Please note, we recommend using the new Map Viewer in ArcGIS Online. There is an issue in Map Viewer Classic with the display of grid cell values. The clickable area of each cell is shifted to the northwest. This can result in neighbouring pixel values being displayed. The underlying data is correct, and the values display correctly in the new Map Viewer and in ArcGIS Pro. The Australian population grid 2022 is a modelled 1 km x 1 km grid representation of the estimated resident population (ERP) of Australia from 30 June 2022. The population grid is created by reaggregating estimated resident population data from Statistical Areas Level 1 (SA1) to a 1 km x 1 km grid across Australia based on point data representing residential address points. The value of each grid cell represents the estimated population density (number of people per square kilometre) within each 1 km x 1 km grid cell.

SA1 boundaries are defined by the Australian Statistical Geography Standard (ASGS) Edition 3 (2021) and the 1 km x 1 km grid is based on the National Nested Grid.

Data considerations Caution must be taken when using the population grid as it presents modelled data only; it is not an exact measure of population across Australia. Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Regional population, 2022 Additional data input: ABS Address Register Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3, National Nested Grid Further information: Regional population methodology Source: Australian Bureau of Statistics (ABS)

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Australia: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

The Australian population grid 2024 was created using 2024 Estimated Resident Population (ERP) by Statistical Area Level 1 2021 (SA1) data. This data was modelled to 1 kilometre square grid cells to represent the population density of Australia (people per square kilometre). This is modelled data and should be used and interpreted with caution.SA1s are defined by the Australian Statistical Geography Standard (ASGS) Edition 3 2021. The grid was constructed using the National Nested Grid Standard.Processing steps:A subset of the ABS Address Register (AR) was created to represent residential addresses as closely as possible. Indigenous Community Points (ICP) were included where no AR point existed. SA1 centroid points were included where no AR or ICP point existed within an SA1. All these layers were combined into a single point layer (Allpoints).The Allpoints layer was overlaid with the SA1 boundaries to give every point an SA1 code. Points without an SA1 code (outside all SA1 regions) were dropped.ERP by SA1 was averaged across all points within each SA1. Points were converted to raster using the National Nested Grid as template. Point population values which fell within each raster cell were summed.Data and geography referencesMain source data publication: Regional population, 2023–24 financial yearGeographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3Further information: Regional population methodologySource: Australian Bureau of Statistics (ABS)Contact the Australian Bureau of StatisticsEmail geography@abs.gov.au if you have any questions or feedback about this web service.Subscribe to get updates on ABS web services and geospatial products.Privacy at the Australian Bureau of StatisticsRead how the ABS manages personal information - ABS privacy policy.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

The exploration and management of mineral resources heavily rely on the availability of geoscientific data. However, the spatial distribution of these data can vary significantly across South Australia, creating challenges for comprehensive... The exploration and management of mineral resources heavily rely on the availability of geoscientific data. However, the spatial distribution of these data can vary significantly across South Australia, creating challenges for comprehensive geological analysis. Inspired by the Brazilian Geoscientific Knowledge Index (GKI) maps, this project aimed to develop a Data Density Index Map for South Australia. By visualising the distribution of critical geoscientific data, the map serves as a tool for identifying areas with high data concentrations, as well as regions that may benefit from additional data acquisition or exploration activities, ultimately facilitating decision-making and resource information management.

This is Version 1 of the Australian Soil Bulk Density - Whole Earth product of the Soil and Landscape Grid of Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method; Units: g/cm3; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Variance explained (cross-validation): 0.4%; Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being;

1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).

Version 1 of the Australian Soil Property Maps combines mapping from the:

1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps.

These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.

Download ServicesThe density of threatened and migratory species distributions grid is derived from the Department's Species of National Environmental Significance modelled distribution data. All threatened and most migratory species, for which Australia is part of the normal range, are modelled using three categories to indicate where their habitat is known, likely or may occur across Australia. The spatial input data was filtered using the following criteria:1. Distributions for EPBC Act (1999) listed species that are Matters of National Environmental Significance (vulnerable, endangered, critically endangered, extinct in the wild or migratory – where mapped within the Australian context)2. Contains ‘known’ and/or ‘likely to occur’ modelled habitat categories. Species with only ‘may occur’ habitat modelled are not included in the counts.3. High-level habitat filtering based on taxonomy, EPBC Act status and traits to include only terrestrial species or species that have some portion of their lifecycle modelled in terrestrial (freshwater aquatic, estuarine, shore-based or intertidal) environments. This includes all plants (including mangroves), migratory marine species that have mapped breeding sites on land, such as marine turtles or birds, and any animals that move between freshwater, estuarine and marine environments. In some cases, for migratory birds, full range distributions are not mapped and only the known and likely breeding habitat is mapped on land. Where a broader distribution including marine habitats has been mapped, the known and likely categories have been clipped to the Commonwealth of Australia (Geoscience Australia) GEODATA COAST 100K 2004. External territories and islands not present in the 100k coastline dataset are therefore not represented in this derived dataset.The number of overlaps for each distribution in the selected feature set were counted and gridded to a 0.01 decimal degree (~1km) cell size. Note projecting the data will alter the cell size. Given the indicative nature of the source data which includes models of a range of quality and currency, this output should be used as guide showing the relative density of the selected species modelled habitat.The initial raster stretch in ArcGIS Online Map Viewer may appear dark. To improve visibility, it is recommended to change Image Enhancement: Symbology Type to Unique Values and apply a suitable colour ramp.

The ABS Australian population grid 2023 was created using 2023 Estimated Residential Population (ERP) data at the SA1 level. The SA1 level ERP data was then modelled down to a 1km x 1km grid across geographic Australia using various point layers that represent population. The value of each grid cell represents the population density (number of people per square kilometre) in that 1km x 1km cell. This is modelled data and caution must be used in its interpretation, as the population has NOT been measured at the 1km cell level. SA1s are defined by the Australian Statistical Geography Standard (ASGS) Edition 3 (2021) and the grid used is based on the National Nested Grid Standard.Data and geography notes:Source data publication: Regional population, 2022-23Geographic boundary information: Statistical Areas Level 1 (SA1)(2021) - Australian Statistical Geography Standard (ASGS) Edition 3, National Nested Grid StandardAdditional data inputs: ABS Address Register, Indigenous Community Points (ICP)Further information: Regional population methodologySource: Australian Bureau of Statistics (ABS) www.abs.gov.auProcessing steps:A subset of the Address Register was created to represent residential addresses as closely as possible. Indigenous Community Points were included where no AR point existed. SA1 centroid points were included where no AR or ICP point existed within an SA1. All these layers were combined into a single point layer (Allpoints).The Allpoints layer was overlaid with the ASGS 2021 SA1 boundaries to give every point an SA1 code. Points without an SA1 code (outside all SA1 regions) were dropped.Estimated Resident Population by SA1 (ERP) was averaged across all points within each SA1.Points were converted to raster using the National Nested grid as template. Point population values falling within each raster cell were summed.

Population density metrics for 2011 Statistical Area Level 2 (SA2) within 2011 Greater Capital City Statistical Areas (GCCSA), including SA2 Population-weighted density (PWD) for 2011 and 2014, PWD change 2011-2014, and ERP population counts by density classes. Selected Density Classes were based on the Australian Population Density Grid published by the ABS, December 2014 (cat. no. 1270.0.55.007). Corresponding population metrics for 2011 GCCSAs. PWD using standardised 1km grid cells provides a more comparable measure of the density in larger regions. It does this by weighting the density using the proportion of population living at that density. In this way the density measure reflects the density at which people actually live. This removes the effect of large unpopulated areas that may be within the regions being compared. In this way comparisons between regions are more valid.

The map service can be viewed at http://soe.terria.io/#share=s-AgXEN0N0Q95icRW7M9JIC9IYBdE

Downloadable spatial data also available below.

Map prepared by the ABS and presented as Figure BLT3 in Built environment theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au.

This layer is displayed on the Residential density overlay map in City Plan version 7, and identifies designated residential densities. The layer is also available in Council’s City Plan interactive mapping tool. For further information on City Plan, please visit http://www.goldcoast.qld.gov.au/planning-and-building/city-plan-2015-19859.html

Spatial input data to parameterise the gully erosion module of the dSedNet model to simulate sediment generation and transport in the Western Port catchment for a 2018-19 study commissioned by Melbourne Water. Lineage: The 2003 gully map data were reprojected and spatially corrected. 'Active' gullies were determined through visual interpretation of aerial imagery from ESRI Base Layers (approx. 2013-2018) according to where the gully had sharply incised banks and/or presence of bare ground at base or edges. Some gullies were deleted where land use had changed and the gully was no longer visible, e.g. urban development, agriculture. New gullies were mapped where identified from recent aerial imagery. The workflow was executed within the ArcGIS (version 10.2) environment.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

This database presents an index of the intensity, frequency and density of cyclone occurrence in the Australian region. It has been derived from data held in CSIRO CAMRIS database and originally collected by the Bureau of Meteorology from 1958 - 1990. The cyclone_density code in the coverage represents: 1 Australia, 2-23 the nominal index of cyclone density/intensity, as per the Bureau of Meteorology cyclones database.

Format: shapefile.

Quality - Scope: Dataset. Absolute External Positional Accuracy Check: +/- one degree. Non Quantitative accuracy: The attribute called nominal_index holds values 0-23, which represent the intensity and density of cyclone occurrence. The attribute called cyclone_density provides a subjective definition of the density of cyclone occurrence:

Nominal_Index : Cyclone_Density

0 : No cyclone occurrence. 1 : Australian Continent. 2 : Low. 3-8 : Medium 9-16 : High. 17-23 : Very high.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent and oceans. Lineage: The database shows an index of cyclone intensity and frequency from 1958-1990. The map was created from raw data provided by the Bureau of Meterology: 1. Data points represented each 6 hourly location of every cyclone. 2. Modelled the density of points to create a contour map by counting points which fell within a certain radius of each point. Weighted by distance as 1 to all points within 25 km of a cyclone eye, and a linearly decaying weight (with distance) of between 1 and 0 to all points between 25 and 50km away. This assumed that cyclones significantly affect areas less than 25km from the eye, and have a decreasing effect with distance away from the eye. 3. Values on the contour map were multiplied by an index derived from intensity (barometric depression) at cyclone eye. 4. Reclassed intensity - density distribution using a linear scale.

CAMRIS data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. A summary follows of data processing by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources. 2. BASE Table was generated incorporating specific fields. 3. SPANS environment: creating a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America). 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level). 6. Rasterise maps specifying key parameters. 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid, and supplied to ERIN. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

This is Version 2 of the Australian Soil Bulk Density - Whole Earth product of the Soil and Landscape Grid of Australia.

It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546EE212B0048

The map gives a modelled estimate of the spatial distribution of Bulk Density in soils across Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Attribute Definition: Bulk Density of the whole soil (including coarse fragments) in mass per unit volume by a method equivalent to the core method; Units: g/cm3; Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: Cloud Optimised GeoTIFF; Lineage: An attempt was made to update digital soil mapping of whole soil bulk density for Australia. This was an update of first attempt by Viscarra Rossel et al. (2014). Based on model evaluations using a dataset not included in any modelling, the updated version (2nd Version) represents a demonstrable improvement on the 1st version.

Since the first version, more measured site data has been made available and retrievable via the Australian SoilDataFederator. In 2014 there were 3776 sites with measured whole soil bulk density. For the new update, 6116 sites had measured data. Because of usually strong empirical relationships between bulk density, soil texture and soil carbon, the use of pedotransfer functions (to predict bulk density from soil texture and soil carbon) was performed with the intention of increasing data density and spatial coverage of data that would ultimately improve digital soil mapping prediction skill. This added a further 15735 sites after building a spatial pedotransfer function using a dataset of 12308 cases (3939 sites with bulk density, soil carbon and soil texture data).

The basic steps of the work entailed.

Use soil data federator to get pertinent soils observation data

Develop spatial pedotransfer function prediction whole soil bulk density using soil carbon and texture data.

Compile measured and inferred whole soil bulk density data (86306 cases), then setting aside a dataset of 7500 cases for external model evaluation.

Predictive models using random forest algorithm with 78806 data cases fitted. To account for uncertainties in pedotransfer function inferred data, Monte Carlo simulations were performed from the pedotransfer function model. Simulation was repeated 100 times.

Predictive model uncertainties quantified using UNEEC approach (Uncertainty Estimation based on local errors and Clustering).

Quantification of model extension limits derived using hybrid method involving multivariate convex hull analysis and count of observations.

Digital soil maps with quantified uncertainties (5th and 95th prediction interval limits) and assessment of model extrapolation risk were produced at 90m resolution for the following depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm.

All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Code - https://github.com/AusSoilsDSM/SLGA Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html

Density (kilometres traversed) of vessels more than 24 metres long in the Australian exclusive economic zone. Source: National Environmental Science Programme Marine Biodiversity Hub, based on data from the Automatic Identification System managed by the Australian Maritime Safety Authority. See: https://www.nespmarine.edu.au/maps

Map prepared by the Department of Environment and Energy in order to produce Figure MAR16 in the Marine theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au

The map service can be viewed at http://soe.terria.io/#share=s-rKu2rDDnWrc6iKLnueWWJNWWv4u

Downloadable spatial data also available below.

The National ECM Key Habitat Distribution Map Series were derived from the NISB\r Habitat Map created by the University of Tasmania for a partnership between the\r Department of Climate Change and the National Land and Water Resources Audit. It\r supports the DCC/Audit partnership by providing a nationally consistent set of the\r available mapping data that show the distribution of habitats that occur between the\r approximate position of the highest astronomical tide mark (HAT) and the location of\r the outer limit of the photic benthic zone (approximately at the 50-70 m depth contour).\r This area is broadly equivalent to the “inner” and “mid-shelf” regions identified by\r Geoscience Australia. The resulting map data set forms a core component of the ECM\r National Habitat Map Series.\r The habitat classes include: coral reef, rock dominated habitat, sediment dominated\r habitat, mangroves, saltmarsh, seagrass, macroalgae and filter feeders (e.g. sponges), as\r defined in the NISB Habitat Classification Scheme. The scheme is designed to support\r the development of marine ‘ecoregions’ or bioregional subregions. Details of the\r scheme and the process of its development are available in National Intertidal/Subtidal\r Benthic (NISB) Habitat Classification Scheme Version 1 (Mount, Bricher and Newton,\r 2007).\r The 10 km and 50 km tiles distribution maps that form the National ECM Key Habitat\r Distribution Map Series were derived from the NISB Habitat Map in order to produce\r maps at resolutions that are easy to interpret at state and national extents. For each state,\r two layers were produced, one with 10 km and one with 50 km tiles. In each layer, new\r fields were created listing the presence, absence, unknown distribution or nonapplicability\r of the Habitats of Interest (HOI). The HOI are rock substrate (Class 1.2),\r unconsolidated substrates (Class 2.0), coral habitat (classes 1.1 and 1.2.2.3), sediment\r dominated habitats (Class 2.0.1), seagrass dominated habitats (Classes 1.2.2.4 and\r 2.0.2.1), mangrove dominated habitats (Class 2.0.2.2) and saltmarsh dominated habitats\r (Class 2.0.2.3).\r There are technical geographic and cartographic issues that arise when comparing\r mapped data sets of multiple scales, as is the case for this compiled and derived data\r set. The two derived information products were generated to provide a simplified\r spatial representation of the broad distribution patterns of each of the key habitats\r National ECM Habitat Map Series User Guide_v7.doc 30/04/2008 Page 32 of 156 \r across large areas. These derived products are designed to enable the visualisation of\r the habitat distributions at the regional and national extents. It is extremely important\r to note that they are definitely NOT able to be used to calculate areas of habitat\r types.

This is Version1 of the Australian Soil Organic Carbon product of the Soil and Landscape Grid of Australia at 30m resolution.

The map gives a modelled estimate of the spatial distribution of total organic carbon in soils across Australia.

It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/547523BB0801A

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 1 arc sec (~90 x 90 m pixels).

Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Attribute Definition: Mass fraction of carbon by weight in the < 2 mm soil material as determined by dry combustion at 900 Celcius Units: %; Period (temporal coverage; approximately): 1970-2021; Spatial resolution: 1 arc seconds (approx 30m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: Cloud Optimised GeoTIFF; Lineage: Data on total organic carbon (TOC) concentration (%) was extracted with the Soil Data Federator (https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederatorHelp.html) managed by CSIRO. The Soil Data Federator is a web API that compiles soil data from different institutions and government agencies throughout Australia. The laboratory methods for total organic carbon included in the study are 6A1, 6A1_UC, 6B2, 6B2b, 6B3, 6B3a. We selected TOC data from the period 1970-2020 to get a compromise between representativity of current TOC concentration and spatial coverage. The data was cleaned and processed to harmonize units, exclude duplicates and potentially wrong data entries (e.g. missing upper or lower horizon depths, extreme TOC values, unknown sampling date). Additional TOC measurements from the Biome of Australian Soil Environments (BASE) contextual data (Bisset et al., 2016) were also included in the analyses. TOC concentration for BASE samples was determined by the Walkley-Black method (method 6A1). Upper limits for TOC concentration by biome and land cover classes were set according to published literature, consistent datasets (Australian national Soil Carbon Research Program (SCaRP) and BASE, and data exploration to exclude unrealistic TOC values (e.g. maximum TOC = 30% in temperate forests, maximum TOC = 14% in temperate rainfed pasture). Since TOC concentration in Australian ecosystems has been underestimated by previous SOC maps, we did not set conservative TOC upper limits, knowing that machine learning model would likely underestimate high SOC values.

The equal-area quadratic spline function were fitted to the whole collection of pre-processed TOC data, and then values extracted for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm depth intervals, following GlobalSoilMap specifications (Arrouays et al., 2014}.

Covariates: We collected a set of 57 spatially exhaustive environmental covariates covering Australia and representing proxies for factors influencing SOC formation and spatial distribution: soil properties, climate, organisms/vegetation, relief and parent material/age. The covariates were reprojected to WGS84 (EPSG:4326) projection and cropped to the same spatial extent. All covariates were resampled using billinear interpolation or aggregated to conform with a spatial resolution with grid cell of 30 m x 30 m.

Mapping: The spatial distribution of soil TOC concentration is driven by the combined influence of climate, vegetation, relief and parent materials. We thus modelled TOC concentration as a function of environmental covariates representing biotic and abiotic control of TOC. The measurement of SOC and their corresponding value of environmental covariate at same measurement locations were used to fit the mapping model.

Mapping is made with Quantile regression forest, which is similar to the popular random forest algorithm for mapping. Instead of obtaining a single statistic, that is the mean prediction from the decision trees in the random forest, we report all the target values of the leaf node of the decision trees. With QRF, the prediction is thus not a single value but a cumulative distribution of the TOC prediction at each location, which can be used to compute empirical quantile estimates.

All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Code - https://github.com/AusSoilsDSM/SLGA Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html

It should be noted that this data is now somwhat dated!

Human population density is a surrogate indicator of the extent of human pressures on the surrounding landscapes.

Areas with high population density are associated with higher levels of stream pollution and water diversion through sewers and drains. City and urban environments are substantially changed from their pre-European condition but a changed condition is not of itself necessarily poor by societal standards. It is the impacts such as polluted run-off to waterways, air pollution, sewage disposal, household water use and predation of wildlife by pets that confer impacts on catchment condition. Human population centres have an impact well beyond the built environment.

The impact of major population centres is well expressed in the AWRC map, but is best displayed in the 500 map. The main areas of impact are the major coastal and capital cities and suburbs, including popular beachside tourist destinations. Elsewhere, the impact of population density appears to be confined to the Murray and other major river valleys.

The Australian Bureau of Statistics compiles population statistics by sampling statistical local areas (SLAas) through the national census. These data can be converted to a per catchment basis.

Interpretation of the indicator is largely unequivocal, although there are land-uses/activities (e.g. mining) where population density is not a good indicator of the degree of habitat decline. This indicator has not been validated relative to habitat decline. This indicator is easy to understand.

Data are available as:

• continental maps at 5km (0.05 deg) cell resolution for the ILZ;
• spatial averages over CRES defined catchments (CRES, 2000) in the ILZ;
• spatial averages over the AWRC river basins in the ILZ.

See further metadata for more detail.

Several evidential layers calculated from the national-scale gravity anomaly map of Australia (Geophysical Acquisition and Processing Section, 2020) are provided here. This directory includes GeoTIFF grids of Bouguer gravity, the horizontal gradient magnitude of the Bouguer gravity, the Bouguer gravity upward continued 30 km, and the horizontal gradient magnitude of the upward continued gravity, The directory also includes shapefiles of locations that trace the maxima of the horizontal gradient magnitude of the gravity and of the maxima of the horizontal gradient magnitude of the upward continued gravity. Otherwise known as “worms”, the points tracking the maxima mark the edges of shallow density sources (in the case of the Bouguer gravity) and deeper density sources (calculated from the upward continued gravity). The shapefile of worms also includes attribute fields related to the steepness of the gradient and to the trend or strike of the gradient. The reader is encouraged to read the metadata specific to each data layer for details related to the calculation and derivation of each gravity database. References Geophysical Acquisition and Processing Section, 2020, National Gravity Compilation 2019 includes airborne CSCBA image: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/144761.