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From 20 October 2023, COVID-19 datasets will no longer be updated.
Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx.
Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und
COVID-19 cases by notification date and postcode, local health district, and local government area. The dataset is updated weekly on Fridays.
The data is for confirmed COVID-19 cases only based on location of usual residence, not necessarily where the virus was contracted.
Case counts reported by NSW Health for a particular notification date may vary over time due to ongoing investigations and the outcome of cases under review thus this dataset and any historical data contained within is subject to change on a daily basis.
The underlying dataset was assessed to measure the risk of identifying an individual and the level of sensitivity of the information gained if it was known that an individual was in the dataset. The dataset was then treated to mitigate these risks, including suppressing and aggregating data.
This dataset does not include cases with missing location information.
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Australia recorded 11299954 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Australia reported 20553 Coronavirus Deaths. This dataset includes a chart with historical data for Australia Coronavirus Cases.
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Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.
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Health expenditure occurs where money is spent on health goods and services. It occurs at different levels of government, as well as by non-government entities such as private health insurers and individuals.
In many cases, funds pass through a number of different entities before they are ultimately spent by providers (such as hospitals, general practices and pharmacies) on health goods and services.
The term ‘health expenditure’ in this context relates to all funds given to, or for, providers of health goods and services. It includes the funds provided by the Australian Government to the state and territory governments, as well as the funds provided by the state and territory governments to providers.
This data has been superseded, for more recent data on health expenditure, please the AIHW page on health expenditure.
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From 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx. Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und
The data is for locations associated with confirmed COVID-19 cases that have been classified by NSW Health for action. Refer to the latest COVID-19 news and updates for information on action advice provided by NSW Health.
From Monday 15 November 2021, NSW Health will no longer list case locations that a COVID-19 positive person has attended. This is due to a number of reasons, including high vaccination rates in the community. If you are told to self-isolate by NSW Health or get tested for COVID-19 at any time you must follow this advice.
This dataset provides COVID-19 case locations by date of known outbreak, location, address and action. This data is subject to change as further locations are identified. Locations are removed when 14 days have passed since the last known date that a confirmed case was associated with the location.
The Government has obligations under the Privacy and Personal Information Protection Act 1998 and the Health Records and Information Privacy Act 2002 in relation to the collection, use and disclosure of the personal, including the health information, of individuals. Information about NSW Privacy laws is available here: https://data.nsw.gov.au/understand-key-data-legislation.
The information collected about confirmed case locations does not include any information to directly identify individuals, such as their name, date of birth or address.
Other governments and private sector bodies also have legal obligations in relation to the protection of personal, including health, information. The Government does not authorise any reproduction or visualisation of the data on this website which includes any representation or suggestion in relation to the personal or health information of any individual. The Government does not endorse or control any third party websites including products and services offered by, from or through those websites or their content.
For any further enquiries, please contact us on datansw@customerservice.nsw.gov.au
On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and the latest trend plot. It covers the US (county or state level), China, Canada, Australia (province/state level), and the rest of the world (country/region level, represented by either the country centroids or their capitals). Data sources are WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, the COVID Tracking Project (testing and hospitalizations), state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team, JHU APL and JHU Data Services. This layer is opened to the public and free to share. Contact us.
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Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.
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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. Resource contains an ArcGIS …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT). Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers. This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html. The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system. Purpose For use in Bioregional Assessment land classification analyses Dataset History NVIS Version 4.1 The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC). Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files. The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple). Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete. Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate: Unclassified Forest Other Open Woodlands Mallee Open Woodlands and Sparse Mallee Shublands (Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown. As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources. Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions. Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0). Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset. November 2012 Corrections Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012. Dataset Citation Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.
JHU Coronavirus COVID-19 Global Cases, by country
PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.
This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.
Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
Included Data Sources are:
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**Terms of Use: **
This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.
**U.S. county-level characteristics relevant to COVID-19 **
Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:
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From 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/c…Show full descriptionFrom 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx. Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und As of 10 February 2023, NSW Health will report only positive SARS-CoV-2 test results. Recent changes to the COVID-19 public health orders for COVID-19 means it is no longer necessary for laboratories to provide data on negative PCR test results, in line with other diseases. Positive COVID-19 results, through both PCR tests and notified rapid antigen test results, will continue to be reported. NSW Health uses a wide range of surveillance systems, including hospital data, sewage surveillance, and genomic sequencing, to closely monitor COVID-19 and inform its public health response. COVID-19 tests by date and postcode, local health district, and local government area. The dataset is updated weekly on Fridays. The data is for COVID-19 tests and is based on the Local Health District (LHD) and Local Government Area (LGA) of residence provided by the individual at time of testing. A surge in total number of people tested on a particular day may occur as the test results are updated in batches and new laboratories gain testing capacity. The underlying dataset was assessed to measure the risk of identifying an individual and the level of sensitivity of the information gained if it was known that an individual was in the dataset. The dataset was then treated to mitigate these risks, including suppressing and aggregating data. On 16 September 2021, NSW Health implemented a change in the way testing data is reported. We will discontinue publication of unit record test data file as the data will only be provided as an aggregated file The aggregated data file will only include negative tests. Positive tests (i.e. cases) will not be included. Please note the COVID-19 tests dataset does not include registered positive rapid antigen test (RAT) information.
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Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.
Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.
Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:
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License information was derived automatically
Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format. Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc. Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:
Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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This Australian and New Zealand food category cost dataset was created to inform diet and economic modelling for low and medium socioeconomic households in Australia and New Zealand. The dataset was created according to the INFORMAS protocol, which details the methods to systematically and consistently collect and analyse information on the price of foods, meals and affordability of diets in different countries globally. Food categories were informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods.
Methods The dataset was created according to the INFORMAS protocol [1], which detailed the methods to collect and analyse information systematically and consistently on the price of foods, meals, and affordability of diets in different countries globally.
Cost data were collected from four supermarkets in each country: Australia and New Zealand. In Australia, two (Coles Merrylands and Woolworths Auburn) were located in a low and two (Coles Zetland and Woolworths Burwood) were located in a medium metropolitan socioeconomic area in New South Wales from 7-11th December 2020. In New Zealand, two (Countdown Hamilton Central and Pak ‘n Save Hamilton Lake) were located in a low and two (Countdown Rototuna North and Pak ‘n Save Rosa Birch Park) in a medium socioeconomic area in the North Island, from 16-18th December 2020.
Locations in Australia were selected based on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) [2]. The index ranks areas from most disadvantaged to most advantaged using a scale of 1 to 10. IRSAD quintile 1 was chosen to represent low socio-economic status and quintile 3 for medium SES socio-economic status. Locations in New Zealand were chosen using the 2018 NZ Index of Deprivation and statistical area 2 boundaries [3]. Low socio-economic areas were defined by deciles 8-10 and medium socio-economic areas by deciles 4-6. The supermarket locations were chosen according to accessibility to researchers. Data were collected by five trained researchers with qualifications in nutrition and dietetics and/or nutrition science.
All foods were aggregated into a reduced number of food categories informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods. Nutrient data for each food category can therefore be linked to the Australian Food and Nutrient (AUSNUT) 2011-13 database [4] and NZ Food Composition Database (NZFCDB) [5] using the 8-digit codes provided for Australia and New Zealand, respectively.
Data were collected for three representative foods within each food category, based on criteria used in the INFORMAS protocol: (i) the lowest non-discounted price was chosen from the most commonly available product size, (ii) the produce was available nationally, (iii) fresh produce of poor quality was omitted. One sample was collected per representative food product per store, leading to a total of 12 food price samples for each food category. The exception was for the ‘breakfast cereal, unfortified, sugars ≤15g/100g’ food category in the NZ dataset, which included only four food price samples because only one representative product per supermarket was identified.
Variables in this dataset include: (i) food category and description, (ii) brand and name of representative food, (iii) product size, (iv) cost per product, and (v) 8-digit code to link product to nutrient composition data (AUSNUT and NZFCDB).
References
Vandevijvere, S.; Mackay, S.; Waterlander, W. INFORMAS Protocol: Food Prices Module [Internet]. Available online: https://auckland.figshare.com/articles/journal_contribution/INFORMAS_Protocol_Food_Prices_Module/5627440/1 (accessed on 25 October).
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016 Available online: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/2071.0~2016~Main Features~Socio-Economic Advantage and Disadvantage~123 (accessed on 10 December).
Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. Available online: https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html#2018 (accessed on 10 December)
AUSNUT 2011-2013 food nutrient database. Available online: https://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 15 November).
NZ Food Composition Data. Available online: https://www.foodcomposition.co.nz/ (accessed on 10 December)
Usage Notes The uploaded data includes an Excel spreadsheet where a separate worksheet is provided for the Australian food price database and New Zealand food price database, respectively. All cost data are presented to two decimal points, and the mean and standard deviation of each food category is presented. For some representative foods in NZ, the only NFCDB food code available was for a cooked product, whereas the product is purchased raw and cooked prior to eating, undergoing a change in weight between the raw and cooked versions. In these cases, a conversion factor was used to account for the weight difference between the raw and cooked versions, to ensure that nutrient information (on accessing from the NZFCDB) was accurate. This conversion factor was developed based on the weight differences between the cooked and raw versions, and checked for accuracy by comparing quantities of key nutrients in the cooked vs raw versions of the product.
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This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.
National dataset of Australia's Ramsar Wetlands.
The Convention on Wetlands of International Importance (the Ramsar Convention) was signed in Ramsar, Iran on 2 February 1971. The Ramsar Convention aims to halt the worldwide loss of wetlands and to conserve, through wise use and management, those that remain. The Convention encourages member countries to nominate sites containing representative, rare or unique wetlands, or that are important for conserving biological diversity, to the List of Wetlands of International Importance (Ramsar sites). Australia was one of the first countries to become a Contracting Party to the Convention and designated the world's first Ramsar site, Cobourg Peninsula, in 1974.
This project was initiated by the Wetlands Section of the Australian Government Department of the Environment. Spatial data was sourced from the relevant State and Territory agencies and compiled into a single national coverage.
Credit:
(c) Commonwealth of Australia, Department of Environment with data compiled through cooperative efforts of the States/Territories Government wetland agencies.
April 2015.
Credit:
ACT Government, Environment and Planning Directorate,
Credit:
NSW Office of the Environment and Heritage,
Credit:
NT Department of Land Resource Management,
Credit:
Qld Department of Department of Environment and Heritage Protection,
Credit:
SA Department of Environment, Water and Natural Resources,
Credit:
Tas Department of Primary Industries, Parks, Water and Environment,
Credit:
Vic Department of Environment and Primary Industries,
Credit:
WA Department of Environment and Conservation.
This project was initiated by the Wetlands Section of the Australian Government Department of the Environment. Spatial data was sourced from the relevant State and Territory agencies and compiled into a single national coverage.
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 is a polygon dataset which amalgamates spatial boundaries provided by data custodians to produce a national dataset
Data received from each State or Territory were compiled by ERIN and verified by the Wetlands Section of the Australian Government Department of the Environment.
The boundaries are supplied by the custodian States and Territories, no alterations have been made to boundaries except for datum/projection transformations.
Note: The data in the final coverage contains data captured from different sources including Satellite imagery, orthophoto, digitising 1:100 000 maps and smaller scale. SOURCE field contains general description of input data source.
Data were compiled using ArcGIS software.
Updates
Gwydir - Windella boundary updated (5/12/06) from NSW NPWS map file coordinates (ruled in court case to be the legal boundary). Coordinates were provided in AGD66 and projected to GDA94 using the AGD_1966_To_GDA_1994_4 transform.
December 2006 - updated attribute table to include juresdiction, total_site_area_ha and designation_date fields.
November 2007 - Paroo River Wetlands added as 65th site.
April 2009 - Complete update of NSW boundaries with INTERIM boundaries supplied by NSW DECC on 13/3/09. All NSW boundaries were replaced by the boundaries in the supplied dataset. This is for use internally until final boundaries supplied by DECC. Note that the designation date of Lake Pinaroo was incorrect and was changed by DEWHA with permission from DECC. Detailed information on the changes at each site are included in the data supplied by the custodian.
May 2009 - Complete update of VIC boundaries with data provided by DSE. Only boundary changes are to Western Port and Port Phillip Bay and Bellarine Peninsula which were updated with improved mapping.
April 2010 - NSW boundaries updated with revised boundaries from NSW DECC. Shortlands within Hunter Estuary, and Goddard's Lease in Gwydir were surveyed by consultants and have been updated.
April 2010 - Marine boundaries were updated as a result of boundary review to align with the Marine Parks. Minor changes only (datum errors have been corrected).
June 2010 - Kakadu National Park, Hosnies Spring and Pulu Keeling National Park updated. Kakadu National Park was merged from two sites into one; datum errors in Hosnies Spring have been corrected and Pulu Keeling updated to represent the National Park boundary.
June 2010 - WA boundaries replaced with boundary dataset from WA DEC to ensure consistency. Minor data processing changes only (generally less than 4m).
October 2010 - Kakadu National Park, Hosnies Spring and the Dales updated. An error was found in the CAPAD boundaries for these, so they have been updated with new boundaries supplied by Parks.
November 2010 - All Tasmanian Ramsar Wetland boundaries were updated following the provision of new data from the state. Further changes were made in late November with new data being obtained for Apsley Marshes and Moulting lagoon.
June 2011 - Victorian boundaries updated for all sites with the exception of Western Port and Port Phillip Bay and Bellarine Peninsula which were updated in May 2009.
June 2011 - Coorong boundary updated with data provided by South Australian DEH.
July 2011 - Hattah-Kulkyne updated with new data provided by Vic DSE. The new boundary is based on aerial photography to interpret the high water mark of the lake extent.
July 2011 - Ginini Flats updated with new boundary from ACT Department of Territory and Municipal Services.
September 2013 - Piccaninnie ponds added from data provided by SA Department for Environment and Heritage.
Quality
Scope: Dataset
External accuracy:
Variable due to numerous sources
Non Quantitative accuracy:
100%, unique Refcode being the important item.
Conceptual consistency:
No information provided beyond normal procedures for compiling GIS data.
Completeness omission:
Complete
Department of the Environment (2015) Ramsar Wetlands of Australia. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/d65cc156-944d-4961-bfba-eacfd61db63a.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Species occurrence data from the Atlas of Living Australia (ALA), between 1900 and 2023, were used to create data summaries of native species in terrestrial bioregions (IBRA7). This data collection comprises two datasets: counts of native species in each terrestrial bioregion, and lists of native species with taxonomic information and EPBC status in each terrestrial bioregion.
The counts of native species by bioregion informed an analysis of change in the proportion of species likely to persist over the very long term (S), using the species-area relationship (SAR) with a z-value of 0.25 (S = A^z), and average condition (A) within each bioregion over a time series from the Habitat Condition Assessment System (HCAS) version 3.0. A national-level indicator can then be derived as a species richness weighted average of the bioregional analysis. The counts of EPBC Act threatened species by bioregions is used to inform an interpretation of the condition status of each bioregion. The lists of native species by bioregion provide a basis for an updated analysis that takes into account the fact that species are shared between bioregions and this has an impact on the proportion of species likely to persist over the very long term, when summarised at a national level. These outputs support interpretation of the HCAS and National Connectivity Index (NCI) for the Australian Government’s environment corporate performance measure EN01 (2023-24 Annual Report of the Department of Climate Change, Energy, the Environment and Water). This data collection may also be relevant for other types of analysis at the bioregional level or with different data inputs for the SAR analysis.
The ALA aggregates data from hundreds of different data providers, using different methods, into a single database. Consequently, observations in the ALA display spatial, temporal, and taxonomic bias that ensure care must be taken in their interpretation in downstream analysis. When summarising to the bioregional scale, survey effort is broadly correlated with accessibility, meaning that bioregions near major cities may appear disproportionately biodiverse relative to more remote regions. Lineage: All species occurrence data aggregated by the ALA as of 25 June 2024 were downloaded and filtered using the galah R package to include only records:
• passing the ALA general data quality profile (https://support.ala.org.au/support/solutions/articles/6000240256-getting-started-with-the-data-quality-filters) • occurring in the years 1900-2023 • where the taxon was identified to at least species level • identified within an IBRA terrestrial bioregion
A secondary download without the ALA data quality profile was additionally carried out to retrieve records that would have been initially excluded on the grounds of coordinate uncertainty. Records with a high degree of coordinate uncertainty (e.g. in the case of some highly threatened species) were included if their location had been indexed against an IBRA7 bioregion.
Species were considered native if they were not listed on the Global Register of Introduced and Invasive Species - Australia (version 1.10). Species on the GRIIS were mapped to ALA names, and this was then used as the basis for excluding introduced and invasive species from the downloaded records.
Two summary datasets were created based on these filtered records.
Counts of native species: This dataset comprises aggregated counts of records of native species within each terrestrial bioregion, and the total number of native species recorded in the dataset across bioregions. The summary dataset is published as native_spp_counts_bioregion.csv and contains the following elements:
List of taxa in bioregions: This dataset comprises taxonomic information and EPBC status for native species in each bioregion, and is published as native_taxon_list_bioregions.csv. EPBC status of taxa was inferred by mapping names of taxa on the EPBC list to ALA names, and joining these to the downloaded records. The dataset contains the following elements:
The data_sources.csv file includes information on the source datasets within the Atlas of Living Australia that contributed to this collection.
This dataset contains the latest information on car prices in Australia for the year 2023. It covers various brands, models, types, and features of cars sold in the Australian market. It provides useful insights into the trends and factors influencing the car prices in Australia. The dataset includes information such as brand, year, model, car/suv, title, used/new, transmission, engine, drive type, fuel type, fuel consumption, kilometres, colour (exterior/interior), location, cylinders in engine, body type, doors, seats, and price. The dataset has over 16,000 records of car listings from various online platforms in Australia.
- Brand: Name of the car manufacturer
- Year: Year of manufacture or release
- Model: Name or code of the car model
- Car/Suv: Type of the car (car or suv)
- Title: Title or description of the car
- UsedOrNew: Condition of the car (used or new)
- Transmission: Type of transmission (manual or automatic)
- Engine: Engine capacity or power (in litres or kilowatts)
- DriveType: Type of drive (front-wheel, rear-wheel, or all-wheel)
- FuelType: Type of fuel (petrol, diesel, hybrid, or electric)
- FuelConsumption: Fuel consumption rate (in litres per 100 km)
- Kilometres: Distance travelled by the car (in kilometres)
- ColourExtInt: Colour of the car (exterior and interior)
- Location: Location of the car (city and state)
- CylindersinEngine: Number of cylinders in the engine
- BodyType: Shape or style of the car body (sedan, hatchback, coupe, etc.)
- Doors: Number of doors in the car
- Seats: Number of seats in the car
- Price: Price of the car (in Australian dollars)
- Price prediction: Predict the price of a car based on its features and location using machine learning models.
- Market analysis: Explore the market trends and demand for different types of cars in Australia using descriptive statistics and visualization techniques.
- Feature analysis: Identify the most important features that affect the car prices and how they vary across different brands, models, and locations using correlation and regression analysis.
If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂
Thank you
Attribution 2.5 (CC BY 2.5)https://creativecommons.org/licenses/by/2.5/
License information was derived automatically
Improving market access for Australia’s agricultural, fisheries and forestry exports is vital for supporting a vibrant competitive agricultural industry. To do this, the Department of Agriculture and Water Resources works to remove technical requirements imposed by other countries such as labelling, pest and disease process requirements and residue limits that are inconsistent with Australia’s production systems and, in many cases, with international standards and rights and obligations under international trade rules.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Important: Our technical support team is available to assist you during business hours only. Please keep in mind that we can only address technical difficulties during these hours. When using the product to make decisions, please take this into consideration.
Abstract This spatial product shows consistent ‘near real-time’ bushfire and prescribed burn boundaries for all jurisdictions who have the technical ability or appropriate licence conditions to provide this information. Currency Maintenance of the underlying data is the responsibility of the custodian. Geoscience Australia has automated methods of regularly checking for changes in source data. Once detected the dataset and feeds will be updated as soon as possible. NOTE: The update frequency of the underlying data from the jurisdictions varies and, in most cases, does not line up to this product’s update cycle. Date created: November 2023 Modification frequency: Every 15 Minutes Spatial Extent
West Bounding Longitude: 113° South Bounding Latitude: -44° East Bounding Longitude: 154° North Bounding Latitude: -10°
Source Information The project team initially identified a list of potential source data through jurisdictional websites and the Emergency Management LINK catalogue. These were then confirmed by each jurisdiction through the EMSINA National and EMSINA Developers networks. This Webservice contains authoritative data sourced from:
Australian Capital Territory - Emergency Service Agency (ESA)
New South Wales - Rural Fire Service (RFS)
Queensland - Queensland Fire and Emergency Service (QFES)
South Australia - Country Fire Service (CFS)
Tasmania - Tasmania Fire Service (TFS)
Victoria – Department of Environment, Land, Water and Planning (DELWP)
Western Australia – Department of Fire and Emergency Services (DFES)
The completeness of the data within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Known Limitations:
This dataset does not contain information from the Northern Territory government. This dataset contains a subset of the Queensland bushfire boundary data. The Queensland ‘Operational’ feed that is consumed within this National Database displays a the last six (6) months of incident boundaries. In order to make this dataset best represent a ‘near-real-time’ or current view of operational bushfire boundaries Geoscience Australia has filtered the Queensland data to only incorporate the last two (2) weeks data. Geoscience Australia is aware of duplicate data (features) may appear within this dataset. This duplicate data is commonly represented in the regions around state borders where it is operationally necessary for one jurisdiction to understand cross border situations. Care must be taken when summing the values to obtain a total area burnt. The data within this aggregated National product is a spatial representation of the input data received from the custodian agencies. Therefore, data quality and data completion will vary. If you wish to assess more information about specific jurisdictional data and/or data feature(s) it is strongly recommended that you contact the appropriate custodian.
The accuracy of the data attributes within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices.
Note: Geoscience Australia has, where possible, attempted to align the data to the (as of October 2023) draft National Current Incident Extent Feeds Data Dictionary. However, this has not been possible in all cases. Work to progress this alignment will be undertaken after the publication of this dataset, once this project enters a maintenance period.
Catalog entry: Bushfire Boundaries – Near Real-Time
Lineage Statement
Version 1 and 2 (2019/20):
This dataset was first built by EMSINA, Geoscience Australia, and Esri Australia staff in early January 2020 in response to the Black Summer Bushfires. The product was aimed at providing a nationally consistent dataset of bushfire boundaries. Version 1 was released publicly on 8 January 2020 through Esri AGOL software.
Version 2 of the product was released in mid-February as EMSINA and Geoscience Australia began automating the product. The release of version 2 exhibited a reformatted attributed table to accommodate these new automation scripts.
The product was continuously developed by the three entities above until early May 2020 when both the scripts and data were handed over to the National Bushfire Recovery Agency. The EMSINA Group formally ended their technical involvement with this project on June 30, 2020.
Version 3 (2020/21):
A 2020/21 version of the National Operational Bushfire Boundaries dataset was agreed to by the Australian Government. It continued to extend upon EMSINA’s 2019/20 Version 2 product. This product was owned and managed by the Australian Government Department of Home Affairs, with Geoscience Australia identified as the technical partners responsible for development and delivery.
Work on Version 3 began in August 2020 with delivery of this product occurring on 14 September 2020.
Version 4 (2021/22):
A 2021/22 version of the National Operational Bushfire Boundaries dataset was produced by Geoscience Australia. This product was owned and managed by Geoscience Australia, who provided both development and delivery.
Work on Version 4 began in August 2021 with delivery of this product occurring on 1 September 2021. The dataset was discontinued in May 2022 because of insufficient Government funding.
Version 5 (2023/25):
A 2023/25 version of the National Near-Real-Time Bushfire Boundaries dataset is produced by Geoscience Australia under funding from the National Bushfire Intelligence Capability (NBIC) - CSIRO. NBIC and Geoscience Australia have also partnered with the EMSINA Group to assist with accessing and delivering this dataset. This dataset is the first time where the jurisdictional attributes are aligned to AFAC’s National Bushfire Schema.
Work on Version 5 began in August 2023 and was released in late 2023 under formal access arrangements with the States and Territories.
Data Dictionary
Geoscience Australia has not included attributes added automatically by spatial software processes in the table below.
Attribute Name Description
fire_id ID attached to fire (e.g. incident ID, Event ID, Burn ID).
fire_name Incident name. If available.
fire_type Binary variable to describe whether a fire was a bushfire or prescribed burn.
ignition_date The date of the ignition of a fire event. Date and time are local time zone from the State where the fire is located and stored as a string.
capt_date The date of the incident boundary was captured or updated. Date and time are local time zone from the Jurisdiction where the fire is located and stored as a string.
capt_method Categorical variable to describe the source of data used for defining the spatial extent of the fire.
area_ha Burnt area in Hectares. Currently calculated field so that all areas calculations are done in the same map projection. Jurisdiction supply area in appropriate projection to match state incident reporting system.
perim_km ) Burnt perimeter in Kilometres. Calculated field so that all areas calculations are done in the same map projection. Jurisdiction preference is that supplied perimeter calculations are used for consistency with jurisdictional reporting.
state State custodian of the data. NOTE: Currently some states use and have in their feeds cross border data
agency Agency that is responsible for the incident
date_retrieved The date and time that Geoscience Australia retrieved this data from the jurisdictions, stored as UTC. Please note when viewed in ArcGIS Online, the date is converted from UTC to your local time.
Contact Geoscience Australia, clientservices@ga.gov.au
This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.
Australia's Land Borders is a product within the Foundation Spatial Data Framework (FSDF) suite of datasets. It is endorsed by the ANZLIC - the Spatial Information Council and the Intergovernmental Committee on Surveying and Mapping (ICSM) as a nationally consistent and topologically correct representation of the land borders published by the Australian states and territories.
The purpose of this product is to provide: (i) a building block which enables development of other national datasets; (ii) integration with other geospatial frameworks in support of data analysis; and (iii) visualisation of these borders as cartographic depiction on a map. Although this dataset depicts land borders, it is not nor does it suggests to be a legal definition of these borders. Therefore it cannot and must not be used for those use-cases pertaining to legal context.
This product is constructed by Geoscience Australia (GA), on behalf of the ICSM, from authoritative open data published by the land mapping agencies in their respective Australian state and territory jurisdictions. Construction of a nationally consistent dataset required harmonisation and mediation of data issues at abutting land borders. In order to make informed and consistent determinations, other datasets were used as visual aid in determining which elements of published jurisdictional data to promote into the national product. These datasets include, but are not restricted to: (i) PSMA Australia's commercial products such as the cadastral (property) boundaries (CadLite) and Geocoded National Address File (GNAF); (ii) Esri's World Imagery and Imagery with Labels base maps; and (iii) Geoscience Australia's GEODATA TOPO 250K Series 3. Where practical, Land Borders do not cross cadastral boundaries and are logically consistent with addressing data in GNAF.
It is important to reaffirm that although third-party commercial datasets are used for validation, which is within remit of the licence agreement between PSMA and GA, no commercially licenced data has been promoted into the product. Australian Land Borders are constructed exclusively from published open data originating from state, territory and federal agencies.
This foundation dataset consists of edges (polylines) representing mediated segments of state and/or territory borders, connected at the nodes and terminated at the coastline defined as the Mean High Water Mark (MHWM) tidal boundary. These polylines are attributed to convey information about provenance of the source. It is envisaged that land borders will be topologically interoperable with the future national coastline dataset/s, currently being built through the ICSM coastline capture collaboration program. Topological interoperability will enable closure of land mass polygon, permitting spatial analysis operations such as vector overly, intersect, or raster map algebra. In addition to polylines, the product incorporates a number of well-known survey-monumented corners which have historical and cultural significance associated with the place name.
This foundation dataset is constructed from the best-available data, as published by relevant custodian in state and territory jurisdiction. It should be noted that some custodians - in particular the Northern Territory and New South Wales - have opted out or to rely on data from abutting jurisdiction as an agreed portrayal of their border. Accuracy and precision of land borders as depicted by spatial objects (features) may vary according to custodian specifications, although there is topological coherence across all the objects within this integrated product. The guaranteed minimum nominal scale for all use-cases, applying to complete spatial coverage of this product, is 1:25 000. In some areas the accuracy is much better and maybe approaching cadastre survey specification, however, this is an artefact of data assembly from disparate sources, rather than the product design. As the principle, no data was generalised or spatially degraded in the process of constructing this product.
Some use-cases for this product are: general digital and web map-making applications; a reference dataset to use for cartographic generalisation for a smaller-scale map applications; constraining geometric objects for revision and updates to the Mesh Blocks, the building blocks for the larger regions of the Australian Statistical Geography Standard (ASGS) framework; rapid resolution of cross-border data issues to enable construction and visual display of a common operating picture, etc.
This foundation dataset will be maintained at irregular intervals, for example if a state or territory jurisdiction decides to publish or republish their land borders. If there is a new version of this dataset, past version will be archived and information about the changes will be made available in the change log.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
From 20 October 2023, COVID-19 datasets will no longer be updated.
Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx.
Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und
COVID-19 cases by notification date and postcode, local health district, and local government area. The dataset is updated weekly on Fridays.
The data is for confirmed COVID-19 cases only based on location of usual residence, not necessarily where the virus was contracted.
Case counts reported by NSW Health for a particular notification date may vary over time due to ongoing investigations and the outcome of cases under review thus this dataset and any historical data contained within is subject to change on a daily basis.
The underlying dataset was assessed to measure the risk of identifying an individual and the level of sensitivity of the information gained if it was known that an individual was in the dataset. The dataset was then treated to mitigate these risks, including suppressing and aggregating data.
This dataset does not include cases with missing location information.