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.

Read more from The Proptech Cloud

What Is A Geohash And How Is It Used?

Discover what a geohash is, how it works, and its real-world applications in mapping, logistics and data analysis.

What Is A Cadastre?

Learn how cadastres capture property boundaries, ownership details, and values, and see how technology is transforming them with 3D models, GIS and digital platforms.

Top Property and Proptech Events in Australia 2025

These industry events provide valuable opportunities to learn from industry leaders, explore emerging technologies, and network with peers who are shaping the future of the sector.

The Shifting Landscape of Property Ownership in Australia

Uncover the ways in which fintech and proptech are revolutionising property ownership in Australia, making it more accessible and dynamic than ever before.

Parcels, Titles, Addresses and Properties. What’s The Difference?

​Parcels, titles, addresses and properties. These terms are often used interchangeably in real estate, but are they the same thing?

Geohashes and Efficient Geospatial Joins in Snowflake

Geohashes and Efficient Geospatial Joins in Snowflake

Geohashes are an incredibly useful tool when it comes to spatial analysis. They serve as an encoding system that translates geographic coordinates into a short string of letters and digits, which simplifies and optimises geospatial operations.

One area where geohashes shine is in making geospatial joins more efficient. In this blog, we’ll dive into what geohashes are, and how you can leverage Snowflake’s ST_GEOHASH function to improve your geospatial joins in Snowflake.

What is a geohash?

A geohash is a hierarchical spatial data structure that subdivides space into a grid of cells, each cell having a unique string identifier. Geohashes convert a two-dimensional geographic coordinate (latitude and longitude) into this alphanumeric string. The length of the string determines the precision of the geohash; a longer string means a more precise location.

Read our blog on What is a Geohash for a detailed overview.

Geohash

How geohashes make geospatial joins more efficient

Geospatial joins can be computationally expensive because they often require pairing each record in one dataset with every record in another to calculate distances or find overlaps. This can lead to a computational complexity of O(N*M), which is not ideal for large datasets.

Geohashes simplify this problem by converting the geospatial coordinates into strings. When you want to join based on geographic proximity, you can simply perform a string comparison, which is far less computationally expensive than a full spatial join.

Snowflake and ST_GEOHASH

Snowflake offers native support for geospatial functions, including ST_GEOHASH. Below is a simple example of how you can use this function to create a geohash in Snowflake:

-- Create a geohash for a specific latitude and longitude
SELECT ST_GEOHASH(37.7749, -122.4194, 12) AS geohash;
In this example, 37.7749 is the latitude, -122.4194 is the longitude, and 12 is the precision of the geohash.

To perform a geospatial join using geohashes, you can do the following:

-- Create two tables with geospatial data
CREATE TABLE locations1 (id INT, latitude FLOAT, longitude FLOAT);
CREATE TABLE locations2 (id INT, latitude FLOAT, longitude FLOAT);

-- Populate tables (this is just a representation)
-- ...

-- Add a geohash column to both tables
ALTER TABLE locations1 ADD COLUMN geohash STRING;
ALTER TABLE locations2 ADD COLUMN geohash STRING;

-- Update the geohash columns using ST_GEOHASH
UPDATE locations1 SET geohash = ST_GEOHASH(latitude, longitude, 12);
UPDATE locations2 SET geohash = ST_GEOHASH(latitude, longitude, 12);

-- Perform the join using the geohash
SELECT a.*, b.*
FROM locations1 a, locations2 b
WHERE a.geohash = b.geohash;

 

Geohash – Streamlining geospatial joins

Geohashes offer a streamlined way to perform geospatial joins, drastically reducing the computational resources required. With native functions like ST_GEOHASH in Snowflake, it’s easier than ever to incorporate geohashes into your geospatial workflows. By leveraging the power of geohashes, you can perform complex geospatial analyses more efficiently, saving both time and money.

Read more from The Proptech Cloud

What Is A Geohash And How Is It Used?

Discover what a geohash is, how it works, and its real-world applications in mapping, logistics and data analysis.

What Is A Cadastre?

Learn how cadastres capture property boundaries, ownership details, and values, and see how technology is transforming them with 3D models, GIS and digital platforms.

Top Property and Proptech Events in Australia 2025

These industry events provide valuable opportunities to learn from industry leaders, explore emerging technologies, and network with peers who are shaping the future of the sector.

The Shifting Landscape of Property Ownership in Australia

Uncover the ways in which fintech and proptech are revolutionising property ownership in Australia, making it more accessible and dynamic than ever before.

Parcels, Titles, Addresses and Properties. What’s The Difference?

​Parcels, titles, addresses and properties. These terms are often used interchangeably in real estate, but are they the same thing?