This portal contains datasets released openly by Northern Territory Government agencies. To request a dataset that is not already published, please email us with as much detail as possible to assist in locating and providing the data. Open data is data that anyone can access, use and share. The real value of data greatly increases when it is shared, enabling greater benefits to be generated for the community. The Northern Territory Government recognises that the data it collects and creates is a strategic asset that can realise value when it is made available for analysis. Businesses and individuals can use government’s open data to create innovation, for research or simply to be more informed. The NTG Open Data Portal provides non-sensitive government information only. Personal or identifiable information will always be protected and will never be released. When using licensed content under a Creative Commons Licence, you are required to attribute the work in the manner specified in the licence (but not in any way that suggests that the Northern Territory Government endorses you or your use of the work). The Northern Territory Government requires that you use the following form of attribution: Attribution to: Organisation name, Northern Territory, title of dataset, date the content was sourced, dataset URL Example: Department of Treasury and Finance, Northern Territory, NT Population Projections_, Sourced on 22 July 2018, https://treasury.nt.gov.au/ If you experience technical problems, please contact us

The report provides statistics on all cancers and each cancer site or site group for the entire NT population; for males and females; and for Indigenous and non-Indigenous populations. Equivalent summary statistics for the total Australian population are included for comparison. To allow comparison within the NT population and with the wider Australian population, the incidence and mortality rates are age-adjusted because the age distribution of the NT population is much younger than the total Australian population. Statistical modelling analysis is used to investigate trends of cancer incidence and mortality over time.

This dataset presents the projected enrolled population at 22 August 2020 for the Northern Territory (NT) by Legislative Assembly (LA) division areas and 2016 Australian Statistical Geography Standards (ASGS) Statistical Area Level 1 (SA1). Projected elector numbers are prepared by the Australian Bureau of Statistics (ABS) according to assumptions reflecting prevailing trends agreed to by the Northern Territory Electoral Commission. This projection is indicative of future population trends and is not official ABS population statistics.

In the instance where an SA1 is divided between two or more LA divisions, the SA1 index will appear on multiple rows in the file. An individual row in the file will represent elector numbers for a whole or partial SA1 as it relates to any given LA division boundary.

For more information please visit the Northern Territory Government Open Data Portal and read the ABS Projection Assumptions Document.

Please note:

• Members of the Legislative Assembly that reside outside their electoral division are not represented in this dataset.

There are two Northern Territory (NT) (presence and absence point) Weed Datasets describing the recorded weed status at a single point in time. The datasets are compiled from various weed surveys, including the publicly available WeedMate app. Some data is sourced from herbarium records, historical weed records and other types of surveys, including vegetation and land condition monitoring. The NT Weed Datasets are largely a compilation of data derived from GPS locations attributed to describe weed species presence/absence, size, density, population demographics and treatments of those infestations. The datasets are used to assist the Weed Management Branch (WMB), Northern Territory Government (NTG) and other stakeholders to identify existing weed infestations and new incursions. This information is also used for weed management planning and to guide weed control and compliance programs. The datasets are spatially located in Geocentric Datum of Australia 1994 (GDA94) datum as decimal degrees, and the file formats are ESRI Shapefile and file geodatabase. The data custodian is the Director of the Weed Management Branch. It is important to note that weed infestations frequently change, either reducing with successful control or increasing where control programs are not in place. Accordingly, recorded observations are valid only for the date of the record. Further survey may be required to determine the current infestation status.

The Darwin urban centre consists of Darwin City and the associated suburbs from Buffalo Creek, Berrimah and East Arm westwards, and represents approximately 35% of the Northern Territory’s population. The Darwin region falls within the NT Police Darwin Metropolitan Command. The offence data were extracted from the NT Police PROMIS system. Offence rates have been calculated using the latest available estimated resident population data from the Australian Bureau of Statistics (Australian Demographic Statistics, cat. 3101.0, with regional splits based on Regional Population Growth, Australia, 2012, cat. 3218.0). Data requests can be submitted for data not available on the website.

Property Description

Hylak_id Unique lake identifier. Values range from 1 to 1,427,688.

**Lake_name ** Name of lake or reservoir. This field is currently only populated for lakes with an area of at least 500 km2; for large reservoirs where a name was available in the GRanD database; and for smaller lakes where a name was available in the GLWD database.

Country Country that the lake (or reservoir) is located in. International or transboundary lakes are assigned to the country in which its corresponding lake pour point is located and may be arbitrary for pour points that fall on country boundaries.

Continent Continent that the lake (or reservoir) is located in. Geographic continent: Africa, Asia, Europe, North America, South America, or Oceania (Australia and Pacific Islands)

Poly_src The name of datasets that were used in the column. Source of original lake polygon: CanVec; SWBD; MODIS; NHD; ECRINS; GLWD; GRanD; or Other More information on these data sources can be found in Table 1.

Lake_type Indicator for lake type: 1: Lake 2: Reservoir 3: Lake control (i.e. natural lake with regulation structure) Note that the default value for all water bodies is 1, and only those water bodies explicitly identified as other types (mostly based on information from the GRanD database) have other values; hence the type ‘Lake’ also includes all unidentified smaller human-made reservoirs and regulated lakes.

Grand_id ID of the corresponding reservoir in the GRanD database, or value 0 for no corresponding GRanD record. This field can be used to join additional attributes from the GRanD database.

Lake_area Lake surface area (i.e. polygon area), in square kilometers.

Shore_len Length of shoreline (i.e. polygon outline), in kilometers.

Shore_dev Shoreline development, measured as the ratio between shoreline length and the circumference of a circle with the same area. A lake with the shape of a perfect circle has a shoreline development of 1, while higher values indicate increasing shoreline complexity.

Vol_total Total lake or reservoir volume, in million cubic meters (1 mcm = 0.001 km3). For most polygons, this value represents the total lake volume as estimated using the geostatistical modeling approach by Messager et al. (2016). However, where either a reported lake volume (for lakes ≥ 500 km2) or a reported reservoir volume (from GRanD database) existed, the total volume represents this reported value. In cases of regulated lakes, the total volume represents the larger value between reported reservoir and modeled or reported lake volume. Column ‘Vol_src’ provides additional information regarding these distinctions.

Vol_res Reported reservoir volume, or storage volume of added lake regulation, in million cubic meters (1 mcm = 0.001 km3). 0: no reservoir volume

Vol_src 1: ‘Vol_total’ is the reported total lake volume from literature 2: ‘Vol_total’ is the reported total reservoir volume from GRanD or literature 3: ‘Vol_total’ is the estimated total lake volume using the geostatistical modeling approach by Messager et al. (2016)

Depth_avg Average lake depth, in meters. Average lake depth is defined as the ratio between total lake volume (‘Vol_total’) and lake area (‘Lake_area’).

Dis_avg Average long-term discharge flowing through the lake, in cubic meters per second. This value is derived from modeled runoff and discharge estimates provided by the global hydrological model WaterGAP, downscaled to the 15 arc-second resolution of HydroSHEDS (see section 2.2 for more details) and is extracted at the location of the lake pour point. Note that these model estimates contain considerable uncertainty, in particular for very low flows. -9999: no data as lake pour point is not on HydroSHEDS landmask

Res_time Average residence time of the lake water, in days. The average residence time is calculated as the ratio between total lake volume (‘Vol_total’) and average long-term discharge (‘Dis_avg’). Values below 0.1 are rounded up to 0.1 as shorter residence times seem implausible (and likely indicate model errors). -1: cannot be calculated as ‘Dis_avg’ is 0 -9999: no data as lake pour point is not on HydroSHEDS landmask

Elevation Elevation of lake surface, in meters above sea level. This value was primarily derived from the EarthEnv-DEM90 digital elevation model at 90 m pixel resolution by calculating the majority pixel elevation found within the lake boundaries. To remove some artefacts inherent in this DEM for northern latitudes, all lake values that showed negative elevation for the area north of 60°N were substituted with results using the coarser GTOPO30 DEM of USGS at 1 km pixel resolution, which ensures land surfaces ≥0 in this region. Note that due to the remaining uncertainties in the EarthEnv-DEM90 some small negative values occur along the global oce...

The workforce dataset contains monthly workforce sizes from July 2005 to June 2018 in the eight Australian capital cities with estimated stratification by indoor and outdoor workers. It is included in both csv and rda format. It includes variables for:

Year Month GCCSA (Greater Capital City Statistical Area, which is used to define capital cities) Date (using the first day of the month) fulltime: Fulltime workers parttime: Parttime workers n. Overall workers outorin. Estimated indoor or outdoor status

This data are derived from the Australian Bureau of Statistics (ABS) Labour Force, Australia, Detailed, LM1 dataset: LM1 - Labour force status by age, greater capital city and rest of state (ASGS), marital status and sex, February 1978 onwards (pivot table). Occupational data from the 2006, 2011 and 2016 Census of Population and Housing (ABS Census TableBuilder Basic data) were used to stratify this dataset into indoor and outdoor classifications as per the "Indooroutdoor classification.xlsx" file. For the Census data, GCCSA for the place of work was used, not the place of usual residence.

Occupations were defined by the Australian and New Zealand Standard Classification of Occupations (ANZSCO). Each 6-digit ANZSCO occupation (the lowest level classification) was manually cross-matched with their corresponding occupation(s) from the Canadian National Occupation System (NOC). ANZSCO and NOC share a similar structure, because they are both derived from the International Standard Classification of Occupations. NOC occupations listed with an “L3 location” (include main duties with outdoor work for at least part of the working day) were classified as outdoors, including occupations with multiple locations. Occupations without a listing of "L3 location" were classified as indoors (no outdoor work). 6-digit ANZSCO occupations were then aggregated to 4-digit unit groups to match the ABS Census TableBuilder Basic data. These data were further aggregated into indoor and outdoor workers. The 4-digit ANZSCO unit groups’ indoor and outdoor classifications are listed in "Indooroutdoor classification.xlsx."

ANZSCO occupations associated with both indoor and outdoor listings were classified based on the more common listing, with indoors being selected in the event of a tie. The cross-matching of ANZSCO and NOC occupation was checked against two previous cross-matches used in published Australian studies utilising older ANZSCO and NOC versions. One of these cross-matches, the original cross-match, was validated with a strong correlation between ANZSCO and NOC for outdoor work (Smith, Peter M. Comparing Imputed Occupational Exposure Classifications With Self-reported Occupational Hazards Among Australian Workers. 2013).

To stratify the ABS Labour Force detailed data by indoors or outdoors, workers from the ABS Census 2006, 2011 and 2016 data were first classified as indoors or outdoors. To extend the indoor and outdoor classification proportions from 2005 to 2018, the population counts were (1) stratified by workplace GCCSA (standardised to the 2016 metrics), (2) logit-transformed and then interpolated using cubic splines and extrapolated linearly for each month, and (3) back-transformed to the normal population scale. For the 2006 Census, workplace location was reported by Statistical Local Area and then converted to GCCSA. This interpolation method was also used to estimate the 1-monthly worker count for Darwin relative to the rest of Northern Territory (ABS worker 1-monthly counts are reported only for Northern Territory collectively).

ABS data are owned by the Commonwealth Government under a CC BY 4.0 license. The attached datasets are derived and aggregated from ABS data.

Abstract

Northern Australia Infrastructure Facility (NAIF) is a development financier to infrastructure projects in the Northern Territory, Queensland, Western Australia and the Australian Indian Ocean Territories. NAIF’s mission is to be an innovative financing partner in the growth of northern Australia. A key focus of any financing is to drive public benefit, economic, population growth, and Indigenous involvement in northern Australia.

This NAIF dataset contains the limit and extent of Northern Australia as defined in the Northern Australia Infrastructure Facility Act 2016 including the Northern Australia Infrastructure Facility Amendment (Extension and Other Measures) Bill 2021 and the Northern Australia Infrastructure Facility Amendment (Miscellaneous Measures) Bill 2023. This is a maintained dataset and is kept updated to reflect any amendments to the legislation.

The definition in the Northern Australia Infrastructure Facility Act 2016 states that Northern Australia means the area that includes the following:

(a) the Northern Territory;

(b) the areas of Queensland and Western Australia that are North of the Tropic of Capricorn other than the Meekatharra Statistical Area level 2;

(c) the areas South of the Tropic of Capricorn of each Statistical Area level 2 that has an area covered by paragraph (b);

(d) the following Statistical Areas level 2:

(i) Gladstone;

(ii) Gladstone Hinterland;

(iii) Carnarvon;

(da) the Territory of Christmas Island;

(db) the Territory of Cocos (Keeling) Islands;

(e) the Local Government Areas of Meekatharra and Wiluna (despite paragraph (b));

(ea) the Local Government Area of Ngaanyatjarraku;

(f) the territorial sea adjacent to areas covered by paragraphs (a) to (db).

Currency

Date modified: 08 September 2023

Modification frequency: As needed

Data Extent

Spatial Extent

West: 95° South: -28° East: 153° North: -8°

Source InformationGeoscience Australia catalog entry: Northern Australia Infrastructure Facility Act 2016 - Northern Australia Definition with Amendment Bill 2021 and Bill 2023

Based on the definition of Northern Australia area in the Northern Australia Infrastructure Facility Act 2016 along with the consultation of NAIF, Geoscience Australia derived this dataset using:

• The latest GDA2020 digital boundary files of Statistical Area Level 2 - 2021 and the Local Government Areas - 2023 downloaded from Australian Bureau of Statistics.

• The Seas and Submerged Lands Act (SSLA) 1973 web service from Geoscience Australia

Lineage Statement

This dataset is a latest update of the limit and extent of Northern Australia to support the Northern Australia Infrastructure Facility Act 2016.

Point of Contact

Geoscience Australia, ClientServices@ga.gov.au

This dataset presents the Northern Territory (NT) elector numbers as at 15 February 2019 against 2016 Australian Statistical Geography Standard (ASGS) Statistical Area Level 1 (SA1) indexes and NT Legislative Assembly (LA) division names.

In the instance where an SA1 is divided between two or more LA divisions, the SA1 index will appear on multiple rows in the file. An individual row in the file will represent elector numbers for a whole or partial SA1 as it relates to any given LA division boundary.

For more information please visit the Northern Territory Government Open Data Portal.

Please note:

• Members of the Legislative Assembly that reside outside their electoral division are not represented in this dataset.

Flinders and Upper North Local Health Network (FUNLHN) provides a wide range of public acute, residential aged care, community health and mental health services to South Australians based in regions. FUNLHN delivers a comprehensive range of health services throughout 6 public hospitals/health services in regional South Australia, according to population needs, focusing on integrating its service delivery with metropolitan hospitals and other service providers in regional locations. With effect from 1 July 2019, the State Government established 10 Local Health Networks (LHNs), each with its own Governing Board, which commenced operation on 1 July 2019. From this date, six new regional LHNs replaced Country Health SA Local Health Network. To access data published for reporting periods prior to 2019-20, please see https://data.sa.gov.au/data/dataset/country-health-sa-local-health-network

Context

Airports from https://openflights.org

Content

Airport ID Unique OpenFlights identifier for this airport. Name Name of airport. May or may not contain the City name. City Main city served by airport. May be spelled differently from Name. Country Country or territory where airport is located. See countries.dat to cross-reference to ISO 3166-1 codes. IATA 3-letter IATA code. Null if not assigned/unknown. ICAO 4-letter ICAO code. Null if not assigned. Latitude Decimal degrees, usually to six significant digits. Negative is South, positive is North. Longitude Decimal degrees, usually to six significant digits. Negative is West, positive is East. Altitude In feet. Timezone Hours offset from UTC. Fractional hours are expressed as decimals, eg. India is 5.5. DST Daylight savings time. One of E (Europe), A (US/Canada), S (South America), O (Australia), Z (New Zealand), N (None) or U (Unknown). See also: Help: Time Tz database time zone Timezone in "tz" (Olson) format, eg. "America/Los_Angeles". Type Type of the airport. Value "airport" for air terminals, "station" for train stations, "port" for ferry terminals and "unknown" if not known. In airports.csv, only type=airport is included. Source Source of this data. "OurAirports" for data sourced from OurAirports, "Legacy" for old data not matched to OurAirports (mostly DAFIF), "User" for unverified user contributions. In airports.csv, only source=OurAirports is included. The data is UTF-8 (Unicode) encoded.

Note: Rules for daylight savings time change from year to year and from country to country. The current data is an approximation for 2009, built on a country level. Most airports in DST-less regions in countries that generally observe DST (eg. AL, HI in the USA, NT, QL in Australia, parts of Canada) are marked incorrectly.

Acknowledgements

https://openflights.org

Inspiration

I imported this data set to be able to perform analytics on Airport data combined with other large data sets.

This dataset describes habitat suitability for feral pig breeding and persistence in northern Australia during the dry season. It is the result of a spatially-explicit, resource-based and …Show full descriptionThis dataset describes habitat suitability for feral pig breeding and persistence in northern Australia during the dry season. It is the result of a spatially-explicit, resource-based and regional-scale habitat model that integrated expert knowledge on feral pig breeding requirements and home range movements as well as seasonal variability in environmental conditions. The modelled habitat suitability index (HSI) can theoretically range between 0 and 100, with higher values indicating better habitat quality for feral pig breeding. Due to modelling methods and assumptions, HSI values in this dataset effectively range between 11 and 81. They can be broadly classified as follows: HSI ≥ 60 = highly suitable habitat; HSI ≥ 40 = moderately suitable habitat; HSI < 40 = unsuitable habitat. Predicted habitat suitability should not be confused with actual feral pig occurrence. Individuals may be sighted at any time in unsuitable breeding habitat. Conversely, suitable breeding habitat may remain unoccupied. While there is a link between habitat suitability and population density, this may not always be straightforward (i.e. comparable habitat may carry vastly different actual or potential densities depending on the nature and quality of available resources). Feral pig habitat suitability in northern Australia was modelled for two seasonal scenarios. The dry season scenario captured unfavourable conditions during the late dry season, when resources required by feral pigs are generally scarce and scattered across the region. It was developed using spatial proxies averaged across two months (October/November) over five years (2010 to 2014). Seasonal model results were validated against four independent distributional data sets. Underlying model parameters were elicited from experts. This dataset represents results from an expert-averaged model run. The model contained a variable "Disturbance stress" for which no spatial proxies were available. In this dataset, we assumed a uniformly “high” intensity and frequency of control activities, which likely overestimated disturbance and may undervalue habitat suitability in situations where there is actually little management. A detailed description of modelling methods and assumptions is provided in Froese et al. 2017 (https://doi.org/10.1371/journal.pone.0177018).

BackgroundIndigenous populations globally are disproportionately affected by chronic hepatitis B virus (HBV) infection however contemporary sero-prevalence data are often absent. In the Indigenous population of the Northern Territory (NT) of Australia the unique C4 sub-genotype of HBV universally circulates. There are no studies of the sero-prevalence, nor the impact of the vaccination program (which has a serotype mismatch compared to C4), at a population-wide level.MethodsWe examined all available HBV serology results obtained from the three main laboratories serving NT residents between 1991 and 2011. Data were linked with a NT government database to determine Indigenous status and the most recent test results for each individual were extracted as a cross-sectional database including 88,112 unique individuals. The primary aim was to obtain a contemporary estimate of HBsAg positivity for the NT by Indigenous status.ResultsBased on all tests from 2007–2011 (35,633 individuals), hepatitis B surface antigen (HBsAg) positivity was 3·40% (95%CI 3·19–3·61), being higher in Indigenous (6·08%[5·65%-6·53%]) than non-Indigenous (1·56%[1·38%-1·76%]) Australians, p

This land suitability for Sugarcane raster data (in GeoTIFF format) represents areas of potential suitability for this crop and its specific irrigation management systems in the Flinders and Gilbert catchments of North Queensland. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This data is part of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project and is designed to support sustainable regional development in Australia being of importance to Australian Governments and agricultural industries. The project identifies new opportunities for irrigation development in these remote areas by providing improved soil and land evaluation data to identify opportunities and promote detailed investigation. A companion dataset exists, “Confidence of suitability data for the FGARA project”. A link to this dataset can be found in the “related materials” section of this metadata record. Lineage: These suitability raster data for Sugarcane and its individual irrigation management systems have been created from a range of inputs and processing steps. Below is an overview. For more information refer to the CSIRO FGARA published reports and in particular: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO. Broadly, the steps were to: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

Eyre and Far North Local Health Network (EFNLHN) is responsible for the planning and delivery of hospital and health services over 337,626 square kilometers, taking in the Eyre Peninsula, western part of South Australia and north to Coober Pedy. We are one of the largest LHNs by area in Australia providing services to a resident population of just over 40,000 including hospital, community, in-home and residential aged care, early childhood and disability support, and community-based mental health services. The State Government established 10 Local Health Networks (LHNs), each with its own Governing Board, which commenced operation on 1 July 2019. From this date, six new regional LHNs replaced Country Health SA Local Health Network. To access data published for reporting periods prior to 2019-20, please see https://data.sa.gov.au/data/dataset/country-health-sa-local-health-network

This dataset presents the footprint of the percentage of adults who saw three or more health professionals for the same condition in the preceding 12 months. The data spans the years of 2014-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

This dataset presents the footprint of the percentage of adults who were admitted to any hospital in the preceding 12 months. The data spans the years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

This dataset is the June 2022 release of Geoscape Planning for a single SA2 area (Darwin City) with SA2 code (71002). Buildings is a spatial dataset which represents Australia's built environment derived from remotely sensed imagery and aggregated data sources. The Buildings dataset has relationships with the G-NAF, Cadastre, Property and Administrative Boundaries products produced by Geoscape Australia. Users should note that these related Geoscape products are not part of Buildings. For more information regarding Geoscape Buildings, please refer to the Data Product Description and the June 2022 Release Notes. Please note: As per the licence for this data, the coverage area accessed by you can not be greater than a single Level 2 Statistical Area (SA2) as defined by the Australian Bureau of Statistics. If you require additional data beyond a single SA2 for your research, please request a quote from AURIN. Buildings is a digital dataset representing buildings across Australia. Data quality and potential capture timelines will vary across Australia based on two categories, each category has been developed based on a number of factors including the probability of the occurrence of natural events (e.g. flooding), population distribution and industrial/commercial activities. Areas with a population greater than 200, or with significant industrial/commercial activity in a visual assessment have been defined as 'Urban' and all other regions have been defined as 'Rural'. This dataset has been restricted to the Darwin City SA2 by AURIN.

This dataset presents the footprint of the percentage of adults who needed to see a GP but did not in the preceding 12 months. The data spans the years of 2013-2017 and is aggregated to 2015 …Show full descriptionThis dataset presents the footprint of the percentage of adults who needed to see a GP but did not in the preceding 12 months. The data spans the years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014- 15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion. Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population. Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN. The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities). Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings. Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values. NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null. Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

Background: In Australia, community-associated methicillin-resistant Staphylococcus aureus (MRSA) lineage sequence type (ST) 93 has rapidly risen to dominance since being described in the early 1990s. We examined 459 ST93 genome sequences from Australia, New Zealand, Samoa, and Europe to investigate the evolutionary history of ST93, its emergence in Australia and subsequent spread overseas.Results: Comparisons with other S. aureus genomes indicate that ST93 is an early diverging and recombinant lineage, comprising of segments from the ST59/ST121 lineage and from a divergent but currently unsampled Staphylococcal population. However, within extant ST93 strains limited genetic diversity was apparent with the most recent common ancestor dated to 1977 (95% highest posterior density 1973–1981). An epidemic ST93 population arose from a methicillin-susceptible progenitor in remote Northern Australia, which has a proportionally large Indigenous population, with documented overcrowded housing and a high burden of skin infection. Methicillin-resistance was acquired three times in these regions, with a clade harboring a staphylococcal cassette chromosome mec (SCCmec) IVa expanding and spreading to Australia’s east coast by 2000. We observed sporadic and non-sustained introductions of ST93-MRSA-IVa to the United Kingdom. In contrast, in New Zealand, ST93-MRSA-IVa was sustainably transmitted with clonal expansion within the Pacific Islander population, who experience similar disadvantages as Australian Indigenous populations.Conclusion: ST93 has a highly recombinant genome including portions derived from an early diverging S. aureus population. Our findings highlight the need to understand host population factors in the emergence and spread of antimicrobial resistant community pathogens.