Australia Population in Largest City: as % of Urban Population data was reported at 22.768 % in 2024. This records an increase from the previous number of 22.673 % for 2023. Australia Population in Largest City: as % of Urban Population data is updated yearly, averaging 24.964 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 27.701 % in 1971 and a record low of 22.181 % in 2013. Australia Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

These files provide more detailed outputs from BITRE's 'Freight vehicle congestion in Australia’s five major cities - 2019' publication (see: https://www.bitre.gov.au/publications/2021/freight-vehicle-congestion-australias-five-major-cities-2019), which reported freight vehicle telematics based measures of traffic congestion for freight vehicles on 53 selected routes across Australia’s five mainland state capital cities—Sydney, Melbourne, Brisbane, Adelaide and Perth. The selected routes comprise the major motorways, highways and arterial roads within each city that service both passenger and freight vehicles.

Disclaimers: https://www.infrastructure.gov.au/disclaimers.

Content

In this dataset you can find hundreds of thousands of the largest cities in the world and info about their latitude, longitude, timezone, location, etc.

Acknowledgements

This data comes from https://data.world/fiftin/cities/workspace/file?filename=RU.txt.

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous

Geoscape Administrative Boundaries is Australia’s most comprehensive national collection of boundaries, including government, statistical and electoral boundaries. It is built and maintained by Geoscape Australia using authoritative government data. Further information about contributors to Administrative Boundaries is available here.

This dataset comprises seven Geoscape products:

• Localities
• Local Government Areas (LGAs)
• Wards
• Australian Bureau of Statistics (ABS) Boundaries
• Electoral Boundaries
• State Boundaries and
• Town Points

Updated versions of Administrative Boundaries are published on a quarterly basis.

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Notable changes in the May 2025 release

• Victorian Wards have seen almost half of the dataset change now reflecting the boundaries from the 2024 subdivision review. https://www.vec.vic.gov.au/electoral-boundaries/council-reviews/ subdivision-reviews.

  • There have been spatial changes (area) greater than 1 km2 to 66 wards in Victoria.

• One new locality ‘Kenwick Island’ has been added to the local Government area ‘Mackay Regional’ in Queensland.

  • There have been spatial changes(area) greater than 1 km2 to the local government areas 'Burke Shire' and 'Mount Isa City' in Queensland.

• There have been spatial changes(area) greater than 1 km2 to the localities ‘Nicholson’, ‘Lawn Hill’ and ‘Coral Sea’ in Queensland and ‘Calguna’, ‘Israelite Bay’ and ‘Balladonia’ in Western Australia.

• An update to the NT Commonwealth Electoral Boundaries has been applied to reflect the redistribution of the boundaries gazetted on 4 March 2025.

• Geoscape has become aware that the DATE_CREATED and DATE_RETIRED attributes in the commonwealth_electoral_polygon MapInfo TAB tables were incorrectly ordered and did not match the product data model. These attributes have been re-ordered to match the data model for the May 2025 release.

IMPORTANT NOTE: correction of issues with the 22 November 2022 release

• On 28 November 2022, the Administrative Boundaries dataset originally released on 22 November 2022 was amended and re-uploaded after Geoscape identified some issues with the original data for 'Electoral Boundaries'.
• As a result of the error, some shapefiles were published in 3D rather than 2D, which may affect some users when importing data into GIS applications.
• The error affected the Electoral Boundaries dataset, specifically the Commonwealth boundary data for Victoria and Western Australia, including 'All States'.
• Only the ESRI Shapefile formats were affected (both GDA94 and GDA2020). The MapInfo TAB format was not affected.
• Because the datasets are zipped into a single file, once the error was fixed by Geoscape all of Administrative Boundaries shapefiles had to be re-uploaded, rather than only the affected files.
• If you downloaded either of the two Administrative Boundary ESRI Shapefiles between 22 November and 28 November 2022 and plan to use the Electoral Boundary component, you are advised to download the revised version dated 28 November 2022. Apologies for any inconvenience.

Further information on Administrative Boundaries, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on Administrative Boundaries, including software solutions, consultancy and support.

Note: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

The Australian Government has negotiated the release of Administrative Boundaries to the whole economy under an open CCBY 4.0 licence.

Users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International license (CC BY 4.0).

Preferred attribution for Adapted Material:

Incorporates or developed using Administrative Boundaries © Geoscape Australia licensed by the Commonwealth of Australia under Creative Commons Attribution 4.0 International licence (CC BY 4.0).

What to Expect When You Download Administrative Boundaries

Administrative Boundaries is large dataset (around 1.5GB unpacked), made up of seven themes each containing multiple layers.

Users are advised to read the technical documentation including the product change notices and the individual product descriptions before downloading and using the product.

Please note this dataset is the most recent version of the Administrative Boundaries (AB). For previous versions of the AB please go to this url: https://data.gov.au/dataset/ds-dga-b4ad5702-ea2b-4f04-833c-d0229bfd689e/details?q=previous

License Information

This dataset is the Greater Capital City Statistical Area (GCCSA) boundaries as defined by the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2011. For the original data and more information, refer to the Australian Bureau of Statistics' Issue. The ABS encourages the use of the ASGS by other organisations to improve the comparability and usefulness of statistics generally, and in analysis and visualisation of statistical and other data. The Australian Statistical Geography Standard (ASGS) brings together in one framework all of the regions which the ABS and many others organisations use to collect, release and analyse geographically classified statistics. The ASGS ensures that these statistics are comparable and geospatially integrated and provides users with an coherent set of standard regions so that they can access, visualise, analyse and understand statistics.

The Global Metropolis Weather Dataset offers comprehensive weather data for major metropolitan areas worldwide, encompassing prominent global cities recognized for their economic, cultural, and political significance. This dataset provides meteorological observations from cities such as New York, London, Paris, Tokyo, Hong Kong, Dubai, Rome, and Sydney, facilitating comparative analysis of weather patterns across diverse urban landscapes. With essential weather metrics including temperature, humidity, wind speed, cloud cover, pressure, UV index, and visibility, this dataset serves as a valuable resource for research in urban climate studies, resilience planning, and climate adaptation strategies. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18544731%2F832f0684cfca2298e9886972750a2fa2%2Fworld-with-banner-clipart-1.jpg?generation=1712345913580655&alt=media" alt="">

This dataset presents the footprint of economic activity for Australia's capital cities. The data is aggregated to Statistical Area Level 2 (SA2) from the 2011 Australian Statistical Geography Standard (ASGS) and spans the year of 2014. This data has been created by the Grattan Institute for the Mapping Australia's Economy: Cities as Engines of Prosperity Report, Kelly, J-F., Donegan, P., Chisholm, C., & Oberklaid, M. published 20 July 2014. The report maps the Australian economy by the location of economic activity, defined as the dollar value of goods and services produced by workers within a particular area. It finds that economic activity is concentrated most heavily in the central business districts (CBDs) and inner areas of large cities. For more information including the data creation methodology, please refer to the Mapping Australia's Economy: Cities as Engines of Prosperity Report. Please note: AURIN has spatially enabled the original data using the ASGS 2011 SA2 Digital Boundaries.

Supplementary Information Files for Socio-economic groups moving apart: An analysis of recent trends in residential segregation in Australia's main capital citiesWe study changes in the spatial distribution and segregation of socio-economic groups in Australia using a new data set with harmonised census data for 1991 and 2011. We find a general increase in residential segregation by education and occupation groups across the major capital cities in Australia. Importantly, these trends cannot be explained in general by changes in the demographic structure of groups and areas but rather by the rise in the over and underrepresentation of groups across areas. In particular, our analysis reveals clear diverging trends in the spatial configuration of high and low socio-economic groups as measured by their occupation and education. Whereas high-skilled groups became more concentrated in the inner parts of cities, the low-educated and those working in low-status occupations became increasingly overrepresented in outer areas. This pattern is observed in all five major capital cities, but it is especially marked in Sydney, Melbourne and Brisbane.

Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Current building datasets suffer from incomplete coverage of building multi-attributes. This paper presents the first national-scale Multi-Attribute Building dataset (CMAB) with artificial intelligence, covering 3,667 spatial cities, 31 million buildings, and 23.6 billion m² of rooftops with an F1-Score of 89.93% in OCRNet-based extraction, totaling 363 billion m³ of building stock. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating morphology, location, and function features. Using multi-source data, including billions of remote sensing images and 60 million street view images (SVIs), we generated rooftop, height, structure, function, style, age, and quality attributes for each building with machine learning and large multimodal models. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation, mostly above 80%. Our dataset and results are crucial for global SDGs and urban planning.Data records: A building dataset with a total rooftop area of 23.6 billion square meters in 3,667 natural cities in China, including the attribute of building rooftop, height, structure, function, age, style and quality, as well as the code files used to calculate these data. The deep learning models used are OCRNet, XGBoost, fine-tuned CLIP and Yolo-v8.Supplementary note: The architectural structure, style, and quality are affected by the temporal and spatial distribution of street views in China. Regarding the recognition of building colors, we found that the existing CLIP series model can not accurately judge the composition and proportion of building colors, and then it will be accurately calculated and supplemented by semantic segmentation and image processing. Please contact zhangyec23@mails.tsinghua.edu.cn or ylong@tsinghua.edu.cn if you have any technical problems.Reference Format: Zhang, Y., Zhao, H. & Long, Y. CMAB: A Multi-Attribute Building Dataset of China. Sci Data 12, 430 (2025). https://doi.org/10.1038/s41597-025-04730-5.

This dataset contains observed bike counts from sites across the city. These counts are often referred to "Super Tuesday" and is Australia’s biggest annual commuter bike count. The count also contains information for gender, and movement flow of people on bikes. There is a large number of fields captured for this dataset, which has been compiled into an attached metadata document.

This dataset contains observed bike counts from sites across the city known as "Super Sunday". This is Australia’s biggest survey of recreational travel. Held annually in mid-November, the count looks at how runners, walkers, bike riders and other recreational users move around

There is a large number of fields captured for this dataset, which has been compiled into an attached metadata document.

With 8.7 Million Businesses in Australia , Techsalerator has access to the highest B2B count of Data/Business Data in the country.

Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

At Techsalerator we cover all regions and cities in Australia. A few listed:

Regions : New South Wales (Sydney) Queensland (Brisbane) South Australia (Adelaide) Tasmania (Hobart) Victoria (Melbourne) Western Australia (Perth)

Cities : name Sydney Melbourne Brisbane Perth Adelaide Gold Coast Canberra Newcastle Wollongong Logan City Geelong Hobart Townsville Cairns Toowoomba Darwin Rockingham Launceston Bendigo Ballarat Mandurah Mackay Bundaberg Bunbury Maitland Armadale Rockhampton Adelaide Hills South Brisbane Hervey Bay Reservoir Tamworth Wagga Wagga Albury Berwick Port Macquarie Queanbeyan Orange Blacktown Shepparton Caloundra Hoppers Crossing Werribee Melton Castle Hill Saint Albans Nowra Frankston Frankston East Rowville Warrnambool Baulkham Hills Albany Mount Waverley St Albans Auburn Bathurst Pakenham South Point Cook Dubbo Epping Wodonga Kalgoorlie Bankstown Mill Park Gladstone Kwinana Lismore Mildura Preston Sunbury Hurstville Narre Warren South Noble Park Southport Kellyville Port Stephens Banora Point Doncaster East Croydon Geraldton Maroubra Coffs Harbour Mosman Richmond Narre Warren Randwick Strathfield Bundoora Alice Springs Quakers Hill Endeavour Hills Palmerston Coburg Dandenong Fremantle Ferntree Gully Campsie Kew Hampton Park Canning Vale Glen Iris Mount Gambier Marrickville Northcote Granville Mount Isa Keysborough Armidale Morphett Vale Dianella Forest Lake Mornington Thornlie Ashfield Traralgon Dandenong North Busselton Cabramatta Greystanes Tarneit Maryborough Caboolture Kirwan Langwarrin Carlingford Liverpool Caringbah Brighton Glenferrie Hawthorn Hawthorn South Goulburn Boronia Woodridge Booval Thomastown Cheltenham Punchbowl Prospect Greensborough Gawler Burnie Balwyn North Lalor Brunswick Hornsby St Clair Springvale Wheelers Hill Craigieburn Whyalla Glenroy Camberwell Malvern East Murray Bridge Echuca Devonport Roxburgh Park Glenmore Park Epping Ballajura Essendon Cherrybrook Altona Meadows Cranbourne Katoomba Surfers Paradise Parramatta Broken Hill Doncaster Eltham Fairfield Morayfield Engadine Eastwood Saint Kilda Highton Mulgrave Forster Wantirna South Dee Why Thornbury Wahroonga Frankston South Wyndham Vale Gosnells Mount Eliza Willetton Carrum Downs North Ryde Mount Martha Wangaratta Sunnybank Hills Cronulla Sunshine West Taree Earlwood Sunnybank South Grafton Cessnock Hillside Westmead Carnegie Nerang Narangba Deer Park Taylors Lakes Deception Bay Umina Seaford Burwood Yagoona West Pennant Hills Paralowie Lilydale Moe Clayton Lara Griffith Bracken Ridge Eight Mile Plains Parafield Gardens Prestons Buderim Brighton East Carindale Port Hedland Duncraig Pascoe Vale Rochedale South Coorparoo Meadow Heights Mitcham Casula Bossley Park Cranbourne North Caulfield North Lakemba Kingston Grovedale Horsham Bentleigh Ballina Kingsford Lidcombe Carlton Wantirna Manly Ingleburn Burleigh Waters Elwood Cleveland Victoria Point Yarraville Singleton Bongaree Raymond Terrace Mount Druitt Bacchus Marsh Newtown Moonee Ponds Palm Beach Ascot Vale Morwell Port Melbourne Yeppoon Keilor East Port Augusta Port Pirie Footscray Williamstown Sale Coogee Templestowe Lower Brunswick West Hawthorn East Surrey Hills Port Lincoln Doonside Concord Toongabbie Dulwich Hill Balwyn Miranda Toorak Beaumaris Port Kennedy Broome Fawkner Scarborough St Kilda East Inala Warwick Rosebud Hampton Como Ashwood Chadstone Marsfield Kiama Mayfield Leichhardt Springvale South Goonellabah Port Augusta West Geelong West Lavington Doreen Newport Greenvale Blackburn Burwood West Ryde Kingswood Park Penrith Varsity Lakes Donvale Muswellbrook Taylors Hill Bateau Bay North Melbourne Wynnum West Karratha Charlestown Wynnum Penshurst Mentone Paddington Caringbah South Clayton South Happy Valley Diamond Creek Redfern North Fitzroy Warnbro Vermont South Sandy Bay East Maitland South Perth Lithgow Bayswater Manly West Parkdale Caulfield South Gympie Bairnsdale Bowen Nunawading Samford Valley Wanneroo Mount Gravatt East Altona North Caroline Springs Bulleen Kensington Gladesville Menai Bondi Beach Well...

This dataset is the Statistical Area Level 1 (SA1) boundaries as defined by the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2011. For the original data and more information, refer to the Australian Bureau of Statistics' Issue. The ABS encourages the use of the ASGS by other organisations to improve the comparability and usefulness of statistics generally, and in analysis and visualisation of statistical and other data. The Australian Statistical Geography Standard (ASGS) brings together in one framework all of the regions which the ABS and many others organisations use to collect, release and analyse geographically classified statistics. The ASGS ensures that these statistics are comparable and geospatially integrated and provides users with an coherent set of standard regions so that they can access, visualise, analyse and understand statistics.

The world’s largest noise complaint dataset with over 160K reports including labeled noise sources. Ideal for AI training in acoustic event detection and urban noise analysis. Available via CSV, S3, and API (coming soon). GDPR-compliant.

The final Australian National Liveability Study 2018 datasets comprise a suite of policy relevant spatial indicators of local neighbourhood liveability and amenity access estimated for residential address points across Australia's 21 largest cities, and summarised at range of larger area scales (Mesh Block, Statistical Areas 1-4, Suburb, LGA, and overall city summaries). The indicators and measures included encompass topics including community and health services, employment, food, housing, public open space, transportation, walkability and overall liveability. The datasets were produced through analysis of built environment and social data from multiple sources including OpenStreetMap the Australian Bureau of Statistics, and public transport agency GTFS feed data. These are provided in CSV format under an Open Data Commons Open Database licence. The 2018 Australian National Liveability data will be of interest to planners, population health and urban researchers with an interest in the spatial distribution of built environment exposures and outcomes for data linkage, modelling and mapping purposes. Area level summaries for the data were used to create the indicators for the Australian Urban Observatory at its launch in 2020.

A detailed description of the datasets and the study has been published in Nature Scientific Data, and notes and code illustrating usage of the data are located on GitHub.

The spatial data were developed by the Healthy Liveable Cities Lab, Centre for Urban Research with funding support provided from the Australian Prevention Partnership Centre #9100003, NESP Clean Air and Urban Landscapes Hub, NHMRC Centre of Research Excellence in Healthy, Liveable Communities #1061404 and an NHMRC Senior Principal Research Fellowship GNT1107672; with interactive spatial indicator maps accessible via the Australian Urban Observatory. Any publications utilising the data are not necessarily the view of or endorsed by RMIT University or the Centre of Urban Research. RMIT excludes all liability for any reliance on the data.

This dataset is the Statistical Area Level 1 (SA1) boundaries as defined by the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016. For the original data and more information, refer to the Australian Bureau of Statistics' Issue. The ABS encourages the use of the ASGS by other organisations to improve the comparability and usefulness of statistics generally, and in analysis and visualisation of statistical and other data. The Australian Statistical Geography Standard (ASGS) brings together in one framework all of the regions which the ABS and many others organisations use to collect, release and analyse geographically classified statistics. The ASGS ensures that these statistics are comparable and geospatially integrated and provides users with an coherent set of standard regions so that they can access, visualise, analyse and understand statistics.

The world’s largest noise complaint dataset including labeled noise sources. Ideal for AI training in acoustic event detection and urban noise analysis. Available via CSV, S3, and API (coming soon). GDPR-compliant.

This dataset contains the Aussie Backyard Bird Count 2020, 2021, 2022, 2023 and 2024 Results for the City of Townsville provided by BirdLife Australia. The Aussie Backyard Bird Count is one of the …Show full descriptionThis dataset contains the Aussie Backyard Bird Count 2020, 2021, 2022, 2023 and 2024 Results for the City of Townsville provided by BirdLife Australia. The Aussie Backyard Bird Count is one of the largest citizen science projects in Australia. Data has been aggregated to suburb and includes the count of individual bird species in each suburb.

This dataset is the Mesh Block (MB) boundaries as defined by the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2011 for the Northern Territory (NT). For the original data and more information, refer to the Australian Bureau of Statistics' Issue. The ABS encourages the use of the ASGS by other organisations to improve the comparability and usefulness of statistics generally, and in analysis and visualisation of statistical and other data. The Australian Statistical Geography Standard (ASGS) brings together in one framework all of the regions which the ABS and many others organisations use to collect, release and analyse geographically classified statistics. The ASGS ensures that these statistics are comparable and geospatially integrated and provides users with an coherent set of standard regions so that they can access, visualise, analyse and understand statistics.