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;

Australia Population in Largest City data was reported at 5,315,600.000 Person in 2024. This records an increase from the previous number of 5,235,407.000 Person for 2023. Australia Population in Largest City data is updated yearly, averaging 3,709,165.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 5,315,600.000 Person in 2024 and a record low of 2,134,673.000 Person in 1960. Australia Population in Largest City 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 urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The 3 second (\~90m) Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) version 1.0 was derived from resampling the 1 arc second (\~30m) gridded DEM (ANZCW0703013355). The DEM represents ground surface topography, and excludes vegetation features. The dataset was derived from the 1 second Digital Surface Model (DSM; ANZCW0703013336) by automatically removing vegetation offsets identified using several vegetation maps and directly from the DSM. The 1 second product provides substantial improvements in the quality and consistency of the data relative to the original SRTM data, but is not free from artefacts. Man-made structures such as urban areas and power line towers have not been treated. The removal of vegetation effects has produced satisfactory results over most of the continent and areas with defects are identified in the quality assessment layers distributed with the data and described in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010). A full description of the methods is in progress (Read et al., in prep; Gallant et al., in prep). The 3 second DEM was produced for use by government and the public under Creative Commons attribution.

The 3 second DSM and smoothed DEM are also available (DSM; ANZCW0703014216,

DEM-S; ANZCW0703014217).

Dataset History

Source data

1. SRTM 1 second Version 2 data (Slater et al., 2006), supplied by Defence Imagery and Geospatial Organisation (DIGO) as 813 1 x 1 degree tiles. Data was produced by NASA from radar data collected by the Shuttle Radar Topographic Mission in February 2000.

2. GEODATA 9 second DEM Version 3 (Geoscience Australia, 2008) used to fill voids.

3. SRTM Water Body Data (SWBD) shapefile accompanying the SRTM data (Slater et al., 2006). This defines the coastline and larger inland waterbodies for the DEM and DSM.

4. Vegetation masks and water masks applied to the DEM to remove vegetation.

5. 1 second DEM resampled to 3 second DEM.

1 second DSM processing

The 1 second SRTM-derived Digital Surface Model (DSM) was derived from the 1 second Shuttle Radar Topographic Mission data by removing stripes, filling voids and reflattening water bodies. Further details are provided in the DSM metadata (ANZCW0703013336).

1 second DEM processing (vegetation offset removal)

Vegetation offsets were identified using Landsat-based mapping of woody vegetation. The height offsets were estimated around the edges of vegetation patches then interpolated to a continuous surface of vegetation height offset that was subtracted from the DSM to produce a bare-earth DEM. Further details are provided in the 1 second DSM metadata (ANZCW0703013355).

Void filling

Voids (areas without data) occur in the data due to low radar reflectance (typically open water or dry sandy soils) or topographic shadowing in high relief areas. Delta Surface Fill Method (Grohman et al., 2006) was adapted for this task, using GEODATA 9 second DEM as infill data source. The 9 second data was refined to 1 second resolution using ANUDEM 5.2 without drainage enforcement. Delta Surface Fill Method calculates height differences between SRTM and infill data to create a "delta" surface with voids where the SRTM has no values, then interpolates across voids. The void is then replaced by infill DEM adjusted by the interpolated delta surface, resulting in an exact match of heights at the edges of each void. Two changes to the Delta Surface Fill Method were made: interpolation of the delta surface was achieved with natural neighbour interpolation (Sibson, 1981; implemented in ArcGIS 9.3) rather than inverse distance weighted interpolation; and a mean plane inside larger voids was not used.

Water bodies

Water bodies defined from the SRTM Water Body Data as part of the DSM processing were set to the same elevations as in the DSM.

Edit rules for land surrounding water bodies

SRTM edit rules set all land adjacent to water at least 1m above water level to ensure containment of water (Slater et al., 2006). Following vegetation removal, void filling and water flattening, the heights of all grid cells adjacent to water was set to at least 1 cm above the water surface. The smaller offset (1cm rather than 1m) could be used because the cleaned digital surface model is in floating point format rather than integer format of the original SRTM.

Some small islands within water bodies are represented as voids within the SRTM due to edit rules. These voids are filled as part of void filling process, and their elevations set to a minimum of 1 cm above surrounding water surface across the entire void fill.

Overview of quality assessment

The quality of vegetation offset removal was manually assessed on a 1/8 ×1/8 degree grid. Issues with the vegetation removal were identified and recorded in ancillary data layers. The assessment was based on visible artefacts rather than comparison with reference data so relies on the detection of artefacts by edges.

The issues identified were:

\* vegetation offsets are still visible (not fully removed)

\* vegetation offset overestimated

\* linear vegetation offset not fully removed

\* incomplete removal of built infrastructure and other minor issues

DEM Ancillary data layers

The vegetation removal and assessment process produced two ancillary data layers:

\* A shapefile of 1/8 × 1/8 degree tiles indicating which tiles have been affected by vegetation removal and any issue noted with the vegetation offset removal

\* A difference surface showing the vegetation offset that has been removed; this shows the effect of vegetation on heights as observed by the SRTM radar

instrument and is related to vegetation height, density and structure.

The water and void fill masks for the 1 second DSM were also applied to the DEM. Further information is provided in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010).

Resampling to 3 seconds

The 1 second SRTM derived Digital Elevation Model (DEM) was resampled to 3 seconds of arc (90m) in ArcGIS software using aggregation tool. This tool determines a new cell value based on multiplying the cell resolution by a factor of the input (in this case three) and determines the mean value of input cells with the new extent of the cell (i.e. Mean value of the 3x3 input cells). The 3 second SRTM was converted to integer format for the national mosaic to make the file size more manageable. It does not affect the accuracy of the data at this resolution. Further information on the processing is provided in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010).

Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_aac46307-fce9-449d-e044-00144fdd4fa6/SRTM-derived+3+Second+Digital+Elevation+Models+Version+1.0

Dataset Citation

Geoscience Australia (2010) Geoscience Australia, 3 second SRTM Digital Elevation Model (DEM) v01. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/12e0731d-96dd-49cc-aa21-ebfd65a3f67a.

Updated September 2021 to include land use tiles. These tiles address issues with analysis of the large single dataset.# This vector dataset is a compilation of land use data for Western Australia, as at August 2018. It has been derived from various vector datasets with attribution relevant to land use in Western Australia. The date of mapping (2008 to 2018) and scale of mapping (1:5 000 to 1:250 000) vary, reflecting the source data, capture date and scale. The data shows a single dominant land use for a given area, based on the primary management objective of the land manager. As a seamless spatial dataset for Western Australia, it can be used to identify, map and analyse high level land use categories (such as nature conservation, dryland cropping and irrigated horticulture) and more specific land use categories (such as aquaculture and tree fruits) including some commodities (such as bananas). These categories can be extracted or combined with other spatial datasets to provide new insights and analysis concerning land use in Western Australia. Land use is classified according to the Australian Land Use and Management (ALUM) Classification version 8, a three-tiered hierarchical structure. There are five primary classes, identified in order of increasing levels of intervention or potential impact on the natural landscape. Water is included separately as a sixth primary class. Primary and secondary levels relate to the principal land use. Tertiary classes may include additional information on commodity groups, specific commodities, land management practices or vegetation information. The primary, secondary and tertiary codes work together to provide increasing levels of detail about the land use. Land may be subject to a number of concurrent land uses. For example, while the main management objective of a multiple-use production forest may be timber production, it may also provide conservation, recreation, grazing and water catchment land uses. In these cases, production forestry is commonly identified in the ALUM code as the prime land use. The operational scales of the mapping vary according to the intensity of land use activities and landscape context. Scales range from 1:5 000 and 1:25 000 for irrigated and peri-urban areas, to 1:100 000 for broadacre cropping regions and 1:250 000 for the semi-arid and arid pastoral zone. The scale of mapping generally reflects the intensity of land use. The vector geodatabase has been cut into 7 shapefile tiles. This reduces the file size and enables more analysis. The extent of tiles is shown below: • Swan Natural Resource Management region, • South West Natural Resource Management region, • South Coast Natural Resource Management region, • Northern Agricultural Region Natural Resource Management region, • Peel Harvey Natural Resource Management region, • Avon Natural Resource Management region, • Rangelands Natural Resource Management region

This dataset is the most current national compilation of catchment scale land use data for Australia (CLUM), as at September 2017. It replaces the Catchment Scale Land Use of Australia - Update May 2016 released in June 2016. It is a seamless raster dataset that combines land use data for all state and territory jurisdictions, compiled at a resolution of 50 metres by 50 metres. It has been compiled from vector land use datasets collected as part of state and territory mapping programs through the Australian Collaborative Land Use and Management Program (ACLUMP). Catchment scale land use data was produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field. The date of mapping (2003 to 2017) and scale of mapping (1:5 000 to 1:250 000) vary, reflecting the source data, capture date and scale. This information is provided in a supporting polygon dataset.

The following areas have been updated since the May 2016 version: Desert Channels and Mackay-Whitsundays natural resource management (NRM) regions in Queensland; the Adelaide and Mount Lofty Ranges NRM region in South Australia (extending into part of the Murray-Darling Basin NRM region); the state of New South Wales; the state of Victoria; the state of Tasmania; the state of Western Australia; and the Northern Territory. The capital city of Adelaide was also updated using 2016 mesh block information from the Australian Bureau of Statistics. This equates to over 585 million hectares or 76% of Australia, the largest update of catchment scale land use mapping to date.

Users should update any references or links to previous CLUM datasets in their databases. Users should also note that it is not possible to calculate land use change statistics between annual CLUM national compilations as not all regions are updated each year; land use mapping methodologies, precision, accuracy and source data (in particular satellite imagery) have improved over the years; and the land use classification has changed over time. In particular, the major differences between this September 2017 version and the May 2016 version are:

• The September 2017 version has been mapped to version 8 of the Australian Land Use and Management (ALUM) Classification (released in October 2016)
• Improvements to the Western Australia mapping including improved mapping of native forests, conservation areas, topographic features, horticulture and intensive animal production
• Changes to the mapping of native and modified pastures in New South Wales
• Changes to the mapping of dairies in Tasmania so that only the dairy infrastructure is mapped as dairies and the dairy pastures are mapped as modified pastures.

It is only possible to calculate change when earlier land use datasets have been revised and corrected to ensure that changes detected are real change and not an artefact of the mapping process. The Queensland Land Use Mapping Program (QLUMP) have done this on an NRM regions basis for Queensland and can be accessed at:

• Data: Search the Queensland Spatial Catalogue (http://dds.information.qld.gov.au/dds/) for "Land Use Mapping"
• Reports: https://www.qld.gov.au/environment/land/vegetation/mapping/qlump-reports.

The CLUM data shows a single dominant land use for a given area, based on the primary management objective of the land manager (as identified by state and territory agencies). As a seamless spatial dataset for Australia, it can be used to identify, map and analyse high level land use categories (such as irrigated horticulture and dryland cropping) and more specific land use categories such as grapes, cotton, cereals, sugar and tree fruits. These categories can be extracted or combined with other spatial datasets to provide new insights and analysis concerning land use in Australia. A complementary dataset Catchment Scale Land Use of Australia - Commodities - September 2017 provides commodity level mapping as a vector dataset.

Land use is classified according to the Australian Land Use and Management (ALUM) Classification version 8, a three-tiered hierarchical structure. There are five primary classes, identified in order of increasing levels of intervention or potential impact on the natural landscape. Water is included separately as a sixth primary class. Primary and secondary levels relate to the principal land use. Tertiary classes may include additional information on commodity groups, specific commodities, land management practices or vegetation information. The primary, secondary and tertiary codes work together to provide increasing levels of detail about the land use. Land may be subject to a number of concurrent land uses. For example, while the main management objective of a multiple-use production forest may be timber production, it may also provide conservation, recreation, grazing and water catchment land uses. In these cases, production forestry is commonly identified in the ALUM code as the prime land use.

The operational scales of catchment scale mapping vary according to the intensity of land use activities and landscape context. Scales range from 1:5 000 and 1:25 000 for irrigated and peri-urban areas, to 1:100 000 for broadacre cropping regions and 1:250 000 for the semi-arid and arid pastoral zone. The date of mapping generally reflects the intensity of land use. The most current mapping occurs in intensive agricultural areas; older mapping generally occurs in the semi-arid and pastoral zones. The primary classes of land use in the ALUM Classification are:

1. Conservation and natural environments - land used primarily for conservation purposes, based on maintaining the essentially natural ecosystems present
2. Production from relatively natural environments - land used mainly for primary production with limited change to the native vegetation
3. Production from dryland agriculture and plantations - land used mainly for primary production based on dryland farming systems
4. Production from irrigated agriculture and plantations - land used mostly for primary production based on irrigated farming
5. Intensive uses - land subject to extensive modification, generally in association with closer residential settlement, commercial or industrial uses
6. Water - water features (water is regarded as an essential aspect of the classification, even though it is primarily a land cover type, not a land use)

The Catchment Scale Land Use of Australia - Update September 2017 is a product of the Australian Collaborative Land Use and Management Program (ACLUMP). ACLUMP, of which ABARES is a partner, promotes the development of consistent information on land use and land management practices. This consortium of Australian, state and territory government partners is critical to providing nationally consistent land use mapping at both catchment and national scale, underpinned by common technical standards including an agreed national land use classification. ACLUMP provides a national land use data directory and the maintenance of land use datasets on Australian and state government data repositories. More information on ACLUMP available at www.abares.gov.au/landuse

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.

A representation of South Australia’s surface water linear drainage network. Includes significant artificial drainage structures such as urban stormwater channels and the South East drainage scheme. This dataset has been created by merging a wide range of state and federal spatial datasets in order to extract the most accurate spatial and textual data for each region of the state. The dataset utilises a consistent national data schema (AHGF) but the geometry varies in accuracy, capture scale and completeness. Ideally viewed in conjunction with the Waterbodies dataset.

The Australian Government’s Peri-Urban Mobile Program will to improve mobile coverage in bushfire prone areas on the peri-urban fringe of our major cities. PUMP will deliver funding to improve mobile connectivity in bushfire priority areas along the edges of Australia’s major cities. The Department of Infrastructure, Transport, Regional Development and Communications’ website www.communications.gov.au/what-we-do/phone/mobile-services-and-coverage/peri-urban-mobile-program.