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

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.

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.

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.

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.

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

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

This map is based on information from the 1966 census, and shows distribution and numbers of population in N.S.W. and the A.C.T. The map was printed by the Commonwealth Government Printer.

The scale is approx. 30 miles = 1 inch.


(SR Map No.52714). 1 map.

Note:
This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

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.

Download: Density of indicative threatened ecological community distributions (arcgis.com)Web service: species/ec_density (ImageServer)The density of indicative threatened ecological community distributions is derived from the Department's ecological communities of national environmental significance data. Threatened Ecological Communities (TEC) distributions contain 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 TECs that are Matters of National Environmental Significance (critically endangered or endangered).2. Contains ‘known’ and/or ‘likely to occur’ habitat categories. 3. Marine TECs are includedThe 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. The source distribution for each TEC is determined independently of others and is indicative in nature. As such, a count higher than one may indicate:• TECs have been mapped in the same habitat or • TECs are mapped adjacent within the same 1km grid cell or • TECs distributions have been mapped at different scales or levels of detail Given the indicative nature of the source data which includes data of a range of quality and currency, this output should be used as a guide to the location of TECs across the country.The selection of TEC distributions for inclusion in the count is based on the EPBC Act list of TECs and spatial data in the Department enterprise GIS as at the revision date in the metadata. Current EPBC Act listed TECs are described in the Species Profiles and Threats application (SPRAT: https://www.environment.gov.au/cgi-bin/sprat/public/sprat.pl).

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.

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.

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).

Gold Density Map - February 2005 Edition

This is Version 1 of the Australian Total Soil Nitrogen product of the Soil and Landscape Grid of Australia.

The map gives a modelled estimate of the spatial distribution of total nitrogen 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). An additional measure of model reliability is through assessment of model extrapolation risk. This measure provides users a spatial depiction where model estimates are made within the domain of the observed data or not.

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: Total soil nitrogen; Units: % (percentage of fine soil mass); Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 24; 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: A full description of the methods used to generate this product can be found at -https://aussoilsdsm.esoil.io/slga-version-2-products/total-soil-nitrogen

The first effort to derive national digital soil mapping of total soil nitrogen (expressed as a percentage of fine soil mass) is published and available on the CSIRO Data Access Portal among other places. The present work sort to update this mapping as part of ongoing efforts to expand and improve Australia’s national mapping and characterisation of its soil resources. Collectively these national soil mapping efforts constitute the Soil and Landscape Grid of Australia. The original work has been deemed as Version 1 (completed 2015), while the new work logically is Version 2 (completed 2023). This work has been made possible through support and funding from Australia’s National Collaborative Research Infrastructure Strategy (NCRIS) via the Terrestrial and Ecosystem Research Network

As with the first effort, digital soil mapping is the underpinning framework for the ultimate creation of soil maps in this instance.

As with the other more recent national digital soil mapping efforts, the SoilDataFederator (Searle 2020) has been instrumental in the dynamic collation of disparate soil observational datasets from across the country. These data have been sourced mainly from each State and Territory Government departments tasked with soil survey and collection. Plus there are other data contributions from Universities and to a lessor extent individual research groups. The SoilDataFederator also taps into the larger CSIRO developed Natsoil database (CSIRO 2020) which holds the data related to research projects and field stations that CSIRO has managed.

The improvement in digital soil mapping has come about via several mechanism.

1. A huge expansion of the available library of data corresponding to each of the main soil state factors has been made possible (Searle et al. 2022). This is through acquisition of new data sets and improvement of others compared with those used for version 1.

2. Adoption of machine learning to derive empirical relationships between target variable (total soil nitrogen content) and various data related to the state factors that help determine and control soil variability across landscapes, here the Australian continent and very nearshore islands. While the adoption of ML is not an entirely new advancement, the coupling of it with additional data, and integration of it within a psedo-3D predictive framework permit an improved ability to spatially and vertically characterise soils than Version 1 did.

3. Together with a more powerful and streamlined predictive modelling approach, the quantification of uncertainties draws on the use of the UNEEC (Uncertainty Estimation based on Empirical Errors and Clustering; Shrestha and Solomatine 2006) approach instead of bootstrapping approach so that prediction interval bounds are more custom to the variations in state factor information. Bootstrapping tends to create uniform prediction interval ranges, whereas UNEEC can distinguish areas of relatively lower and higher uncertainties based on differences in soil and landscape characteristics. Therefore, for Version 2, the uncertainties are more custom and tightly defined to the environment they are quantified in.

4. An approach to understand and characterise issues of model extrapolation has been developed. This seeks to highlight areas where there is high confidence that models are going be unreliable, because these areas are outside the range of the underpinning data used in modelling. This issue is addressed via combination of data geometric and distance-based techniques.

References:

CSIRO (2020): CSIRO National Soil Site Database. v9. CSIRO. Data Collection. https://doi.org/10.25919/c4br-0r30.

Searle, Ross (2020): TERN Soil Data Federator. v1. CSIRO. Software Collection. http://hdl.handle.net/102.100.100/480151?index=1

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. Data used to produce the …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data used to produce the predicted Total Dissolved Solids map for the Hutton Aquifer and equivalents in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). There are four layers in the Hutton Aquifer and equivalents Total Dissolved Solids map data A. Location of hydrochemistry samples (Point data, Shapefile) B. Predicted Concentration (Filled contours , Shapefile) C. Predicted Concentration Contours (Contours, Shapefile) D. Prediction Standard Error (Filled contours , Shapefile) The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map. The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data. Concentrations are Total Dissolved Solids mg/L. Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude. The Hutton Aquifer and equivalents Total Dissolved Solids map is one of four hydrochemistry maps for the Hutton Aquifer and equivalents and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81709. References: Hitchon, B. and Brulotte, M. (1994): Culling criteria for ‘standard’ formation water analyses; Applied Geochemistry, v. 9, p. 637–645 Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790] Dataset History SOURCE DATA: Data was obtained from a variety of sources, as listed below: Water quality data from the Queensland groundwater database, Department of Environment and Resource Management Geological Society of Queensland water chemistry database (1970s to 1980s). Muller, PJ, Dale, NM (1985) Storage System for Groundwater Data Held by the Geological Survey of Queensland. GSQ Record 1985/47. Queensland. Geoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p. Feitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government Geoscience Australia (2010) Hydrogeochemical collection. A compilation of quality controlled groundwater data taken from well completion reports from QLD and NSW. Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government BOUNDARIES: Data covers the extent of the Hutton Aquifer and equivalents as defined in Great Artesian Basin - Hutton Aquifer and equivalents - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81682). METHOD: Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries and clipped further where there was no data within 100 km. QAQC: Prior to data analysis all hydrochemistry data was assessed for reliability by Quality Assurance/Quality Control (QA/QC) procedures. A data audit and verification were performed using various quality checking procedures including identification and verification of outliers. The ionic balance of each analysis was checked, and where the ionic charge balance differed by greater than 10%, these analyses were deemed unacceptable and were not considered for future analysis. Data that passed the initial QA/QC procedures were checked against borehole construction and stratigraphic records to determine aquifer intercepts. Data were discarded in cases where there was no recorded location information or screen interval/depth information (to cross reference with borehole stratigraphy). Groundwater chemistry data was sourced from multiple studies, government databases, and companies. Many of the studies used sub-sets of the same data. All duplicates were removed before mapping and analysis. The differences between data sources had to be reconciled to ensure that maximum value of the data was retained and for errors in the transcription to be avoided. This precluded any automated processing system. Random checks were routinely made against the source data to ensure quality of the process. Some source data was in the form of thousands of consecutive rows and required python scripts or detailed table manipulations to correctly re-format the information and re-produce records with all the well data, its location and hydrochemical data for a particular sample date on one row in the collated Excel spreadsheet. Alkalinity measurements, in particular, were often reported differently between studies and even within the same database and required conversion to a common unit. All data before 1960 was discarded. The study uses a data collection compiled from petroleum well completion reports from QLD and NSW. This data underwent a thorough QC process to ensure that drilling mud contaminated samples were excluded, based on the procedure described by Hitchon, B. & Brulotte, M. (1994). Less than 5% of the samples compiled passed the QC procedure, but these provide invaluable insight into the chemistry of very deep parts of the aquifers (typically 1 - 2km deep). Where multiple samples have been taken at the same well, an average of the analyses was used in the kriging but outliers were removed. Outliers were identified by looking for large differences between predicted and measured samples. Excessively high values compared to predicted values and typical measurements at the same bore were discarded. Dataset Citation Geoscience Australia (2015) Hutton Aquifer and equivalents Total Dissolved Solids map: Data. Bioregional Assessment Source Dataset. Viewed 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/f5f16389-d97e-46b3-bd43-83255acf257d.