The Millennium Coral Reef Mapping Project provides thematic maps of coral reefs worldwide at geomorphological scale. Maps were created by photo-interpretation of Landsat 7 and Landsat 8 satellite images. Maps are provided as standard Shapefiles usable in GIS software. The geomorphological classification scheme is hierarchical and includes 5 levels. The GIS products include for each polygon a number of attributes. The 5 level geomorphological attributes are provided (numerical codes or text). The Level 1 corresponds to the differentiation between oceanic and continental reefs. Then from Levels 2 to 5, the higher the level, the more detailed the thematic classification is. Other binary attributes specify for each polygon if it belongs to terrestrial area (LAND attribute), and sedimentary or hard-bottom reef areas (REEF attribute). Examples and more details on the attributes are provided in the references cited. The products distributed here were created by IRD, in their last version. Shapefiles for 29 atolls of Australia as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Funded by National Aeronautics and Space Administration, NASA grants NAG5-10908 (University of South Florida, PIs: Franck Muller-Karger and Serge Andréfouët) and CARBON-0000-0257 (NASA, PI: Julie Robinson) from 2001 to 2007. Funded by IRD since 2003 (in kind, PI: Serge Andréfouët).

Public Open Space Geographic Information System data collection for Perth and Peel Metropolitan Areas

The public open space (POS) dataset contains polygon boundaries of areas defined as publicly available and open. This geographic information system (GIS) dataset was collected in 2011/2012 using ArcGIS software and aerial photography dated from 2010-2011. The data was collected across the Perth Metro and Peel Region.

POS refer to all land reserved for the provision of green space and natural environments (e.g. parks, reserves, bushland) that is freely accessible and intended for use for recreation purposes (active or passive) by the general public. Four types of “green and natural public open spaces” are distinguished: (1) Park; (2) Natural or Conservation Area; (3) School Grounds; and (4) Residual. Areas where the public are not permitted except on payment or which are available to limited and selected numbers by membership (e.g. golf courses and sports centre facilities) or setbacks and buffers required by legislation are not included.

Initially, potential POSs were identified from a combination of existing geographic information system (GIS) spatial data layers to create a generalized representation of ‘green space’ throughout the Perth metropolitan and Peel regions. Base data layers include: cadastral polygons, metropolitan and regional planning scheme polygons, school point locations, and reserve vesting polygons. The ‘green’ space layer was then visually updated and edited to represent the true boundaries of each POS using 2010-2011 aerial photography within the ArcGIS software environment. Each resulting ’green’ polygon was then classified using a decision tree into one of four possible categories: park, natural or conservation area, school grounds, or residual green space.

Following the classification process, amenity and other information about each POS was collected for polygons classified as “Park” following a protocol developed at the Centre for the Built Environment and Health (CBEH) called POSDAT (Public Open Space Desktop Auditing Tool). The parks were audited using aerial photography visualized using ArcGIS software. . The presence or absence of amenities such as sporting facilities (e.g. tennis courts, soccer fields, skate parks etc) were audited as well as information on the environmental quality (i.e. presence of water, adjacency to bushland, shade along paths, etc), recreational amenities (e.g. presence of BBQ’, café or kiosks, public access toilets) and information on selected features related to personal safety.

The data is stored in an ArcGIS File Geodatabase Feature Class (size 4MB) and has restricted access.

Data creation methodology, data definitions, and links to publications based on this data, accompany the dataset.

PLEASE NOTE: These data do not include data over Tasmania. Please see links relevant to that area.

GEODATA TOPO 250K Series 3 is a vector representation of the major topographic features appearing on the 1:250,000 scale NATMAPs supplied in KML format and is designed for use in a range of commercial GIS software. Data is arranged within specific themes. All data is based on the GDA94 coordinate system.

GEODATA TOPO 250K Series 3 is available as a free download product in Personal Geodatabase, ArcView Shapefile or MapInfo TAB file formats. Each package includes data arranged in ten main themes - cartography, elevation, framework, habitation, hydrography, infrastructure, terrain, transport, utility and vegetation. Data is also available as GEODATA TOPO 250K Series 3 for Google Earth in kml format for use on Google Earth TM Mapping Service.

Product Specifications

Themes: Cartography, Elevation, Framework, Habitation, Hydrography, Infrastructure, Terrain, Transport, Utility and Vegetation

Coverage: National (Powerlines not available in South Australia)

Currency: Data has a currency of less than five years for any location

Coordinates: Geographical

Datum: Geocentric Datum of Australia (GDA94)

Formats: Personal Geodatabase, kml, Shapefile and MapInfo TAB

Release Date: 26 June 2006

The Digital Surficial Geologic-GIS Map of Isle Au Haut and Immediate Vicinity, Acadia National Park, Maine is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (isha_surficial_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (isha_surficial_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (acad_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (acad_surficial_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (isha_surficial_geology_metadata_faq.pdf). Please read the acad_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Maine Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (isha_surficial_geology_metadata.txt or isha_surficial_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This software contains the v1.0.0 release of Nilas: the south ocean mapping platform (https://nilas.org). This mapping tool (beta) has been developed by the Australian Antarctic Division for the Antarctic sea-ice zone to support their research and operational activities. Nilas displays multiple layers of physical and biogeochemical variables. These variables are primarily derived from remotely sensed products and updated as source data become available. The source code is well documented with both readme files and inline comments. This application is written primarily in javascript and was developed using Node.js, vite and a small amount of vue. The Nilas platform was based on the Leaflet open source library. It can be configured to display other Antarctic related geospatial products including raster and vector data.

See the related record, "AAS_4506_NILAS_DATA" for data from this project.

Area layers of US, Australia, and Canada building footprints for use with GIS mapping software, databases, and web applications.

A legacy of over 500 paper maps records geological lineament analysis of Australia conducted by the late Tim O'Driscoll in Western Mining Corporation Exploration Division during the 1960s to 1980s. …Show full descriptionA legacy of over 500 paper maps records geological lineament analysis of Australia conducted by the late Tim O'Driscoll in Western Mining Corporation Exploration Division during the 1960s to 1980s. The lineament interpretations were used to target mineral exploration, famously including the analysis that led to the discovery of the Olympic Dam deposit in South Australia. Papers discussing the lineament approach are collected in Bourne & Twidale (2007). Lineaments were interpreted from a range of data available at the time, including magnetic and gravity maps, topography, standard geological maps, and 'chicken track'interpretation of aerial photographs and early satellite images. This product comprises high quality digital scans of 130 of the original paper maps, rectified and georeferenced for use in GIS software. Geoscience Australia reproduces these maps and makes them available publicly for their historic and scientific interest. The paper originals are held in the Geoscience Australia library.

Drought and aridity hazard regional change summaries for the National Climate Risk Assessment (NCRA). The drought and aridity hazards have been summarised for boundaries relating to assessments of freshwater and terrestrial natural environments.

Australian Climate Service (ACS) drought and aridity hazard datasets (https://github.com/AusClimateService/hazards-drought) 1. AI - Aridity index 2. SPI3 - Standardised precipitation index 3

Each hazard dataset includes an ensemble of climate models for four global warming levels (GWLs), 1.2, 1.5, 2.0 and 3.0 degrees Celsius, above a pre-industrial mean for 1850 to 1900.

Either the absolute change (difference to GWL1.2) or the relative change (difference to GWL1.2 divided by GWL1.2) was calculated for each of the higher GWLs compared to GWL1.2.

Regions of interest boundaries 1. Australia 2. NCRA regions 3. Aggregate Ecological Groups (AEGs) 4. Combined NCRA regions and AEGs 5. Level 2 drainage basins from the Australian Hydrological Geospatial Fabric (AHGF) 6. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with perennial drainage lines from the AHGF Network Streams 7. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with ephemeral drainage lines from AHGF Network Streams

For each hazard/GWL-change combination, a set of statistics (the mean, 10th percentile, 50th percentile, 90th percentile and standard deviation) was calculated for each climate model in the ensemble over the regions of interest areas. From these, the median of each statistic from all the models in the ensemble was calculated.

Collection data files The outputs are tabular data in comma-separated value (csv) files with one file per hazard/GWL-change combination. The columns are ID and region name that link to the regions of interest boundary datasets for visualising in GIS software, and the medians of the statistics. Rows are the individual region of interest polygons. Lineage: DATASETS Hazard data The National Climate Risk Assessment (NCRA) hazard data were supplied by the Australian Climate Service (ACS) from data stored on the National Computational Infrastructure (NCI) as part of Project ia39. The hazard data came in the form of an ensemble of climate models, with outputs for four global warming levels (GWLs) from each of the individual models. The GWLs are 1.2, 1.5, 2.0 and 3.0 degrees Celsius above a pre-industrial mean for 1850 to 1900.

Australian Climate Service (ACS) drought and aridity hazard datasets (https://github.com/AusClimateService/hazards-drought) used in this collection: 1. AI - Aridity index 2. SPI3 - Standardised precipitation index 3

Regions of interest boundaries The hazard data were summarized by regions of interest using polygon shape files for the following areas: 1. Australia - 2021 - Shapefile (https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/digital-boundary-files) 2. NCRA regions (https://www.acs.gov.au/datasets/4f8b960fd3694fc28d6ff3d1278e9e75_0/about) 3. Aggregate Ecological Groups (AEGs) derived from National Vegetation Information System (NVIS) data (https://data.csiro.au/collection/csiro:64128) 4. Spatially combined NCRA and AEG polygons (https://data.csiro.au/collection/csiro:64133) 5. Level 2 drainage basins from the Australian Hydrological Geospatial Fabric (AHGF) (http://www.bom.gov.au/water/geofabric/download.shtml) 6. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with perennial drainage lines from the AHGF Network Streams (where the Perennial attribute is "Perennial") (https://data.csiro.au/collection/csiro:64133; drainage lines from http://www.bom.gov.au/water/geofabric/download.shtml) 7. Spatially combined AHGF Level 2 drainage basins and hazard dataset grid cells which intersect with ephemeral drainage lines from AHGF Network Streams (where the Perennial attribute is "Non Perennial") (https://data.csiro.au/collection/csiro:64133; drainage lines from http://www.bom.gov.au/water/geofabric/download.shtml)

METHODS The drought and aridity hazard datasets were summarised for each set of regions of interest boundaries using methods and code from the ACS (https://github.com/AusClimateService/plotting_maps).

Either the absolute change (difference to GWL1.2) or the relative change (difference to GWL1.2 divided by GWL1.2) was calculated for each of the higher GWLs compared to GWL1.2.

Absolute change was calculated for AI and SPI3 using the Australia, NCRA, AEG and combined NCRA-AEG boundaries. Relative change was calculated for AI for the drainage basins, the combined drainage basins-perennial drainage lines, and the combined drainage basins-ephemeral drainage lines boundaries.

For each hazard/GWL-change combination, a set of statistics (the mean, 10th percentile, 50th percentile, 90th percentile and standard deviation) was calculated for each climate model in the ensemble over the regions of interest areas. From these, the median of each statistic from all the models in the ensemble was calculated. For a more detailed description of the methods, see the 'Spatial data processing methods' file in the Supporting Documentation section.

OUTPUTS - COLLECTION DATA FILES The outputs are tabular data in comma-separated value (csv) files with one file per hazard/GWL-change combination. The columns are ID and region name (linking to the boundary datasets for visualising in GIS software), and the medians for each of the statistics. Rows are the individual region of interest areas (polygons).

Digital Geology and Lithology maps of the Strangways Range Region in the eastern Arunta Region of the Northern Territory have been produced from a scanned image of the first edition map published by the Bureau of Mineral Resources in 1984. The image was digitised using Microstation and ArcInfo software, and attributed to meet standards for Version 2004.01 of the Geoscience Australia Digital Data Dictionary for GIS Produces as closely as possible. The finished product has been provided as ArcView shape files and ArcInfo export files on CD-ROM. Extensive internal quality assurance and quality control processes have been used to verify the data.

Proportional change in effective area of similar ecological environments for Mammals as a function of land clearing within the present long term (30 year average) climate (1990 centred) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover.

This metric describes the effects of land clearing on the area of similar environments to each grid cell as a proportion. Each cell is compared with a sample of 60,000 points in both uncleared landscape and degraded landscape (pairwise similarities summed (e.g. a completely similar cell will contribute 1, a dissimilar cell 0, with a range of values in between). The contribution of each cell is then multiplied by a 0 (cleared) to 1 (intact) condition index based on the natural areas layer. By dividing the test area by the current area, we are able to quantify the reduction in area as a function of land use/climate change. Values less than one indicate a reduction, values of 1 no change, and values greater than 1 (rare cases in the north) show an increase in similar environments.

This metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org.

Data are provided in two forms: 1. Zipped ESRI float grids: Binary float grids (.flt) with associated ESRI header files (.hdr) and projection files (.prj). After extracting from the zip archive, these files can be imported into most GIS software packages, and can be used as other binary file formats by substituting the appropriate header file. 2. ArcGIS layer package (.lpk): These packages contain can be unpacked by ArcGIS as a raster with associated legend.

Additionally a short methods summary is provided in the file 9sMethodsSummary.pdf for further information.

Layers in this 9s series use a consistent naming convention: BIOLOGICAL GROUP _ FROM BASE_ TO SCENARIO_ ANALYSIS e.g. A_90_CAN85_S or R_90_MIR85_L where BIOLOGICAL GROUP is A: amphibians, M: mammals, R: reptiles and V: vascular plants

Lineage: Proportional change in the area of similar ecological environments was calculated using the highly parallel bespoke CSIRO Muru software running on a LINUX high-performance-computing cluster, taking GDM model transformed environmental grids as inputs. Proportional change was calculated by taking the area of baseline ecological environments similar to each present cell as the denominator and the area of present cells with their contribution scaled by the natural areas condition index (0 degraded to 1 intact) as the numerator. More detail of the calculations and methods are given in the document “9sMethodsSummary.pdf” provided with the data download. GDM Model: Generalised dissimilarity model of compositional turnover in reptile species for continental Australia at 9 second resolution using ALA data extracted 28 February 2014 (GDM: REP_r3_v2) Climate data. Models were built and projected using: a) 9-second gridded climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment b) 9-second gridded climatology for continental Australia 2036-2065 CanESM2 RCP 8.5 (CMIP5): Summary variables with elevation and radiative adjustment Natural Areas Layer (intact to degraded land) Australian Government Department of the Environment (2014) Natural areas of Australia - 100 metre (digital dataset and metadata). Available at http://www.environment.gov.au/metadataexplorer/explorer.jsp and up to date information for Western Australia were provided at 25m Albers projection were reprojected to GDA94, merged and aggregated to a continuous measure of proportion of intact area per grid cell at 9s.

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.

These data represent the OZMIN Oracle relational database containing geological and resource information for Australian mineral deposits. OZMIN has been compiled from published references and has been designed so that attribute information can be retrieved and analysed in relation to spatial data contained in geographic information systems. The national mineral deposits dataset contains data on over one thousand major and historically significant mineral deposits for 60 mineral commodities (including coal). Data available via mapping interfaces on the Geoscience Australia website are updated weekly whilst data available via download are a snapshot at the "Ending Date" of the current database entries.

Full Metadata available at: http://www.ga.gov.au/meta/ANZCW0703003393.html

Dataset History

The data within this dataset is derived directly from the corporate ORACLE OZMIN Mineral Deposits database.

An ASCII extraction of the Geoscience Australia ORACLE database is generated as ASCII comma-delimited files for each table that is part of or used by the OZMIN database. Only data that is part of the current release of OZMIN (Release 3 - October 2000) is included.

An MS ACCESS database format is also replicated from the ORACLE database and uses the same table structure. Only data that is part of the current release of OZMIN (Release 3 - October 2000) is included.

The spatial representation of this database in (ArcView and MapInfo format) is extracted and generated using ArcInfo GIS software to meet the published data standard within the Geoscience Australia data dictionary. The extraction of the spatial GIS datasets is done within ArcInfo using advanced AML code (ORACOV.AML) developed by Dmitar Butrovski, Geoscience Australia.

Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-b68d-7506-e044-00144fdd4fa6/OZMIN+Mineral+Deposits+Database

Dataset Citation

Geoscience Australia (2013) OZMIN Mineral Deposits Database. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/34247a24-d3cf-4a98-bb9d-81671ddb99de.

This dataset contains maps showing the principal attributes of tides around the Australian coast. It has been derived from data published in the Australian National Tide Tables.

Format: shapefile.

Quality - Scope: Dataset. External accuracy: +/- one degree. Non Quantitative accuracy: Data are assumed to be correct. Three datasets describe tidal information around Australia:

Cover_Name, Item_Name, Item_Description:

TIDEMAX, MAX_TIDE_(M), Maximum tidal range in metres.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Data was projected to geographics using the WGS84 datum and spheroid, to be compatible for the Australian Coastal Atlas. The digital datsets were attributed using the information held in the legend (.key) files.

CSIRO: All 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. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary follows of processing completed by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and The Complete Book of Australian Weather). 2. From the information held in r-BASE a BASE Table was generated incorporating specific fields. 3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point. 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - created from the BASE map and table by 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 such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn). 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid. 8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

CAMRIS incorporates the Australian estuarine database, which includes the National Estuaries Study (Bucher and Saenger 1989, http://dx.doi.org/10.1111/j.1467-8470.1991.tb00726.x). Attributes include location, name, climatic variables, run-off coefficients, land use, flood frequency, water quality, habitat types including seagrass/mangrove/saltmarsh, fisheries/conservation/amenity values, administration, literature and threats.

Format: shapefile.

Quality - Scope: Dataset. Absolute External Positional Accuracy: Assumed to be correct. +/- one degree. Non Quantitative accuracy: The estuaries coverage contains 1566 points and the following attributes:

ESTUARY_NO : Inventory number, contains a letter prefix to denote State in which estuary lies. Estuaries are numbered clockwise around the continent.

NAME : Name of major input stream used to identify an estuary unless the estuary itself is named.

GEO_ZONE : Set of 12 coastal geographical zones (ACIUCN 1986).

CLIM_ZONE : Set of 3 named climatic zones.

CATCH_AREA : Catchment Area (sq km).

AVE_ANN_RF : Mean annual rainfall (mm), recorded at station nearest estuary.

RUNOFF_COEF : Runoff figure, best approximation to a catchment average rainfall, usually the average value for the respective drainage basin.

MAX_TIDAL_RANGE : Maximum tidal range (m).

WATER_AREA : Water area (sq km).

SAND-MUD_AREA : Sand and Mud Area (sq km).

MANGROVE_AREA : Area of Mangroves (sq km).

SEAGRASS_AREA : Area of Seagrass (sq km).

SALTMARSH_AREA : Area of Saltmarsh (sq km).

ESTUARINE_AREA : Est area of estuary (sq km).

GALLOWAY_SECTION : Galloway section number - each 3x10km strip is numbered, clockwise around the coast.

LONGITUDE : Longitude of estuary site (dd).

LATITUDE : Latitude of estuary site (dd).

LANDUSE_CODE : % catchment clearance.

FLOOD_REGIME : Frequency of flooding.

WATER-QUAL : Subjective assessment of water quality only.

MANGROVE_COVER : Degree Mangrove cover.

SEAGRASS_COVER : Degree Seagrass cover.

SALTMARSH_COVER : Degree Saltmarsh cover.

FISH_VALUE : Importance of an estuary as a commercial or amateur fishing ground.

FISH_THREAT : Threats to fisheries.

CONS_VALUE : Qualitative conservation values.

CONS_THREAT : Threats to conservation.

AMENITY_VALUE : Amenities value.

ECO_STATUS : Effects of human activity.

RESEARCH : Depth of information used to assess estuary.

ADMIN : Statutory classifications that restricts use.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Projected the estuaries point coverage to geographics with the WGS84 spheroid. The coverage has been attributed with information taken from the Bucher and Saenger (1989) National estuaries inventory.

CSIRO: 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. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary of data processing follows:

1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and Complete book of Australian Weather).

2. From the information held in r-BASE a BASE Table was generated incorporating specific fields.

3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point.

4. BASE Table imported into SPANS and a BASE Map generated.

5. Categorise Maps - created from the BASE map and table by 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 such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn).

7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid.

8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats.

9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

Curnamona Province MGA GIS DVDVersion 2 of the Curnamona Province dataset was produced as a co-operative venture between Primary Industries and Resources South Australia (PIRSA), GA (Geoscience Australia) and DMR (New South Wales Department of Mineral Resources). It contains data from South Australia and New South Wales.For a generalised map of Dataset Locations, click here. * Coordinate projection MGA Zone 54 (GDA94). * Dataset size 2.4 Gb * Available formats: ESRI shapefile (ArcGIS, ArcView), MapInfo on DVD-ROM. Users must provide their own software. * DVD-ROM Dataset Price AUD$20. To purchase online, please link to SARIG and go to the Products section.

Available at the Map and Data Library. CD #163.

The Cardiac ARIA project, with its extensive use of Geographic Information Systems (GIS), ranks each of Australia’s 20,387 urban, rural and remote population centres by accessibility to essential services or resources for the management of a cardiac event. Cardiac ARIA is based upon the accessibility concepts used to develop ARIA in that it calculates distance along the road network from all 20,387 population locations across Australia to the geolocated cardiac services and resources identified by the expert panel. Similar to the ARIA index, Cardiac ARIA can then be aggregated to any other area unit such as a local government area, statistical local area, postcode, census collection district or any other user defined catchments. Cardiac ARIA required a significant amount of detailed data, and sourcing these data for this project was a major undertaking (Seen in Table 2 of related publication). These data included context data such as the road network for Australia and population locations, to detailed data on the location of ambulance stations, hospitals, cardiac units etc. For a detailed list of all data collected and their sources for this project see Appendix 2 of related publication. The data collection consisted of two stages: Phase 1 - Expert panel consensus process.
To provide a single master list of services and resources, based on a composite of the practice guidelines for a “cardiac event”. Context of the study Implementation of the guidelines was to be set within all community settings with careful consideration given to the needs of populations (e.g. greater than 60 minutes from a CBD) in the urban fringes and rural and remote areas, and current literature. Once the master list was agreed upon, datasets with location information were sourced and applied to GIS software. The Cardiac ARIA index was developed by locating CVD services and resources within a population centre and then calculating the distance/time needed to travel by road to each CVD service (see the methods section for more detail). Distance by air (rotary and fixed wing aircraft) was also calculated, though not used in this version of the Cardiac ARIA index. Each population centre was assigned an index for CVD accessibility. Phase 2 - Data acquisition and GIS modelling. Phase 2 of the project relates directly to Objective 2 of the project: to derive a classification for each of Australia’s 20,387 urban, rural and remote population locations based on its access to cardiac services. The GIS methodology used to calculate both the acute and aftercare Cardiac ARIA uses raster based cost-distance mapping to generate cost distance layers for each input layer and the raster calculator to combine the layers into the final ARIA indices using ESRI’s Arc Map, version 9.3.1. The GIS software utilised for this project was ESRI ArcMap Version 9.3.1, with all of the data conforming to Australian Lamberts Conformal Conic projection, Geocentric Datum of Australia 1994 (ESRI Arcview 2011; ESRI Software 2011).

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.

This dataset gives the extents of South Australian pastoral lease stations and other properties within the pastoral region of SA. The extents of the properties shown are based on the areas being managed by the leasees and boundaries are defined by fence lines rather than legal lease boundaries. Fences and legal lease boundaries are frequently divergent.

Purpose

This dataset will show the extents of South Australian pastoral lease stations within the pastoral region of SA.

Dataset History

Assessment/Inspection officers visit each station and drive around the property using a mobile device with GPS capability for field data entry including tracking and waypoints of features. The station owner/manager also contribute new information. Maps are often used to explain complex fencing changes. The GIS officer is responsible for adding the changes collected in the field into the database using ESRI ArcGIS software. Imagery and GoogleEarth are often used to verify data collected however often this is based on 2007 or older imagery.Linework is based on data captured from a variety of sources, some of which are not known.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Pastoral Stations - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/22ce4795-d3a9-432a-89a7-8fe53391d50d.

The National Groundwater Information System (NGIS) is a spatial database for GIS specialists that contains a range of groundwater information submitted by States and Territories. The System contains more than 850 000 bore locations with associated lithology logs, bore construction logs and hydrostratigraphy logs. 2D and 3D aquifer geometries are also available for some areas. Jurisdictional groundwater management area boundaries are the most recent addition to the System. Bores, bore log and …Show full descriptionThe National Groundwater Information System (NGIS) is a spatial database for GIS specialists that contains a range of groundwater information submitted by States and Territories. The System contains more than 850 000 bore locations with associated lithology logs, bore construction logs and hydrostratigraphy logs. 2D and 3D aquifer geometries are also available for some areas. Jurisdictional groundwater management area boundaries are the most recent addition to the System. Bores, bore log and groundwater management area information from the System can be accessed and visualised using the Australian Groundwater Explorer , without using desktop GIS software. The Explorer also includes data not contained in the National Groundwater Information System, such as groundwater level and salinity time-series data. Landscape characteristic spatial layers, such as geology, land use and river regions can also be displayed to provide context to the groundwater data. Hydrogeologic units within the System have been standardised for national consistency using the National Aquifer Framework. Version 1.3 uses the NGIS Version 2.3 data model and was released in August 2015.

This dataset contains data derived from the GEOSAT satellite radar altimeter wave measuring program. Maps have been produced from processed data, showing attributes including mean significant wave height and the 100 year mean significant wave.

Format: shapefile.

Quality - Scope: Dataset. Absolute External Positional Accuracy: +/- one degree. Non Quantitative accuracy: Attributes are assumed to be correct.

Dataset measures wave height in metres, at 0.25m intervals:

Cover_Name, Item_Name, Description: mswaveheight, GRID-CODE, Numercial code to index the polygons mswaveheight, MSWAVE_HGT_(M), Mean significant wave height ranging 0-4.5m.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Data was projected to Geographics using the WGS84 spheroid and datum to be compatible for viewing through the Australian Coastal Atlas. The data was attributed with the range of wave height in metres, at an interval of 0.25metres.

CSIRO: All 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. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary follows of processing completed by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and The Complete Book of Australian Weather). 2. From the information held in r-BASE a BASE Table was generated incorporating specific fields. 3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point. 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - created from the BASE map and table by 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 such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn). 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid. 8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

This data set is a classification of the Australian continent into 2-dimensional surface regions. A geological region is a relatively large geographical area with a cohesive, albeit in some cases complex, geological assemblage (Bain and Draper 1997, AGSO Bulletin 240); significantly different in overall geology from the adjoining regions, and differs from geological province in that it does not include depth or time dimensions. NOTE: Specialised Geographic Information System (GIS) software is required to view this data.

The eAtlas delivers its mapping products via two Web Mapping Services, a legacy server (from 2008-2011) and a newer primary server (2011+) to which all new content it added. This record describes the legacy WMS.

This service delivers map layers associated with the eAtlas project (http://eatlas.org.au), which contains map layers of environmental research focusing on the Great Barrier Reef. The majority of the layers corresponding to Glenn De'ath's interpolated maps of the GBR developed under the MTSRF program (2008-2010).

This web map service is predominantly maintained for the legacy eAtlas map viewer (http://maps.eatlas.org.au/geoserver/www/map.html). All the these legacy map layers are available through the new eAtlas mapping portal (http://maps.eatlas.org.au), however the legends have not been ported across.

This WMS is implemented using GeoServer version 1.7 software hosted on a server at the Australian Institute of Marine Science.

For ArcMap use the following steps to add this service: 1. "Add Data" then choose GIS Servers from the "Look in" drop down. 2. Click "Add WMS Server" then set the URL to "http://maps.eatlas.org.au/geoserver/wms?"

Note: this service has around 460 layers of which approximately half the layers correspond to Standard Error maps, which are WRONG (please ignore all *Std_Error layers.

This services is operated by the Australian Institute of Marine Science and co-funded by the MTSRF program.