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.

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 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.

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 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 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.

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).

Additional file 3: Supplementary Table 1. Predicted 2020 population, number of existing Australian 5 km parkrun events (at July 2021), and number of proposed parkrun events, in greater capital city and regional areas for each state. Supplementary Figure 1a. Map of current and proposed events for the greater capital city (Sydney) region of New South Wales. For this and all subsequent figures, numbered locations correspond to the order of selection by the location-allocation algorithm listed in Supplementary Table 2; the same population density scale has been used for all figures. Supplementary Figure 1b. Map of current and proposed events for regional New South Wales (NSW). Note for this and subsequent regional maps, event locations in the greater capital city are suppressed because of heavy clustering. Note also that the locations selected by the algorithm represent only the centroid of the SA2 area; sometimes this coincides with a regional town, but often the nearest town is visible as a darker purple area indicating higher population density. See the online interactive map for further details. Supplementary Figure 2. Map of current and proposed events for the Northern Territory (NT). Supplementary Figure 3. Map of current and proposed events for Queensland (QLD). Supplementary Figure 4. Map of current and proposed events for South Australia (SA). Supplementary Figure 5. Map of current and proposed events for Tasmania (TAS). Supplementary Figure 6a. Map of current and proposed events for the greater capital city (Melbourne) region of Victoria. Supplementary Figure 6b. Map of current and proposed events for regional Victoria (VIC). Supplementary Figure 7a. Map of current and proposed events for the greater capital city (Perth) region of Western Australia. Supplementary Figure 7b. Map of current and proposed events for regional Western Australia (WA). Supplementary Figure 8. Map of current events for the Australian Capital Territory (ACT). Note that no new events were proposed. Supplementary Table 2. Locations of new events selected by the location-allocation algorithm.

Identifying environmental characteristics that limit species’ distributions is important for contemporary conservation and inferring responses to future environmental change. The Tasmanian native hen is an island-endemic flightless rail and a survivor of a prehistoric extirpation event. Little is known about the regional-scale environmental characteristics influencing the distribution of native hens, or how their future distribution might be impacted by environmental shifts (e.g., climate change). Using a combination of local fieldwork and species distribution modelling, we assess environmental factors shaping the contemporary distribution of the native hen, and project future distribution changes under predicted climate change. We find 37.2% of Tasmania is currently suitable for the native hens, owing to low summer precipitation, low elevation, human-modified vegetation, and urban areas. Moreover, in unsuitable regions, urban areas can create ‘oases’ of habitat, able to support populations with high breeding activity by providing resources and buffering against environmental constraints. Under climate change predictions, native hens were predicted to lose only 5% of their occupied range by 2055. We conclude that the species is resilient to climate change and benefits overall from anthropogenic landscape modifications. As such, this constitutes a rare example of a flightless rail to have adapted to human activity. Methods Local-scale factors measurements (fieldwork) We selected geographically distant populations presenting different rainfall profiles during the late-autumn to spring period, April-November 2019, as rainfall is an important factor for native hens’ survival and reproduction (Ridpath, 1972a; Lévêque, 2022): ‘East’ (wukaluwikiwayna/Maria Island National park; 42°34'51"S 148°03'56"E), ‘North’ (Narawntapu National park; 41°08'53"S 146°36'52"E), and ‘West’ (adjacent to the town of Zeehan [712 inhabitants]; 41°53'03"S 145°19'56"E). The period April-November corresponds to the six-month period preceding the middle point of the breeding season, generally used for native hens’ surveys (Goldizen et al., 1998; Lévêque, 2022). All three populations were surveyed between the 10th and the 22nd of November 2019 (late spring, in the middle point of the breeding season) to determine population structure (total number of groups, group composition [number of adults and young], and breeding activity). Each population was monitored over two to five days, depending on habitat complexity and extent of the population area, until all native hens in the area had been surveyed, i.e., when the territories’ structure was found identical at least four times for populations with no previous data (‘North’ and ‘West’), and at least two times in well-known populations (‘East’; Lévêque, 2022), over two different half-day. To align with methods used by Lévêque (2022), we used territory mapping (Bibby et al., 2000; Gibbons & Gregory, 2006) as native-hens maintain year-round territories, and population sizes were measurable with our survey methodology. Territory mapping consists of establishing the location of birds over a number of visits to obtain distinct clusters representing each territory. Boundaries are determined by vocal disputes between neighbours, which are frequent in native hens. During each survey, a minimum of two observers conducted repeated group identification, based on location, neighbours’ location, and number of individuals per group (from two to five individuals per group in this study). The number of individuals and their age category (fledgling, juvenile, or adult) were recorded per territory. The total pasture area surveyed per population, and the total pasture area occupied by native hens were: North population: 2.0 km2 (1.3 km2 occupied); West population: 1.5 km2 (0.7 km2 occupied); East population: 1.5 km2 (0.6 km2 occupied). We measured environmental characteristics in the native-hens’ territory following methods established by Goldizen et al. (1998) to obtain quantitative measures of i) protection cover, ii) water availability, and iii) food availability; these parameters are important for native hen reproduction (Goldizen et al., 1998).

Protection cover was determined as the length (m) of the interface between dense patches of bushes and pasture, used by native hens for hiding and protecting chicks against predators (Lévêque, 2022). It is an important parameter for breeding success (Goldizen et al., 1998). We measured the total protection cover available to native hens in each population using satellite data from Google Maps (www.google.com/maps, accessed on 09/12/2019). For measures of food availability (grass) on territories, we selected random transects of a total length of 1 m across all territories (East: n = 15, North: n = 26, West: n = 22). Measurements of vegetation characteristics were measured and recorded every 2 cm along each transect, including the percentage of i) total vegetation cover, ii) green vegetation, iii) vegetation cover that was grass, iv) vegetation cover that was moss, and v) the grass height (average length of grass blades). The same observer (LL) recorded all measures. Water availability on territories was recorded as territories that had access to water (running or stagnant) at the time the surveys were undertaken. Rainfall data was collected from the Bureau of Meteorology (B.O.M.; www.bom.gov.au/climate/data) at the three population sites: North population at Port Sorell (Narawntapu National Park – 4km away from the population site), West population at Zeehan (West Coast Pioneers Museum), East population at Maria Island (Darlington). Rainfall was reported as the amount of rainwater that had accumulated i) during the six months prior to breeding season midpoint (31/10/2019); following Goldizen et al. (1998)) and ii) during summer [December-February]. Information on recent droughts (on a 3- to 11-month period prior to 31/10/2019) was assessed using values on rainfall percentile deficiency (below the 10th percentile) from B.O.M. (http://www.bom.gov.au/climate/drought/#tabs=Rainfall-tracker). The 6-, 7-, and 12- month-periods were not accessible. B.O.M. defines the category ‘Serious deficiency’ as rainfall that “lies above the lowest five percent of recorded rainfall but below the lowest ten percent (decile range 1) for the period in question”, and ‘severe deficiency’ as “rainfall is among the lowest five percent for the period in question”.

Species Distribution Modelling Data preparation We collected presence-point data for native hens across Tasmania from the Atlas of Living Australia (ALA: www.ala.org.au; accessed 19 February 2021). We additionally included data from BirdLife Tasmania, the Department of Primary Industries, Water and Environment (DPIPWE) reports, and our personal observations, resulting in a total of 23,923 occurrences. Our study area included the Tasmanian mainland and nearby islands, however a large area from the south-west of Tasmania was removed where native hen distribution is not well documented, however, they are thought to be rare or absent in this region due to large proportion of button grass vegetation creating unsuitable habitat (Fig. S2). All subsequent analyses were undertaken in Program R v4.0.4 (R Core Team, 2021). Duplicates were removed by converting presence points into grid presences at 1 km2 resolution and retaining one native hen observation per grid (n = 2447 grid points after this step). Occurrences were visually inspected for any potential errors/outliers from outside Tasmania and Tasmanian islands: this removed seven false occurrences on King and Flinders islands and two observations in freshwater inland lakes (Lake Crescent and Great Lake). As true-absence records were mostly unavailable, we generated pseudoabsences for sites where other land-bird species had been recorded (indicating observation effort at that point), but without native hen detections (Hanberry et al., 2012; Amin et al., 2021; Barlow et al., 2021). Native hens are large-bodied, ground-dwelling, active in the day, and have a loud, distinct call, all of which accounts for a high detectability, if present at a location. We extracted these data from ALA, with 780,499 possible observations on the Tasmanian mainland and all nearby islands. We then excluded all grid cells with a native hen presence and removed any records within 3 km of native hen records: this value was chosen because it is the dispersal distance under which a native hen can naturally move outside of its territory (Ridpath, 1972a). This process resulted in 3,222 pseudoabsence grid points. Citizen-science datasets offer unique opportunities to study a species distribution using ‘crowd-sourced’ effort, however, they tend to be access-biased and have non-random, clustered observations, leading to overrepresentation of certain regions and biases towards some environmental conditions (usually near urban areas; Steen et al., 2021). One way to reduce spatial autocorrelation is to selectively de-cluster occurrences in biased areas using a pre-defined (minimum linear) Nearest Minimum-neighbour Distance NMD (Pearson et al. 2007). As un-urbanised, sparsely populated areas have the least spatial point clustering (and hence spatial bias), the average number of observations in low human densities areas provides the threshold number of records that can be used to tune and select the optimal NMD (Amin et al., 2021). Therefore, we subdivided our data on a grid of 25 km2 cells to be relevant to the metric of human density and used the median of population density index (excluding cells < 1 human/km2) to define thresholds for low and high density. Population density was extracted from the ‘2011 Census of Population and Housing across Australia’ (bit.ly/3bth7W9). ‘Low density’ was defined as < 6 people/km2 and ‘High density’ as

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.

The Meeberrie earthquake is the largest known onshore Australian earthquake. Its magnitude was ML 7.2 and it was felt over a wide area of Western Australia as shown on the isoseismal map below, from Port Hedland in the north to Albany and Norseman in the south.

Damage from the earthquake was small because of the low population density in the epicentral region, but the shaking at Meeberrie homestead was very severe; all the walls of the homestead were cracked, several rainwater tanks burst, and widespread cracking of the ground occurred. Minor non-structural damage was reported in Perth more than 500km away from the epicentre.

The road density was represented by the child care center’s nearest distance to main road and motorway, and the length of main road/motor way within 100~1000-meter buffer zone surrounding the child care center. We also got the data of PM2.5 concentration from 2013 to 2018 and standard Normalized Difference Vegetation Index (NDVI) data from 2013 to 2019 according to the longitude and latitude of the child care centers. This data might help researchers to evaluate the health impacts of road density on child health, and help policy makers to make transportation, educational and environmental planning decisions to protect children from exposure to traffic-related hazards in Australia.The data of registered child care centers were from the website of Australia’s Children’s Education & Care Quality Authority. Data of proximity to main road and motorway of child care centers’ were from google map using R software (version 3.5.1). Data of vectors of the Australian road network were from Open Street Map. The final data includes 16,146 child care centers and 1,002,600 approved children. The presented data allow for spatial aggregations of the child care centers, the proximity to the main road and motorway. This data could be used to analyze the health risk and disease burden for children from exposure to traffic by combining the mortality or morbidity data in Australia. The implementation of the data may help better design and redistribution of child care centers, and assist transportation and environmental planning for the governments. Data of Surrounding road density of child care centers, Australia as the Microsoft Excel file can be freely accessed via the Science Data Bank at http://www.dx.doi.org/10.11922/sciencedb.00728 (last update: 2021-03-23).

When 23 million Australians filled in their Census forms in 2016, 3.3% of working age adults (15 to 64 years) were identified as having a disability as they reported needingassistance in their self care, mobility and communication in their daily living.Our team at the Centre of Research Excellence in Disability and Health (CRE-DH) has released an Interactive Map, and an accompanying Technical Report.Access the map here: http://go.unimelb.edu.au/pfv6The map shows the proportion of working age adults who were identified as having a disability across Australia. It shows that there is considerable variation in the prevalence of disability between small geographic areas. Users can explore specific geographic areas of interest through the built-in navigation.The Technical Report documents some key decisions on the map specifications and covers topics on data and definition, selection of geographic unit, impact of small counts, missing values, age standardisation, and data stability over time. It also gives a series of snapshots of several regions across the country where very high proportions of working age adults resided.The source of the data referred to in this report is the Census of Population and Housing 2016 collected by the Australian Bureau of Statistics (ABS). The team would like to acknowledge the work of the ABS.NOTE: The geographic areas used in the map are the Statistical Area Level 2 (SA2) as defined in Australian Statistical Geography Standard (ASGS) by the Australian Bureau of Statistics (ABS). The SA2 is the smallest area used in the majority of ABS statistical releases and generally have a population range of 3,000 to 25,000 persons. On average they contain about 6,600 working age adults.

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 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.

This dataset contains the estimated distribution and abundance of cassowaries across the Wet Tropics Region and the sub-regions used in these estimates. The key areas for cassowary conservation are those with the highest densities and abundance.

This data is based on field surveys and data on cassowary density derived from faecal-DNA analysis of dung samples collected during those surveys.

Methods:

Surveys were conducted along 157 transects distributed across the Wet Tropics Region during 2012 - 2014. During these surveys all forms of cassowary sign were recorded and in the case of dung the dung was also collected. Cassowary DNA was extracted from the dung samples and DNA fingerprinting analysis was used to identify individuals. Based on the relationship between dung encounter rates and density of cassowaries at 8 focal sites, the density of cassowaries and their abundance on transects in each of 23 sub - regions was estimated. The sub - regions were based on the estimated cassowary essential habitat. Essential habitat is based on verified sightings of the Southern Cassowary since 1980 and includes vegetation considered to be important for the species in terms of foraging, breeding or other parts of the species' life cycle. This habitat is considered to be necessary for the long-term survival of the cassowary. The important vegetation types associated with essential habitat (91 Regional Ecosystems) are listed within Kutt and colleagues (2004). Further details of the methods are provided in the technical report for this work, Westcott (2014).

Limitations:

eAtlas Note: The original shapefile provided by the researchers has very fine boundary mapping from the cassowary essential habitat mapping. This is then broken down into 23 regions. The scale of these two datasets is vastly different and needs to be considered when using or interpreting this data. A slightly lower resolution version of this data was prepared for download from the eAtlas. This reduces the fine scale habitat mapping slightly, whilst retaining the cassowary regional information. The mapping layer associated with this record corresponds to the original high resolution version.

Format:

Original Shapefile (452 polygons, 25 MB) of the 23 subregions of the Wet Tropics with estimated density and animal numbers. The detail of the region boundaries are based on very detailed estimated essential cassowary habitat mapping Kutt (2004).

Simplified Shapefile (23 polygons, 2.6 MB). This contains the same cassowary estimated density and animal numbers across the Wet Tropics except with simplified habitat mapping. This was derived from the original by dissolving polygons with the same data, then smoothing the polygon using a 150m filter, then simplifying the polygon with an allowable tolerance of 15 m and removing features smaller than 2 hectares. This shapefile was prepared by the eAtlas team. This simplified version is more suitable for large scale maps.

Data Dictionary:

• CassBound: Name of the cassowary mapping region
• CassDens: Estimated density of cassowary in animals per square km.
• Cass_num: Estimated number of cassowaries in region.
• Hectare: Area of the mapping region in Hectares.

References:

Kutt A. S, King S., Garnett S. T. & Latch P. (2004) Distribution of cassowary habitat in the wet tropics bioregion, Queensland. Technical Report to the Queensland Environmental Protection Agency. Environment Protection Agency, Brisbane, Queensland. Westcott, D.A., Metcalfe, S., Jones, D., Bradford, M., McKeown, A., Ford, A. (2014) Estimation of the population size and distribution of the southern cassowary, Casuarius casuarius, in the Wet Tropics Region of Australia. Report to the National Environmental Research Program. Reef and Rainforest Research Centre Limited, Cairns (21pp.).

A series of maps were produced for the publication "Flight paths for helicopter operations in the Australian Antarctic Territory", originally published in hard copy in September 2000. These superseded a series published in 1999.

A new edition of the maps was produced in 2011.

The maps are digitally available from the SCAR Map Catalogue. See the Related URL below.

Digital soil maps (DSMs) are prepared through quantitative modelling techniques that are based on relationships between soil attributes and the environment. DSMs are presented over NSW for a range of key soil properties, including soil organic carbon (SOC), pH, cation exchange capacity, sum-of-bases, available phosphorous, bulk density, clay, silt and sand (total and fine). The maps are at 100 m spatial resolution and cover ten soil depth intervals down to 2 m, consistent with widely used Australian and international systems. Random Forest decision tree modelling techniques were applied. Validation results for the maps indicate generally moderate to high performance and effectiveness. Maps of mean plus upper 95% and lower 5% prediction limits are available. The maps provide at least a useful first approximation of these soil properties across the State. The products are described more fully in the technical report: Gray (2023), Digital soil mapping of key soil properties over NSW, version 2.0 (76p). The report and raster layers can be downloaded through the NSW environmental data portal SEED (https://www.seed.nsw.gov.au/) and are also viewable through the DPE soil and landscape spatial viewer eSPADE (http://espade.environment.nsw.gov.au ). All maps, including prediction limits and intervals, are also available through the DPE data broker.

This is a PDF format map of the country, as released by the United Nations.

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.\
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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.\
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The full dataset comprises the following data layers as delivered in an ArcGIS 9.3 File Geo-database:\
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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.\
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WOODY EXTENT LAYER: A map of woody vegetation derived from classification of 5m SPOT-5 imagery.\
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KEITH CLASS: A map based on aerial photo interpretation and spatial distribution models.\
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MAP SOURCE: A map of the various sources of information used including spatial models, visual interpretation and existing map products.\
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SURVEY DENSITY ALL: A map of the density of all survey sites used.\
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SURVEY DENSITY FULL FLORISTICS: A map of the density of only full floristic survey sites used.\
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MODELLING CONFIDENCE: A map of the confidence outcomes achieved.\
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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.\
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Validation Summary:\
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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.\
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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.\
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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.\
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For access queries regarding the full dataset, please contact: data.broker@environment.nsw.gov.au\
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BRG_Namoi_v2_0_E_4204.\
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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.\
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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.\
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In summary the process for PCT attribution involved the following: \
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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.\
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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.\
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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.\
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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.\
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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.\
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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.\
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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.