What is a Coordinate Reference System (CRS)?

What is a Coordinate Reference System (CRS)?

TL;DR

A Coordinate Reference System (CRS) is a framework that uses coordinates to accurately locate places on Earth’s surface.

In real estate, CRS is crucial for defining property boundaries, aiding in surveys, mapping, and even in property valuation.

There are different types of CRS, each serving specific mapping needs.

For property and urban planning, CRS ensures precise land registration, zoning, and infrastructure development, helping reduce disputes and enhance planning accuracy.

Imagine you’re taking part in a global treasure hunt or a real-life challenge like The Amazing Race, where the Earth itself is your game board. To navigate to your next destination, you rely on a giant map. But how do you know where exactly to go? You need a set of rules to interpret the map accurately and describe specific locations.

This is where a Coordinate Reference System (CRS) comes in.

Think of CRS like a universal language for maps, providing a framework for assigning coordinates (like X and Y on a grid) to different locations on Earth.

With this system in place, everyone can refer to the same locations using a consistent method, ensuring clarity and accuracy when navigating or sharing geographic data.

Let’s dive deeper into what a CRS is and why it’s essential for interpreting the world around us.

Coordinate Reference System (CRS)

A Coordinate Reference System (CRS) is a system that uses numbers to precisely pinpoint locations on maps or the Earth’s surface, ensuring everyone uses the same rules for describing positions.

By defining sets of coordinates and a standardised framework, it offers a consistent way to specify locations, making it possible for maps and geographic data to be accurately interpreted and shared.

A CRS typically includes a reference point, a set of axes, and a unit of measurement.

What is a CRS used for?

A Coordinate Reference System (CRS) is used to accurately represent, map, and interpret coordinates in a specific geographic or projected space. It enables precise location referencing on the Earth’s surface, ensuring that geographic data can be shared and understood consistently.

CRS is widely used in geography to study the Earth’s physical features, environments, and human interactions with these landscapes. It’s also crucial in cartography, where it ensures that maps are created with accurate geographic information, allowing for reliable navigation and analysis across various fields and applications.

Different Types of CRS

  • Geographic CRS: Based on a spherical or ellipsoidal model of the Earth’s surface, commonly using latitude and longitude coordinates.
  • Projected CRS: Maps the three-dimensional spherical or ellipsoidal Earth onto a two-dimensional plane, such as a map or a flat surface. Examples include Universal Transverse Mercator (UTM) and State Plane Coordinate Systems.
  • Vertical CRS: Specifies elevations or depths relative to a reference surface (e.g., sea level).

Some examples of commonly used CRS include:

Practical applications of CRS in property and real estate

Coordinate Reference Systems (CRS) play a vital role in real estate and proptech by providing a standardised way to accurately represent geographic locations. Here are some practical applications:

Property Mapping and Visualisation

  • CRS allows for exact delineation of property boundaries, and accurate property boundaries are essential for legal and planning purposes.
  • CRS can be used by proptechs to create interactive property maps, allowing users to explore listings in a spatial context.

Location-Based Services

  • CRS can be used to calculate distances between properties and amenities like schools, parks, or public transport for proximity analysis.
  • Create virtual boundaries, or geofences, around properties for targeted marketing or notifications.

Urban Planning and Development

  • Ensure new developments comply with local zoning regulations by accurately positioning them within zoning maps.
  • Precise spatial data derived from CRS can be used in the planning of utilities and infrastructure for new real estate developments

Property Valuation

  • Accurately analyse and compare property locations, boundaries and size, and their impact on value.
  • Determine a property’s elevation and proximity to flood zones for risk assessment and insurance purposes.

Virtual and Augmented Reality

  • Virtual property tours are possible with the creation of georeferenced 3D models of properties.
  • Overlay property information on real-world views using mobile devices for use in augmented reality applications.

Data Integration and Analysis

  • Combine property data with other geographic information like demographics, crime rates, or environmental data for comprehensive analysis.
  • Perform complex spatial queries to identify properties meeting specific geographic criteria.

As can be seen in these examples, by leveraging Coordinate Reference Systems, real estate professionals and proptech companies can provide more accurate, data-driven services and make better-informed decisions based on spatial relationships and geographic context.

Originally published: 4 October, 2023

Last updated: 12 September, 2024

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Why Is It So Difficult To Parse Addresses?

Why Is It So Difficult To Parse Addresses?

Precise address data is fundamental to a multitude of services.

The ability to accurately dissect and interpret address components is important for the accurate delivery of mail, managing customer databases, integrating geographic information systems and more.

This blog explores what address parsing is and why it presents such unique challenges.

Discover the intricacies behind making sense of seemingly simple address data and why getting it right is more complicated than it first appears.

TL;DR

Address parsing involves breaking down addresses into their individual components (like street name, city, state, and postcode/ZIP code) to make them understandable for computers.

It’s challenging due to variations in address formats, international differences, ambiguous elements, complex building details, and lack of standardisation.

Despite these difficulties, commercial address parsers achieve high accuracy, and emerging machine learning techniques offer potential for developing custom solutions.

What is Address Parsing?

In essence, address parsing is breaking down and identifying the individual components of an address to make it more understandable and usable for computers. This process ensures that each part of the address is correctly identified, interpreted and standardised for greater accuracy in subsequent applications.

Let’s take a letter that you receive in the mailbox.

On the front, there’s a block of text with your name, street address, city (or suburb or town), state, and postcode (or ZIP code). All these combined tell the postman where to deliver the letter.

Now, let’s say you have a robot assistant, and you want to teach it to understand and organise this information.

You’d instruct the robot to recognise the different parts of the address: This part is the person’s name. This is the street they live on. This part tells us the city, and so on.

Address parsing is like teaching the robot to recognise and separate these individual parts of the address. So, instead of seeing one big block of text, the robot (or computer program) sees the address as different pieces of information:

  • name,
  • street,
  • city,
  • state, and
  • ZIP code/postcode.

This helps computers and software understand and manage addresses more efficiently, just like how you can easily tell apart the street name from the city when you look at the address on a letter.

Why is Address Parsing Difficult?

Address parsing is difficult because addresses vary greatly in format and structure, both within and across countries. Ambiguous elements (e.g., “St.” for “Street” or “Saint”), complex building details, misspellings and multiple languages add to the challenge.

Additionally, addresses often change due to renaming or updates, and there is very little standardisation in how people enter addresses.

These factors make it hard to create a parser that can accurately interpret all possible address variations.

These are some examples that demonstrate the complexity involved. 

Example 1

Address = 64 YORK STREET SYDNEY NSW 2000.

  • 64 = Street number,
  • YORK = Street name,
  • STREET = Street type,
  • SYDNEY = Suburb,
  • NSW = State,
  • 2000 = Postcode

Done, why do people tell me it is difficult….?

 

Example 2

Address = 6/64 THE BOULEVARDE STRATHFIELD NSW 2135

  • 6 = Unit number
  • 64 = Street number
  • THE = Street name
  • BOULEVARDE = Street type
  • STRATHFIELD = Suburb
  • NSW = State
  • 2135= Postcode

Wait, the street name is “THE”?
It should be the “THE BOULEVARDE”!
Boulevarde is a street type as well, but not in this instance! We need a rule for that!

 

Example 3

Address = WTC BLDG A / TWR 4 MATTHEW FL LEVEL 1 18-38A SIDDELEY ST, DOCKLANDS VIC 3008

This address is significantly more difficult to parse than previous examples, however the address still includes many prefixes that can assist with parsing.

It is not uncommon for many of these prefixes to removed to look more like this address:

Address = WTC A / TWR 4 MATTHEW 1 18-38A SIDDELEY ST, DOCKLANDS VIC 3008

Without the BLDG and LEVEL prefixes, we now have additional complexity to deal with.

Challenges with Address Parsing

  • Variability in formats
    Addresses can be written in numerous formats.
    For instance, “123 Maple St. Apt 4B” and “Apt 4B, 123 Maple Street” represent the same location but are formatted differently.
  • International differences
    Different countries have different address structures. What’s common and straightforward in one country might be unusual in another. For instance, some countries might include districts or regions in their addresses, while others don’t.
  • Ambiguous elements
    Some parts of an address can be confused for others.
    For instance, “St.” could be short for “Street” or “Saint.”
    Without context, determining the correct interpretation can be tough.
  • Complex building details
    Addresses can have complex unit numbers, building names, floor numbers, and so forth.
    Parsing these details correctly, especially when they’re in non-standard formats, can be difficult.
  • Misspellings and typos
    People often make mistakes when entering addresses. A parser needs to be robust enough to handle and possibly correct common misspellings or recognise when an address might be invalid.
  • Multiple languages and scripts
    In multilingual countries or regions, addresses might be written in different languages or scripts. Parsing these requires the program to be aware of multiple linguistic structures.
  • Historical changes and inconsistencies
    Cities change, streets get renamed, postal codes get updated. An address parser needs to be updated regularly to account for these changes, or it should be robust enough to recognise and possibly map outdated addresses to their current counterparts.
  • Abbreviations and Synonyms
    There are multiple ways to refer to the same thing in addresses. For example, “Avenue” might be written as “Ave,” “Av,” or “Avnue.” A parser must recognise all these variations as referring to the same concept.
  • Lack of standardisation
    Unlike some data types where a strict format can be enforced, addresses are often entered by users who have no idea about the backend system’s preferred format.
  • Embedded information
    Sometimes, addresses can contain extra information that’s not strictly part of the address but is crucial for delivery, like instructions or landmarks.
Address block on a letter

Is Accurate Address Parsing Possible?

Most commercial address parses achieve parsing accuracy at a rate of 97/98%+.

They achieve this through constant development, testing and refinement of their software over many years.

Is it possible to build your own address parsing solution and achieve similar results?

Maybe.

New capabilities and accessibility of machine learning algorithms mean self-developed address parsing solutions may be able to produce results that are acceptable for your use case. But it is worth noting, the solution won’t be easy to develop and there will be inaccuracies. You should carefully weigh up the effort to develop an address parsing solution vs buying a solution off the shelf.

 

Address Parsing Software Providers

Australia:

  • Geoscape Australia: Provides geospatial data solutions, including address parsing and geocoding for Australian addresses.
  • Precisely: They offer global solutions, including for Australia, in the realm of data quality and address management.
  • Equifax Australia: Offer address cleansing and geocoding solutions.

USA:

  • SmartyStreets: Offers address validation, geocoding, and parsing primarily for the U.S. but also internationally.
  • Melissa Data: Provides data quality solutions, including address validation, correction, and parsing for the USA and other countries.
  • Pitney Bowes: Global solutions, including for the U.S., in data quality and address management.

Canada:

  • Canada Post: Their AddressComplete solution provides parsing, validation, and autocomplete for Canadian addresses.
  • DMTI Spatial: Offers Canadian geospatial data solutions, which include address parsing and validation.

UK:

  • PCA Predict (Loqate): Provides address lookup, validation, and parsing solutions predominantly for the UK but also globally.
  • Allies Computing: Their PostCoder web service offers address lookup and validation for the UK and other countries.
  • Royal Mail: They have solutions for address validation and parsing for UK addresses.

It’s worth noting that many of these providers offer services for multiple countries, not just the ones listed under their respective headers. For example, a company that provides services in the USA might also cater to UK or Australian addresses.

When considering an address parsing provider, it’s essential to check if they cover the specific regions and countries you need, and if they offer the depth of functionality (e.g., address validation, geocoding, etc.) that your project requires.

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How Proptech Is Revolutionising Real Estate

How Proptech Is Revolutionising Real Estate

Real estate, the world’s largest asset class, valued at a staggering $7.56 trillion, has long been a sleeping giant when it comes to technological innovation. But now, it’s waking up. Recent years have witnessed an unprecedented surge in proptech.

What is Proptech?

PropTech is short for Property Technology which, as its name suggests, is the dynamic intersection of property and technology.

Broadly, it refers to the innovative use of technology in the real estate industry and covers a wide range of tech solutions and innovations aimed at disrupting and digitising various aspects of the real estate sector, including property management, leasing, sales, construction, investment and others.

Proptech tackles key issues in how we use and benefit from real estate. It’s already streamlining processes and transactions, creating new opportunities, addressing pain points, cutting costs, enhancing connectivity, productivity and boosting convenience for residents, owners, landlords and other stakeholders.

Why the Surge in Proptech?

Several key factors have contributed to the rapid rise of proptech. The COVID-19 pandemic significantly accelerated the need for virtual, no-touch experiences, driving technological innovation across the sector.

Technological advancements with practical applications in real estate have also played a crucial role. Examples of innovations include:

  • Virtual Reality (VR) and Augmented Reality (AR) enhancing property viewing experiences.
  • Artificial Intelligence (AI) and Machine Learning (ML) providing data-driven insights and personalised recommendations.
  • Internet of Things (IoT) enabling smart home features and efficient property management.
  • Blockchain Technology allowing fractional property ownership, offering new ways for buyers and sellers to connect and potentially cutting costs by removing intermediaries out of the transaction process.
  • Drone Technology offering virtual tours and aerial views,

Increased connectivity and the availability of real estate data, have improved customer experiences and enabled faster, more informed decisions in real estate transactions, planning and development.

Regulatory changes have also revolutionised the way real estate operates.

Regulatory changes serve as a catalyst for proptech innovation. By creating new challenges and setting higher standards, regulations drive the development of advanced technologies and solutions that help businesses comply, operate more efficiently, and enhance their services. This continuous push for innovation ensures that the real estate industry evolves to meet modern demands.

The pressing issue of housing affordability has spurred creative approaches to real estate ownership and investment too. Proptech and financial technology (fintech) are democratising property investment, making it more accessible through crowdfunding platforms, fractional ownership, and Real Estate Investment Trusts (REITs).

The potential for disruption and innovation in the real estate sector has attracted significant investor interest. Corporate venture capital units and accelerator programs further support and fast-track proptech startup funding.

Proptech’s Potential to Reimagine Real Estate

Proptech has gained significant traction in recent years as real estate professionals and investors recognise the potential of technology to disrupt.

According to PropTechBuzz, hundreds of Australian proptech startups are leveraging the power of advanced technologies like big data, AI, AR and generating over $1.4 billion of direct economic output.

Yet, we are only on the cusp of proptech’s true potential.

Signs show that this fledgling industry has yet to reach its pinnacle.

A recent Deloitte survey Global Real Estate Outlook Survey of real estate owners and investors across North America, Europe, and Asia/Pacific reveals:

  • Many real estate firms address years of amassed technical debt by ramping up technology capabilities. 59% of respondents say they do not have the data, processes, and internal controls necessary to comply with these regulations and expect it will take significant effort to reach compliance.
  • Many real estate firms aren’t ready to meet environmental, social, and governance (ESG) regulations. 61% admit their firms’ core technology infrastructures still rely on legacy systems. However, nearly half are making efforts to modernise.

Barriers to progress still exist.

A survey of 216 Australian property companies from 2021 by the Property Council of Australia and Yardi Systems show that

  • There is the perception that solutions must be specially developed or customised (34%).
  • 26% of respondents see changing existing behaviours as the biggest obstacle to overcome, followed by cost (23%) and time constraints (11%).

The Future of Proptech

The future of proptech is looking bright.

As new technology, trends, and other contributing factors converge to accelerate innovation in the real estate (and its neighbouring) sectors, new ideas take flight and promise to disrupt traditional processes.

Proptech brings exciting benefits, boosting the real estate industry’s digital presence, productivity and enhancing experiences for everyone involved.

It fosters innovation and automation, adding convenience, efficiency, transparency and accuracy to administrative and operational tasks.

Additionally, proptech holds the promise of better access to data and analytics and the integration of sustainability practices.

As technology continues to advance and consumer preferences evolve, proptech is likely to play an increasingly prominent role in shaping the future of the real estate industry.

Proptech revolutionising real estate

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What is the Australian Statistical Geography Standard (ASGS)?

What is the Australian Statistical Geography Standard (ASGS)?

What is the Australian Statistical Geography Standard (ASGS)?

The Australian Statistical Geography Standard (ASGS) is essentially a framework used to classify and organise geographical areas across Australia for the purpose of collecting, analysing, and disseminating statistics.

Mesh Block Changes

Edition 3 ASGS 2021 – Sydney Statistical Area 3 (SA3)
Source: ABS Maps

By organising Australia into a system of statistical areas, the ASGS can be used to perform standardised location-based analysis on things that matter to your business. For example, where your customers live or shop, where your competitors operate, where you have business assets located or where weather patterns are observed.

The ASGS is a detailed way of representing Australia’s geographical diversity in statistical data.

By dividing the country into hierarchical levels of statistical areas, ranging from broad regions to specific localities, it allows accurate and meaningful analysis of various data, including population, economic and environmental statistics – ultimately, to better understand where people live and how communities are formed.

The ASGS was introduced in 2011 to replace the previous Australian Standard Geographical Classification (ASGC).

Sydney CBD SA1 & SA2

Edition 3 ASGS 2021 – Sydney Central Business District (CBD) Statistical Area 1 (SA1) and Statistical Area 2 (SA2).
Source: ABS Maps

Sydney CBD SA2 & SA3

Edition 3 ASGS 2021 – Sydney CBD Statistical Area 2 (SA2) and Statistical Area 3 (SA3).
Source: ABS Maps

Sydney CBD SA3 & SA4

Edition 3 ASGS 2021 – Sydney CBD Statistical Area 3 (SA3) and Statistical Area 4 (SA4).
Source: ABS Maps

Updates of the Australian Statistical Geography Standard (ASGS)

The ASGS is revised and refreshed every five years to reflect changes in the population, demographics, the country’s development and geographic boundaries to ensure it remains relevant and useful for statistical purposes.

The third edition, linked to the 2021 Census, began rolling out in July 2021 and includes updates to various geographic categorisations, ending with the Remoteness Structure in March 2023.

The Australian Bureau of Statistics (ABS) oversees the ASGS and reviews it to ensure it meets current needs, incorporating feedback from public consultation. Additionally, they offer the online tool, ABS Maps, for exploring and comparing these statistical areas.

ASGS Edition 3 hierarchy of statistical areas

The Australian Statistical Geography Standard (ASGS) is represented through a hierarchical framework of statistical areas. 

The ASGS is split into ABS and non ABS Structures. Of these, ABS Structures are geographies designed by the ABS for the purposes of releasing and the analysis of statistics.

The hierarchy is made up of several nested levels, which enables a flexible and consistent approach to understanding and managing geographic data. 

Here is the ASGS Edition 3 Structures as published by the Australian Bureau of Statistics:

 

Main Structure and Greater Capital City Statistical Areas

  • Mesh Blocks: The smallest geographic unit, designed to cover all of Australia without gaps or overlaps. Mesh Blocks are the building blocks for larger statistical areas. Read more about Mesh Blocks>
  • Statistical Areas Level 1 (SA1): Groups of Mesh Blocks with populations between 200 and 800 people. They are used in the Census of Population and Housing.
  • Statistical Areas Level 2 (SA2): Aggregates of SA1s, generally with populations between 3,000 and 25,000 people. They represent functional areas that interact socially and economically.
  • Statistical Areas Level 3 (SA3): Groups of SA2s, reflecting regional cities and large urban transport hubs.
  • Statistical Areas Level 4 (SA4): Aggregates of SA3s, representing labour markets or regions with similar socioeconomic characteristics.
  • Greater Capital City Statistical Areas (GCCSA): Represent each of the eight state and territory capital cities.

Special Purpose Regions

Indigenous Structure

  • Indigenous Regions and Areas: Defined to facilitate the statistical analysis of the distribution of Aboriginal and Torres Strait Islander populations. The three hierarchical levels are: Indigenous Locations, Indigenous Areas and Indigenous Regions.

Remoteness Structure

  • Remoteness Areas: Classify areas based on their remoteness from services and population centres. The five remoteness classes are: Major Cities, Inner Regional, Outer Regional, Remote and Very Remote.

Significant Urban Areas, Section of State, Urban Centres and Localities

  • Urban Centres and Localities (UCLs): Define urban and rural areas based on population size and density.
  • Section of State (SOS): SOS groups the UCLs into classes of urban areas based on population size.

  • Classifying urban areas in several different ways is to allow statistical data to be made available to Australian towns and cities and for statistical analysis.

Non ABS Structures

  • Non ABS Structures: These are administrative regions that are not defined or maintained by the ABS. They include eight geographies: Local Government Areas, State Electoral Divisions, Commonwealth Electoral Divisions, Destination Zones, Postal Areas, Suburbs and Localities, Australian Drainage Divisions and Tourism Regions.

What tools can be used for representing and analysing ASGS?

These tools collectively offer a comprehensive approach to representing and analysing ASGS data.

They provide the necessary geographic context and detailed spatial data required for thorough analysis.

A combination of these resources can enable data professionals to enhance their analytical capabilities and produce insightful visualisations to support planning and informed decision making.

 

  • ABS Maps: An online tool provided by the Australian Bureau of Statistics (ABS) that allows users to explore and compare different statistical areas defined by the ASGS.
  • Geographic Information System (GIS) Files: GIS is a framework for gathering, managing, and analysing spatial and geographic data, encompassing a broad range of tools and functionalities. GIS files (formats may include shapefiles, GeoJSON, and KMLare) available for download, allowing for detailed spatial analysis using software like ArcGIS or QGIS.
  • Online Data Portals: Platforms such as the ABS Data Explorer or Australian National Map provide access to a variety of geographic data layers. This allows users to visualise and download data in different formats for analysis.
  • Statistical and Data Visualisation Software: R, Python (with libraries like Geopandas and Folium), and Tableau can be used to import ASGS data for advanced statistical analysis and visualisation, enabling the creation of custom maps and visual representations of the data.
  • Spatial Databases: Databases such as PostGIS that are optimised for storing and querying spatial data and facilitates efficient management and analysis of large geographic datasets.
  • Printed Maps and Documentation: Detailed maps and explanatory notes published by the ABS, outlining the boundaries and characteristics of each statistical area.
Analysis

What is the ASGS used for?

One of ASGS’s primary uses is for the Census, the study of Australia’s population.

The ASGS is integral to Australia’s Census as it defines geographic boundaries, facilitates detailed data collection and enumeration, enables comprehensive data analysis and reporting, and supports informed decision-making, resource allocation, policy making and planning by government agencies.

However it is also used broadly by the Australian Bureau of Statistics (ABS) and other entities.

The ASGS is versatile enough to support a wide array of activities involving the accurate collection of geographic and statistical data across Australia, making it a fundamental element in analysis.

 

  • Detailed demographics: The ASGS allows businesses to segment customers based on detailed geographic and demographic data. By using Statistical Areas (SA1 to SA4), companies can identify distinct customer groups within specific regions, tailoring marketing strategies and product offerings to the unique characteristics of each segment.
  • Targeted marketing: Businesses can use Mesh Blocks or SA1 regions to create hyper-local marketing campaigns, ensuring that promotional efforts are targeted towards areas with the highest potential for engagement and conversion.
  • Real estate and urban planning: The ASGS provides a robust framework for organising geographic areas to satisfy a broad range of uses in real estate. It can be used to enhance analysis of localities for a better understanding of local demographic through to reporting of property-related data. Examples include: analysis of housing trends, planning of urban developments, identifying growth areas or conducting property valuations with a clear understanding of geographic distinctions.
  • Accurate market data aggregation: The hierarchical structure of the ASGS enables businesses to aggregate and report market data accurately across various geographic levels, from local neighborhoods (SA1) to larger regions (SA4). This helps in understanding market trends and consumer behavior across different areas.
  • Market trend analysis: By comparing data across different Census periods using consistent ASGS boundaries, businesses can track market trends and demographic shifts, aiding in long-term strategic planning and investment decisions.
  • Business and market analysis: Companies leverage the ASGS for market analysis, site selection and strategic planning as it can help businesses understand demographic trends and geographical distributions of markets to inform targeted marketing and business expansion strategies.
  • Retail demand forecasting: Retailers can use ASGS data to forecast demand for products in different geographic areas. By understanding population density, age distribution, and income levels within mesh blocks or SA2 areas, businesses can optimise inventory levels to match local demand.
  • Supply chain efficiency: The ASGS framework helps retailers plan efficient distribution routes and manage stock levels more effectively, ensuring that products are available where they are needed most without overstocking or stockouts.
  • Competitor analysis: Businesses can use ASGS-defined regions to map out the geographic locations of competitors, analysing their market coverage and identifying potential gaps or opportunities for expansion.
  • Market share estimation: By integrating ASGS data with sales and demographic information, companies can estimate market share within specific regions, helping to assess competitive strengths and weaknesses.
  • Environmental risk assessment: The ASGS allows for precise mapping of environmental risks such as flood zones, bushfire-prone areas, and pollution levels. By overlaying environmental data with population and infrastructure data from the ASGS, businesses can assess the potential impact of environmental risks on their operations.
  • Environmental regulation compliance and planning: Companies can use ASGS data to ensure compliance with environmental regulations and to plan mitigation strategies for at-risk areas. This helps in safeguarding assets and maintaining operational continuity.
  • Research and academia: Researchers and academics use the ASGS for conducting spatial analysis and regional studies, allowing detailed investigations into socio-economic, environmental, and demographic conditions across different regions.

The essential role of ASGS

The Australian Statistical Geography Standard (ASGS) plays a foundational role in statistical geography, allowing users to analyse and visualise statistics based on location. Its extensive applications, regular updates, and strong endorsement by the ABS ensure its implementation and integrity.

As such, the ASGS remains an essential framework and a mainstay in Australian geographic and statistical analysis—an indispensable tool for ensuring the accuracy, consistency, and relevance of geographic data across various domains.

How to Incorporate Mesh Blocks into Datasets

Incorporating mesh blocks into datasets involves several steps to ensure seamless integration and effective utilisation of geographical information. Here’s a guide on how to incorporate mesh blocks into datasets.

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What Are Mesh Blocks & How Are They Used in Real Estate

What Are Mesh Blocks & How Are They Used in Real Estate

What are Mesh Blocks?

As defined by Australian Bureau of Statistics (ABS), mesh blocks are the smallest geographical area of the Australian Statistical Geography Standard (ASGS) and ABS’s classification of Australia into a hierarchy of statistical areas.

Mesh Blocks are essentially a set of geographic boundaries designed to segment Australia into very small areas. These boundaries are used to apply a systematic grid over the entire country, dividing it into tiny sections called Mesh Blocks.

Each Mesh Block is a polygon that outlines a specific piece of land, which can range from a single block in a city to a vast, sparsely populated area in the countryside.

In 2021, the ABS reported 368,286 Mesh Blocks covering the whole of Australia without gaps or overlaps.

Mesh Blocks covering the whole of Australia. Source: ABS Maps

 

Mesh Block design

Mesh Blocks for the current ASGS Edition 3 are designed according to a standard set of design criteria first developed for ASGS 2011.

Most Mesh Blocks are designed to contain 30 to 60 dwellings, although some low dwelling count Mesh Blocks exist. They are permitted in order to account for other design criteria.

The reasons for the minimum dwelling count of Mesh Blocks is so they’re small enough to aggregate to a wide range of areas, allow comparisons between geographic regions but also prevent accidentally exposing confidential information of individuals or businesses.

 

Mesh Block changes

Mesh Blocks are updated (or redesigned) every 5 years to stay relevant.

Mesh Blocks for the current ASGS Edition 3 was redesigned to ensure it still meets the design criteria first developed for ASGS 2011 and reflects the growth and change in Australia’s population, economy and infrastructure.

Mesh Block Changes

Example of Mesh Block change along the border of Queensland and New South Wales. Source: Australian Bureau of Statistics

How are Mesh Blocks created?

Each Mesh Block is assigned a unique numerical code or identifier. This code is used to reference the Mesh Block in statistical databases and geographic information systems (GIS).

The format of the code can vary but often includes digits that signify hierarchical levels of geography.

In Australia, Mesh Block identifiers are 11-digit codes.

The 11-digit Mesh Block code comprises: State and Territory identifier (1 digit), and a Mesh Block identifier (10 digits).

How are Mesh Blocks used?

The ABS does not and cannot provide detailed segmentation data (Census data) that can be directly connected to individuals or businesses. Instead, they provide anonymised and aggregated data against geographic areas. Mesh Blocks are the smallest geographic area that the ABS provide statistics against, so it offers population and dwelling counts at a hyper-local level – this is particularly useful for Census analysis.

These geographic boundaries allow for the aggregation of data from individual Mesh Blocks into larger geographic units, such as suburbs, towns, cities, and regions. This hierarchical structuring makes it possible to analyse data at various levels, from very detailed local information to broader regional or national trends.

Most businesses, including proptechs, looking to augment their analysis with population segmentation data will adopt Mesh Blocks as their default level geographic unit to gain the highest level of accuracy. The popularity of Mesh Blocks mean many businesses will use it for geographic statistics regardless of whether or not the Census data is being leveraged.

What role do Mesh Blocks play in proptech?

Mesh Blocks play a vital role in Proptech, geospatial data, and the real estate industry in Australia. Some example uses include:

Granular geographical data

Since Mesh Blocks are the smallest geographical units, providing a granular level of detail in geographic data, its precision is valuable for analysing real estate trends at a hyper-local level.

Accurate small area statistics

Mesh Blocks are designed to fulfill the need for accurate small area statistics. In Proptech, having precise data at this level is instrumental for understanding localised property markets, demographics, and trends.

Spatial mapping and analysis

Geospatial data, including Mesh Blocks, facilitates spatial mapping and analysis. Proptech platforms can leverage this data to visualise and analyse property-related information, helping users make more informed decisions based on geographical insights.

Enhanced property valuation

Proptech applications can utilise Mesh Blocks to refine property valuation models. The data on dwellings and residents at this level allows for a more nuanced understanding of property values, considering localised factors.

Land use identification

Mesh blocks broadly identify land use, such as residential, commercial, industrial, parkland, and so forth. Land use information is valuable for proptechs involved in property development, urban planning, and investment strategies.

Targeted marketing and outreach

Proptech businesses can use Mesh Block data to tailor marketing and outreach strategies to specific geographical areas. Understanding the demographics and dwelling counts at this level allows for targeted and effective location-based campaigns.

Census-driven insights

The inclusion of Census data within Mesh Blocks, such as the count of usual residents and dwelling types, provides proptech platforms with up-to-date demographic information. This can aid market analysis, customer profiling, and investment strategies.

Integration with digital boundary files

The availability of Mesh Block boundaries in digital boundary files enhances their usability in Proptech applications. These files can be readily integrated into geospatial systems, making it easier for developers and analysts to work with this geographical data.

The foundational building blocks in real estate

Mesh Blocks are foundational building blocks for geospatial and proptech applications, providing granularity and accuracy for understanding local real estate markets, demographics and land use.

To aid proptechs, The Proptech Cloud offers its Geography – Boundaries & Insights dataset which includes all mesh blocks and their spatial areas for analysis and location-based visualisation of statistics.

The integration of this important information can enhance the precision and relevance of analyses within the proptech and real estate sectors. Read our following blog to learn how to incorporate Mesh Blocks into datasets.

How to Incorporate Mesh Blocks into Datasets

Incorporating mesh blocks into datasets involves several steps to ensure seamless integration and effective utilisation of geographical information. Here’s a guide on how to incorporate mesh blocks into datasets.

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