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

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 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.

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.

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 Australian Population Grid 2022 was created using estimated residential population (ERP) data for Statistical Areas Level 1 (SA1). 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, 2022Geographic boundary information: Statistical Areas Level 1 (SA1) - 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 step:1) A subset of the Address Register that represented residential addresses as closely as possible was made. ICP 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). 2) 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. 3) Estimated Resident Population by SA1 (ERP) was averaged across all points within each SA1. 4) Points were converted to raster, using the National Nested grid as template. Point population values falling within each raster cell were summed.

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.

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).

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 …Show full descriptionDensity (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.

Gold Density Map - February 2005 Edition

These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).

Map showing the density distribution of feral camels across the estimated 2008 range of feral camels in Australia. Data has been derived from kriging interpolation of known aerial survey densities …Show full descriptionMap showing the density distribution of feral camels across the estimated 2008 range of feral camels in Australia. Data has been derived from kriging interpolation of known aerial survey densities extrapolated forward to 2008. Data supplied by the Department of the Environment and Natural Resources © Northern Territory of Australia. This material is licensed under a Creative Commons Attribution-NonCommercial–ShareAlike 4.0 International license. Further information regarding the data see: Saalfeld, W.K. and Edwards, G.P. (2010). Distribution and abundance of the feral camel (Camelus dromedarius) in Australia (http://www.publish.csiro.au/RJ/RJ09058). Please contact the data provider if you wish to use this data for commercial purposes. Map prepared by the Department of Environment and Energy in order to produce Figure LAN32 in the Land theme of Australia State of the Environment 2016 available at http://www.soe.environment.gov.au The map service can be viewed at: http://soe.terria.io/#share=s-1ldrvHb1eeL5u6Y46NWGJJ2D8ev Downloadable spatial data also available below.

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).

This is Version 1 of the Australian Soil Cation Exchange Capacity product of the Soil and Landscape Grid of Australia.

The map gives a modelled estimate of the spatial distribution of cation exchange capacity 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: Cation Exchange Capacity Units: meq/100g; Period (temporal coverage; approximately): 1970-2022; 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: Version 1 Soil and landscape Grid of Australia (Grundy et al. 2015), produced digital mapping of Effective Cation Exchange Capacity (ECEC) which is defined as the total amount of exchangeable bases which are mostly sodium, potassium, calcium and magnesium (collectively termed as bases) in non-acidic soils and bases plus aluminium and hydrogen in acidic soils.

This product, Soil and Landscape Grid National Soil Attribute Maps - Cation Exchange Capacity, described here entails the use of those data pertaining to those data with CEC measurement.

This dataset is made of soil measurements using the following methods as described in Rayment and Lyons (2010): method not recorded (1096), 15A1 (161), 15A2 (365), 15B1 (553), 15B2 (34), 15C1 (3229), 15D1 (265), 15E1 (28), 15K1 (376). The distribution of these sites, colour-coded by each method is shown on Figure 1.

To complement the CEC measurement data, we used data cases (12474) where there is a measured CEC together with soil texture and soil organic carbon co-located measurements. A machine learning pedotransfer function model with these data, together with spatial covariates was used to extend the geographic spread and density of CEC data in order to potentially improve digital soil mapping efforts.

Extensive data processing was involved post data extraction from the SoilDataFederator

Spatial modelling is underpinned by the Cubist (Quinlan 1993) machine learning algorithm.

The spatial modelling integrates both measurement CEC data and CEC data derived by pedotransfer function. The derived CEC have an associated uncertainty and this is incorporated into the spatial model via a simple monte-carlo approach.

The spatial model included a soil depth interval term in order to exploit covariance relationships of soil information within a soil profile. Thus modelling is considered a full soil profile predictive modelling framework.

Prediction uncertainties in this work were done using an approach based on local-errors and clustering (UNEEC) method developed by Shrestha and Solomatine (2006).

Soil maps of predictions and associated uncertainties (expressed as lower and upper prediction limits for 90% confidence) were generated for the following depth intervals: 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/COGs

Chart and table of population level and growth rate for the Melbourne, Australia metro area from 1950 to 2025.

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 Border Rivers Gwydir and Namoi Regional Vegetation Map is a subset of the statewide vegetation mapping and classification program undertaken by the NSW Office of Environment and Heritage (OEH Regional Scale State Vegetation Map) and covers the two former Catchment Management Authority Regions.

The primary thematic data layer in this dataset is a map of regional scale Plant Community Types (PCT's). The map was developed from a process using vegetation surveys, remote sensing derivations, visual interpretation and spatial distribution models.

The full dataset comprises the following data layers as delivered in an ArcGIS 9.3 File Geo-database:

PLANT COMMUNITY TYPE: The primary map of Plant Community Types developed from an ensemble of visual interpretation of high resolution imagery and spatial distribution models.

WOODY EXTENT LAYER: A map of woody vegetation derived from classification of 5m SPOT-5 imagery.

KEITH CLASS: A map based on aerial photo interpretation and spatial distribution models.

MAP SOURCE: A map of the various sources of information used including spatial models, visual interpretation and existing map products.

SURVEY DENSITY ALL: A map of the density of all survey sites used.

SURVEY DENSITY FULL FLORISTICS: A map of the density of only full floristic survey sites used.

MODELLING CONFIDENCE: A map of the confidence outcomes achieved.

While much of the aerial photo interpretation employed was undertaken at around 1:8000, PCT attribution is generally at a much coarser scale. The Map Source layer (as described above) can be used as a guide to how vegetation attribution was derived. We recommend that the highest resolution appropriate for this product be 1:15000.

Validation Summary:

PCT Map: Based on 100% of the survey data (modelling and hand mapping), the final mapped product has an accuracy in the range 68%-70% for prediction of the three most likely PCTs. Be aware that these accuracies are highly variable across each PCT. Some PCT's utilised more site data than others. Keith Class reached a 76% accuracy using the independent test data. Modelled PCT and modelled top 3 PCT overall accuracies were 53% and 68% respectively. Woody Extent received a 92% overall accuracy.

Accompanying documents:
BRGNamoi Technical Notes.pdf - Technical Report
BRGN_PCT_KC_LUT.xls - A look-up table listing the relationship between PCT, Keith Class and Keith Formation classifications.\ BRGNv2_Spatial_Layer_Descriptors.txt
BRGN_V2.mxd
Border Rivers Gwydir / Namoi Regional Native Vegetation Mapping
Technical Notes Version 1.0. Reference: NSW Office of Environment and Heritage, 2015. BRG-Namoi Regional Native Vegetation Mapping. Technical Notes, NSW Office of Environment and Heritage, Sydney, Australia.

The download package contains a "quick view" map composite of the study area only. The quick view maps are of PCT, Keith Class, Keith Form, Map Source and Modelling Confidence. They also show the broad-scale line work. For more detailed line work and woody percent per polygon, please refer to the full dataset.

For access queries regarding the full dataset, please contact: data.broker@environment.nsw.gov.au

BRG_Namoi_v2_0_E_4204.
\ VIS_ID 4204

Purpose

This dataset was developed as part of the OEH State Vegetation Map to provide government and community with regional-scale information about native vegetation.

Dataset History

A summary of the product's lineage is below. Please refer to the Technical Notes v1.0 for a detailed description of the methodologies and source datasets.

The PCT map was derived primarily using a spatial modeling approach augmented with high resolution aerial imagery (50cm ADS40) for visual interpretation and automated line-work derivation.
\ In summary the process for PCT attribution involved the following:

1. Vegetation Survey and Classification: Existing floristic plot data comprised 9054 existing sites after data cleaning. A large number of gaps in existing survey coverage were evident and required further survey information. Stratification based on archive broad vegetation type mapping (Regional Vegetation Types; Eco Logical Australia 2008b) and gap analysis was undertaken to select locations for additional plot data collection. A total of 6013 additional rapid data points were collected. To allocate survey sites to PCTs, full floristic plots were analysed using a UPGMA clustering approach in Primer with significant groups identified using SIMPROF and species contributions for each resulting group calculated using SIMPER. The existing plot data were allocated across 258 PCTs.

2. Pattern Derivation: A multi-resolution segmentation algorithm was used to create image objects with low internal variation. Image objects represent patches of vegetation that can later be classified based on attributes such as crown cover, spectral response, or soil type. The segmentation parameters and scale was derived iteratively based on visual inspection. Vegetation patterns from existing stereoscopic aerial photo interpretation and those recognised in high spatial resolution imagery (ADS40) were used as a reference point. Segmentation was performed using ADS40, SPOT 5 and SRTM derived topographic indices. this process provided the line work for subsequent PCT attribution.

3. Visual attribution of Landscape Class: The purpose of attributing Landscape classes to polygons is to predetermine broad vegetation types for modelling purposes using remote sensing. These classes reduce the PCT options for any one polygon making the modeling more effective in its attribution with commensurate less computing effort/time. A landscape class was attributed to every polygon in the study area. Landscape classes were aided by reference to existing mapping. Corrections were made based on ADS40 with on-screen attribution. Every polygon was visually checked by an expert interpreter.

4. Modelling Envelopes:As a further constraint to modelling outcomes, spatial envelopes were used to constrain PCTs to a certain geographic range, reducing the amount of types competing within the model at any particular location. The constraints used were applied at different stages in the mapping process. The Keith Class (Keith 2004) models were constrained to particular IBRA (Interim Bioregionalisation of Australia v7; Commonwealth of Australia 2012) subregions, selected based on review of the literature and expert opinion. The type models were constrained to particular ranges of a topographic position index, again based on literature review and expert opinion. Not all types were constrained by topographic envelopes, as some were considered to be less correlated with particular topographic positions.

5. Spatial Distribution Modelling of Keith Classes and Plant Community Types. Modelling of Keith Class and PCT used a combination (ensemble) of Generalised Dissimilarity Model (GDM), Boosted Regression Trees (BRT), and a simple Nearest Neighbour model.A suite of candidate environmental predictor variables, including climate, geology, soil, geophysical data, and terrain indices, were compiled for use in the GDM and BRT models. A comprehensive list of these predictor variables can be found in the Technical Notes v1.0.

6. Uplifted API and Expert Editing: Vegetation communities from the Gwydir Wetlands and Floodplain Vegetation Map 2008 (Bowen & Simpson 2010) were spatially translated into the current line-work via a majority extent per polygon algorithm. The vegetation community mapping resulting from the aforementioned procedures was extensively edited on screen to correct attribution where there may have been for example existing API, missed vegetation, ecological anomalies, incorrect assignments, modelling noise and inclusion of late site data. The extent of each attribution source is delineated by the Map Source data layer provided in this dataset.

For further details on methodology and validation please refer to the Border Rivers Gwydir / Namoi Regional Native Vegetation Mapping
Technical Notes Version 1.0. Reference: NSW Office of Environment and Heritage, 2015. BRG-Namoi Regional Native Vegetation Mapping. Technical Notes, NSW Office of Environment and Heritage, Sydney, Australia.

Dataset Citation

NSW Office of Environment and Heritage (2015) Border Rivers Gwydir / Namoi Regional Native Vegetation Map Version 2.0. VIS_ID 4204. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/b3ca03dc-ed6e-4fdd-82ca-e9406a6ad74a.