What Is A Geohash And How Is It Used?

What Is A Geohash And How Is It Used?

What Is A Geohash?

Geohashing is a geocoding system created in 2008 that encodes geographic coordinates (latitude and longitude) into a short and compact string of characters made up of letters and numbers. The resulting compact string of characters is a Geohash.

Think of it like a secret code for locations on Earth – a short and easy-to-understand code. It turns a place’s latitude and longitude, which are like its coordinates on a big map, into a simple code.

For example, the coordinates “latitude 40.7128 degrees North and longitude 74.0060 degrees West” is the same as geohash “6gkzwgjz.” – this shorter code pinpoints the same location but is much easier to share or remember.

In a nutshell, a geohash is a clever way to turn complex latitude and longitude coordinates into simple codes that make it easier to talk about places and share location information.

This is also what makes geohash a versatile tool, making it easier to work with location-based data and services in a wide range of industries and applications.

Breaking Down Geohash – How the World is Encoded into Precise Rectangles

The main concept behind Geohash is to divide the world into a grid of rectangles and then represent each of these rectangles with a unique code. This code becomes shorter as you zoom out to cover larger areas and gets longer as you zoom in for more precision.

Here’s a technical explanation of how Geohash works:

  1. Dividing the world into rectangles
    Geohash starts by dividing the Earth into a grid of rectangles. The whole Earth is initially represented by one big rectangle.
  2. Choosing a binary representation
    Each rectangle is further divided into smaller rectangles. Geohash uses a binary search approach to determine which half of a rectangle contains a given point.
    It assigns a binary digit of 1 for the upper half of the rectangle and 0 for the lower half.
  3. Building the geohash string
    Starting with the whole Earth as one big rectangle, geohash determines whether a point falls into the top or bottom half (binary digit 1 or 0) and appends that digit to the geohash string.The Earth is then divided into the top or bottom half based on the chosen digit.
    This process is repeated iteratively until you have the desired level of precision or length in the geohash string.
  4. Precision and length of the geohash
    The length of the geohash string determines the level of precision: A shorter geohash represents a larger area, while a longer geohash represents a smaller and more precise area:
    – A Geohash of length 1 represents a large area, such as a continent.

    – A Geohash of length 2 represents a smaller area, such as a country.
    – A Geohash of length 3 represents a region or large city.
    – A Geohash of length 4 represents a smaller city or town.
    – Geohashes of length 5 or higher represent progressively smaller areas, down to street level and even specific locations.

  5. Encoding characters
    To enable the geohash string to be more easily read by humans and generally be more user-friendly, it translates the binary digits into a set of characters. Geohash typically uses a character set of 32 characters: 0-9 and the letters a through z (excluding i, l, o).
  6. Final geohash
    The result is a geohash string that represents a specific geographic location. This string is both compact and can be easily shared or stored.

What Does Geohash Look Like?

Geohash divides the world into a grid of rectangles, as indicated in this portion of the world map.

Geohash

Use Cases of Geohash

Geohash is widely used for its simplicity and efficiency in spatial indexing, making it useful for a wide range of practical applications across industries.

  • Location-based searches
    Geohash is used in location-based search engines and applications, showing nearby points of interest, businesses, or places based on their current location or a specified area.
  • Geofencing
  • Geohash is employed to create geofences, or virtual boundaries, which can be used for triggering actions or alerts when a device or user enters or exits a specific geographic area, such as location-based marketing, asset tracking, security applications, social networking and even dating apps.
  • Mapping and cartography
    Geohash is used in cartography to represent geographic data efficiently. It can simplify the storage and retrieval of spatial information in mapping systems.
  • Digital adventures
    Users can connect with people and discover new destinations worldwide via apps which leverage geohashes to enhance travel journaling and social interactions, making global adventures more interactive and engaging.

These are just a few examples, and the applications of geohash continue to expand as location-based data becomes increasingly important across various domains.

The simplified representation of geographic coordinates makes geohash more accessible and user-friendly, which is why it is such a versatile geocoding system with a wide range of uses.

How Does Geohash Compare With Other Spatial Encoding Systems?

A geohash is one of many spatial encoding systems designed to represent geographic locations compactly and efficiently. Geohash can be substituted with alternative systems depending on the specific requirements of the application.

Each spatial encoding system is tailored to address particular needs, offering unique features and trade-offs that make some systems more suitable for specific use cases. For example, geohashes excel in simplicity and spatial indexing, while systems like H3 or Plus Codes may provide advantages in precision, scalability, or user-friendliness.

Read our blog which compares Geohash with H3>

Originally published: 26 September, 2023

Last updated: 26 November, 2024

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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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