3: Which Geospatial Indexing System Should I Use?

Geohash vs H3: Which Geospatial Indexing System Should I Use?

For years, the go-to geospatial indexing system has been Geohash. However, a relative new contender has emerged, challenging the status quo – H3. So should you use Geohash or H3?

Here, we’ll explore the differences between Geohash and H3, to help you decide which geospatial indexing system best suits your needs.

Geohash: A Familiar Friend

Geohash is a widely-used geocoding system that encodes geographic coordinates into a short string of letters and numbers. It divides the world into a grid of rectangles, each with a unique Geohash code. The longer the Geohash string, the more precise the location it represents.

H3: The Challenger

H3, on the other hand, is a relatively newer geospatial indexing system that’s gaining traction for its unique approach. Developed by ride-sharing company Uber, H3 uses a hexagonal grid to represent the Earth’s surface. Each hexagon is assigned a unique H3 index, offering a different perspective on geospatial indexing compared to Geohash.

Comparing Geohash and H3

We delve into the main differences between Geohash and H3 on a number of measures.

Precision

  • Geohash: Precision varies based on the length of the code. Longer codes are more precise, but this increases storage and complexity.
  • H3: H3 offers consistent precision regardless of location. Hexagons can be further subdivided for more precision, ensuring uniformity.

Spatial Relationships

  • Geohash: Geohash’s rectangular grid can struggle to represent spatial relationships accurately, especially near the poles (it should be noted that realistically, this is not going to be an issue in most use cases).
  • H3: H3’s hexagonal grid provides better spatial relationships, making it ideal for applications like ride-sharing services and navigation.

Support and Ease of Use

  • Geohash: Geohash is simple and widely adopted, making it easier to find resources and libraries for various programming languages.
  • H3: While H3 is gaining popularity, it may not have the same level of community support and resources as Geohash.

Applications

  • Geohash: Geohash is well-suited for applications that require basic geospatial indexing, such as location-based search or geofencing.
  • H3: H3 shines in complex applications like urban planning, logistics, and ride-sharing due to its consistent precision and better spatial relationships.

Scalability

  • Geohash: As Geohash codes get longer for more precision, storage and indexing can become inefficient.
  • H3: H3 scales more efficiently because it maintains uniform precision, regardless of location.
Geohash vs H3 Comparison

Source: H3

Geohash or H3: Choosing the right system

When it comes down to the choice between Geohash and H3, it really depends on your specific needs:

  • If you require a straightforward geospatial indexing system for basic applications, Geohash is a reliable choice with extensive community support.
  • On the other hand, if you’re dealing with complex spatial relationships, require consistent precision, or are working on innovative projects like urban planning or ride-sharing services, H3 offers a more promising solution. In the real estate context, it can be useful in urban planning, geofencing, spatial analysis, property market analysis.

Geospatial indexing is a fundamental technique used to manage and organise geographic or location-based data efficiently, in order to make data-based decisions or enhance applications.

Geohash is the old guard, tried and tested, while H3 is the newcomer with fresh ideas and uniform precision.

As we can see, both Geohash and H3 have their merits. However, the ultimate decision of which system to use should be based on the requirements of your project.

Snowflake releases H3 functionality

Snowflake provides SQL functions that enable you to use H3 with GEOGRAPHY objects.
This preview feature is now available to all accounts.

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What is H3?

What is H3?

There are a number of geospatial indexing systems which caters to spatial data types, query requirements, and use cases, with the choice often depending largely on the needs of your geospatial application and type of data. H3 is the relatively newer kid on the geospatial block, promising accuracy and scalability. Let’s delve in to understand its defining characteristics, how it works, and its practical applications.

What is H3?

H3 is a geospatial indexing system developed by Uber Technologies. It’s designed to partition the Earth’s surface into a hierarchical grid of hexagons. Each hexagon is assigned a unique H3 index, and this grid provides a way to represent and analyse geographic data with consistent precision.

In simpler terms, H3 is a way of breaking down the world into pieces, similar to how a jigsaw puzzle has pieces that fit together. These pieces are shaped like hexagons, like the honeycomb in a beehive.

These hexagons come in different sizes, so bigger hexagons can be used to talk about big areas like a country, whereas small hexagons can be used to talk about tiny areas like a neighbourhood.

Each of these hexagons is assigned a special code to help computers and maps understand where a place is on Earth. So instead of saying you’re at a certain latitude or longitude, you can simply give the code and your location can be pinpointed exactly.

Key characteristics of H3

  1. Hierarchical Grid
    This geospatial indexing system uses a hierarchical structure with multiple levels of hexagons. At each level, hexagons are subdivided into smaller hexagons, providing a scalable way to represent locations at different levels of detail.
  2. Uniform Precision
    Uniform precision across the globe means that hexagons at the same level of the hierarchy will represent approximately the same area, and are consistently spaced between hexagons.
  3. Spatial Relationships
    H3 provides better spatial relationships than traditional rectangular grids like latitude and longitude or Geohash. Hexagons have a more natural fit for mapping many real-world features and are less prone to distortions, especially near the poles.
  4. Resolution Levels
    By supporting multiple resolution levels, this system allows users to choose the appropriate level of detail for their application. Higher resolution levels provide more precision but may result in a larger number of hexagons to manage.
  5. Efficient Spatial Queries
    H3 makes it efficient to perform spatial queries, such as point-in-polygon tests, nearest-neighbor searches, and spatial aggregations. This is particularly valuable for applications like ride-sharing, logistics, and urban planning.
  6. Open Source
    H3 is open-source and available to the public, making it accessible for developers and researchers to use and contribute to its development.
  7. Geospatial Libraries
    H3 has been integrated into various geospatial libraries and programming languages, making it easier for developers to work with this geospatial indexing system in their applications.

How does H3 work?

Here’s a technical explanation of how H3 works:

  1. Hexagonal Grid
    H3 starts by subdividing the Earth’s surface into hexagonal grids. These hexagons are the basic building blocks of the system.
  2. Hierarchical Levels
    H3 employs a hierarchical approach with multiple zoom levels. At each zoom level, the hexagons are divided into smaller hexagons. This hierarchy allows for representing locations with varying levels of precision.
  3. Unique Hexagon IDs
    Each hexagon in the grid is assigned a unique identifier called an H3 index. These indices are used to identify specific geographic areas. An H3 index consists of two parts: a base cell and a resolution level. The base cell determines the general area, and the resolution level refines the precision within that area.

What does H3 look like?

This geospatial indexing system partitions the globe into hexagons for accurate analysis, as indicated in this image.

Geohash vs H3 Comparison

Source: Uber

Real estate applications of H3

As you can imagine, a geospatial indexing system developed by ride-share company, Uber would make it indispensable for ride-sharing and navigation, optimising driver and passenger matching, but also in determining best pickup and drop off points, fare calculations and route planning.

Due to its ability to represent geo locations accurately and analyse geographical data efficiently, it has wide appeal and vast uses in real-estate too. In most situations, anytime you might use the more commonly used Geohash, you could potentially use H3.

So, how does H3 compare?

H3 is one of the geospatial indexing systems at your disposal, answering to various spatial data types, query requirements, and use cases. However, the choice between using H3 and other indexing systems depends largely on the needs of your geospatial application and type of data.

Read how H3 and Geohash compare if you’re considering which system to adopt.

Snowflake releases H3 functionality

Snowflake provides SQL functions that enable you to use H3 with GEOGRAPHY objects.
This preview feature is now available to all accounts.

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