What is a Geodetic Datum?

What is a Geodetic Datum?

A geodetic datum can be described as a reference point or starting line that helps us measure and describe locations on the Earth’s surface.

Let’s use a sporting example to explain the concept.

Say you’re playing baseball, and have a home base where you start your game. This home base is like a geodetic datum. It’s a fixed point on the field that everyone agrees on as the reference point for scoring runs.

In baseball, all the distances are measured from home base. For example, how far you hit the ball or how far you run around the bases is based on your relationship to that central point. Without a fixed home base, it would be challenging to keep track of scores and positions accurately.

In the world of geography and mapping, Earth’s surface is vast and not perfectly flat, so we need a similar reference point. And a geodetic datum does just this, serving as a foundation for mapping and navigation.

What is a geodetic datum?

A geodetic datum is a model that defines the shape and size of the Earth’s surface, as well as the location of a reference point (the origin) and the orientation of the axes.

A geodetic datum provides the framework for measuring and comparing positions on the Earth’s surface.

Components

A geodetic datum consists of several key components:

  • Reference Ellipsoid: Describes the shape of the Earth (e.g., WGS 84).
  • Geodetic Center: Specifies the Earth’s centre point.
  • Prime Meridian: Defines the longitudinal reference line (e.g., Greenwich Meridian).

What purposes does a geodetic datum serve?

Geodetic datums serve as a reference framework for defining the shape of the Earth and its orientation. They provide a consistent basis for measuring latitude, longitude, elevations and are essential in geospatial analysis.

Geodetic datums help us create accurate maps, GPS navigation and positioning, and other location-based systems by giving us a standardised starting point to measure distances and positions from.

Different types of geodetic datums

Some geodetic datums are global, aiming to provide a worldwide reference framework, although many countries and regions around the world commonly use different geodetic datums to best fit the curvature of the Earth in their boundaries for greater accuracy. They may also vary from one region to another due to historical, technical, and practical reasons.

Examples of commonly used geodetic datums include the World Geodetic System 1984 (WGS84) and the North American Datum 1983 (NAD83).

Geodetic datum used in property and real estate

How geodetic datums are used in property and real estate

Geodetic datums are fundamental to property and real estate by providing a standard referencing system for defining property boundaries, mapping, location-based services, and decision-making processes related to land use and property transactions.

  • Property Surveys:
    When a property is surveyed, geodetic datums provide a reference framework for precisely locating property boundaries and corners. Surveyors use coordinates based on the datum to define the property’s position on the Earth’s surface accurately. This is critical for property boundaries, ensuring that the land’s legal description is accurate.
  • Land Records and Title Deeds:
    Property records and title deeds often include coordinate information based on a specific geodetic datum. This information ensures the accuracy and consistency of land ownership records.
  • Geographic Information Systems (GIS) Mapping: GIS used in the real estate industry rely on geodetic datums to create digital maps. These maps help real estate professionals and government agencies manage property information, zoning, and land use more effectively.
  • Location-Based Services:
    Real estate agents and online platforms use mapping applications that rely on geodetic datums to display property locations accurately. This helps potential buyers or renters understand the property’s precise location and nearby amenities.
  • Property Valuation:
    Geodetic datums can be used in property valuation models to consider factors like the property’s location, proximity to schools, transportation, and other geographic features. These factors can affect a property’s value.
  • Land Use Planning:
    Urban and regional planners use geodetic datums to assess the suitability of land for different purposes, such as residential, commercial, or industrial development. They consider geographic factors and zoning regulations.
  • Environmental Impact Assessment:
    When evaluating the environmental impact of a real estate development, geodetic datums help assess factors like proximity to water bodies, floodplains, and protected areas.
  • Infrastructure Development:
    Geodetic datums are essential for planning and constructing infrastructure, such as roads, utilities, and public transportation systems. Accurate location information ensures that developments are built correctly.
  • Property Insurance:
    Insurers may use geodetic datums to assess the risk associated with a property’s location, particularly regarding natural disasters like floods or earthquakes.

Geodetic datums are used widely by proptechs and serve a number of purposes in real estate and property management. 

Read our blog to learn about the geodetic datums we use and reference in Australia.

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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.

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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.

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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.

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The Role of Data in Real Estate Decisions

The Role of Data in Real Estate Decisions

Businesses use a wide range of data to confidently analyse trends, forecast changes, and identify opportunities. In today’s competitive real estate industry, the data behind informed business decisions can be the difference between success and failure.

Why is data-driven decision-making so important in real estate?

With the advent of big data and accessibility of data, companies are now able to make more accurate and strategic decisions by analysing information, key trends and metrics. Data-driven decision-making has become critical in real estate because it allows businesses to identify opportunities, reduce risks and maximise returns on investments.

Using data in real-estate decisions

Some examples may include:

  • Real estate investment decisions

    Real estate and related data as diverse as historical sales data, demographics, market demand, property valuations can be used to identify profitable investment and development opportunities, determine a value of a property and assess potential returns on property investments.

  • Retail analytics

    Property data together with foot traffic data, demographics, sales data, can play a role in location selections and market expansion decisions. It will also influence merchandising decisions such as retail promotions and campaigns, product placement, store layout and inventory management.

  • Real estate sales decisions

    By analysing current and historical market trends, businesses can determine optimal pricing strategies for properties. Similarly demographic data can be used to identify the target audience for a particular property and inform advertising and marketing efforts.

  • Mortgage and financing decisions

    For risk assessments, lenders use property data to evaluate the value and condition of collateral for mortgage loans, determining loan eligibility and interest rates.
    On the flipside, credit scoring models may incorporate property data to evaluate borrower creditworthiness.

  • Government and urban planning

    Urban planners use property data to identify areas in need of infrastructure development, such as roads, schools, and utilities. Governments may use property data to enforce zoning regulations and property tax assessments.

  • Building and construction planning

    Property data aids in estimating construction costs, project timelines, and feasibility studies.

  • Insurance underwriting and claims

    Insurers consider property data when determining premiums and coverage for homeowners and property insurance policies. Property data may play a role in processing claims by verifying property details and assessing damage.

  • Environmental impact assessment

    Property, environmental, regulatory and geospatial data may all factor into decisions made concerning property projects.

Tips for interpreting and analysing property data

While data-driven decision-making is a valuable tool in real estate, it is important to understand how to properly analyse and interpret property data.

Here are some tips to keep in mind:

  • Data Accuracy

    Ensure the data you’re using is accurate and up-to-date. Rely on reputable sources and verify the information where possible.

  • Compare and Contrast

    Don’t make decisions based on a single data point. Compare property data from different sources and periods to identify trends and outliers.

  • Consider Context

    Understand the broader economic and market context in which the data exists. External factors like interest rates, local regulations, and economic conditions can significantly impact real estate data.

  • Data Visualisation

    Utilise data visualisation tools to transform complex data sets into easy-to-understand graphs and charts. Visual representations can highlight patterns and trends.

  • Consult Experts

    When in doubt, seek advice from experienced proptech professionals or data scientists and analysts. They can provide valuable insights and guidance in interpreting property data effectively.

  • Human Judgement

    Don’t ignore intuition and personal experience entirely. Data is important, but it should be used to inform decisions, not replace human judgement.

As we’ve explored, various types of data play a pivotal role in shaping business decisions. In an era of big data and accessible information, real estate professionals have the tools at their disposal to analyse trends, forecast changes, and seize opportunities like never before.

Businesses can navigate the intricate terrain of real estate with greater precision, with data illuminating the path.

Human judgment and expertise are still indispensable. Seek out experts, draw from your intuition, and let data guide your decisions, not dictate them.

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