AndreaBozzo/ceres-open-data-index huggingface dataset:
GCCSA-P11b Proficiency in Spoken Eng by Year of Arrival by Sex-Census 2016

GCCSA based data for Proficiency in Spoken English by Year of Arrival in Australia by Sex, in Place of Enumeration Profile (PEP), 2016 Census. Count of persons born overseas in the following categories of proficiency: Speaks English only, Multilingual speaks English well, Multilingual speaks English not well/at all, Multilingual English proficiency not stated, Total multilingual people, Language and English proficiency not stated, and Total persons.  Excludes persons born in 'Australia, (includes External Territories), nfd', 'Norfolk Island' and 'Australian External Territories, nec' and persons who did not state a country of birth. Where year of arrival is stated as 2016, it refers to the period from 1 January 2016 to 9 August 2016. P11 is broken up into 2 sections (P11a – P11b), this section contains 'Persons Speaks other language and speaks English Total Year of arrival 2006-2010’ - 'Persons Total Total’. The data is by GCCSA 2016 boundaries. Periodicity: 5-Yearly. 
Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
For more information visit the data source: http://www.abs.gov.au/census. 

Tags: 2016, arrival, census, english, language, p11b, pep, place of enumeration profile, proficiency, sex
Organization: Government of the Commonwealth of Australia - Australian Bureau of Statistics
Geoscape - Hobart Buildings (Polygon) June 2022

This dataset is the June 2022 release of Geoscape Planning for a single SA2 area (Hobart) with SA2 code (61027).
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 Hobart SA2 by AURIN.

Tags: BUILT ENVIRONMENT AND DESIGN, Building
Organization: Geoscape
DJSB Small Area Labour Market - Unemployment Rate SA2 2010-2018

This datasets presents smooth values for the unemployment rate of Statistical Area Level 2 (SA2) regions for each quarter starting December 2010 up to June 2018. The boundaries used for the dataset follow the 2011 edition of the Australian Statistical Geography Standard (ASGS).
Small Area Labour Markets presents regional estimates of unemployment and the unemployment rate at two small area levels:


For approximately 2,100 Australian Bureau of Statistics’ (ABS) Statistical Area Level 2s (SA2s), on a State/Territory and Metropolitan/Non-metropolitan basis; and


For each of Australia’s 540 Local Government Areas (LGAs).


The estimates in Table 1 and 2 are smoothed using a four-quarter average to minimise the variability inherent in small area estimates. A description of the methodology used to prepare the estimates in this publication is presented in the Explanatory Notes, as well as on page 43 of the PDF Publication.
Please note: 


AURIN has spatially enabled the original data.


Where data values were "-" (no data provided) in the original data they have been set to null.


Caution: Highly disaggregated estimates of unemployment and the unemployment rate at the SA2 and LGA level can display significant variability and should be viewed with caution. Indeed, quarter-to-quarter comparisons may not be indicative of actual movements in the labour market. It is therefore recommended that year-on-year comparisons be used.



Tags: estimates, labour, labour force, market, rate, small area, unemployment
Organization: Government of the Commonwealth of Australia - Department of Jobs and Small Businesses
PHIDU - Age Distribution - 5 Year Age Groups: Aboriginal Females (PHN) 2016

This dataset, released in December 2017, contains the female Aboriginal usual resident population by 5 year age groups: 0-4 years to 65+ years, 2016. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).
There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.
For more information please see the data source notes on the data. 
Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016.
AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Tags: Aboriginal, HEALTH SCIENCES, INDIGENOUS STUDIES, Public health, Torres Strait Islander health, wellbeing
Organization: Torrens University Australia - Public Health Information Development Unit
SA2 Estimates of Personal Income - Own Unincorporated Income 2010-2015

This dataset presents aggregated values of Own Unincorporated Business Income as a category of the estimates of Personal Income for Small Areas ABS release. The data spans over the financial years of 2010-11 to 2014-15 and is aggregated to the 2016 Statistical Area Level 2 (SA2) boundaries.
This release presents regional data on the number of income earners, amounts they receive, and the distribution of income for the 2010-11 to 2014-15 financial years. An improved geocoding process has been introduced for this release. As such, previously released estimates for the 2010-11 and 2012-13 financial year have been superseded. The following personal income categories are provided in this census release:


Employee Income


Own Unincorporated Business Income


Investment Income


Superannuation Income


Other Income (Income not allocatable to any other categories)


Total Income (Sum of previous categories)
These statistics provide insights into the nature of regional economies and the economic well-being of the people who live there. The data has been sourced from the Australian Taxation Office (ATO) and is presented with the updated 2016 editions of the Australian Statistical Geography Standards (ASGS):  Statistical Area Level 2 (SA2); Statistical Area Level 3 (SA3); Statistical Area Level 4 (SA4); Greater Capital City Statistical Area (GCCSA) and Local Government Area (LGA).


For more information on the release please visit the Australian Bureau of Statistics.
Please note:


When interpreting these results, it should be noted that some low income earners, for example those receiving Government pensions and allowances, or those who earned below the tax free threshold, may not be present in the data, as they may not be required to lodge personal tax forms. Other individuals may not lodge a tax return even if required, therefore care should be taken in interpreting the data as well as comparing the data in this publication with other income data produced by the ABS.


To minimise the risk of identifying individuals in aggregate statistics, a confidentialisation process called perturbation has been applied to the data. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released.


Where data is not available or not for publication, the record has been set to a null value.



Tags: ABS, ATO, Australia, aus, economy, finance, income, personal
Organization: Government of the Commonwealth of Australia - Australian Bureau of Statistics
LGA15 Disability Over 18 Years Old - 2010

Modelled estimates of people aged 18 years and over with profound/severe/moderate/mild core activity restriction, 2010 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). Data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/. 
Source: ABS, 2010.

Tags: asgc, core activity restriction, disability , lga profile 2015, phidu, public health information development unit, torrens university australia, tua
Organization: Torrens University Australia - Public Health Information Development Unit
Standard Instrument Local Environmental Plan (LEP) - Height of Building (HOB) 05/08/2016 for NSW

This data identifies the maximum height of a building that may be erected on certain land. Clause 4.3 of the the Local Environment Plan (LEP) deals with building height, but clause 4.3 may also be subject to certain exceptions under other provisions of the Local Environment Plan (LEP). The HOB dataset describes the maximum building height allowed for areas. The height is defined as the vertical distance between ground level (existing) and/or AHD and the highest point of the building, including plant and lift overruns, but excluding communication devices, antennas etc.
 The original GIS data was captured by local councils, DP&amp;I and contractors and provided in accordance with the DP&amp;I's Standard Requirements for GIS LEP data and Standard Technical Requirements for LEP maps. Before the LEP is notified on the NSW Legislation website, the mapping is checked by DP&amp;I and the Parliamentary Council's Office to ensure that it is consistent with these standards. After the LEP is notified, the GIS data is quality controlled using an ISO 9001-Quality-Certified, Quality Management System and incorporated into the Statewide dataset.
 Height of Building relates directly to and adopts the accuracy of its reference dataset, Land and Property Information DCDB. The GIS data may be subject to change as a part of quality control, or if the LEP is amended. Please confirm dates of respective features before use as this dataset has a large temporal extent.

Tags: HOB, environment, height of building, human, instrument, land use, new south wales, nsw, planning
Organization: Government of New South Wales - Department of Planning and Environment
GCCSA-P23b Relationship in Household by Age by Sex-Census 2016

GCCSA based data for Relationship in Household by Age by Sex, in Place of Enumeration Profile (PEP), 2016 Census. Count of persons in occupied private dwellings categorised by the relationship of each person in a family to the family reference person or, where a person is not part of a family, that person's relationship to the household reference person. Excludes persons in 'Other non-classifiable' households.  Includes same sex couples. P23 is broken up into 2 sections (P23a - P23b), this section contains 'Females Unrelated individual living in family household Age 15-24 years' - 'Person Total Total'. The data is by GCCSA 2016 boundaries. Periodicity: 5-Yearly. 
 Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
 For more information visit the data source: http://www.abs.gov.au/census.

Tags: 2016, age, census, composition, household, p23b, pep, place of enumeration profile, relationship, sex
Organization: Government of the Commonwealth of Australia - Australian Bureau of Statistics
SA1-G51a Industry of Employment by Age by Sex-Census 2016

SA1 based data for Industry of Employment by Age by Sex, in General Community Profile (GCP), 2016 Census. Count of employed persons aged 15 years and over. G51 is broken up into four sections (G51a-G51d) this section contains 'Males Agriculture forestry and fishing Age 15-19 years' - 'Males Inadequately described Not stated Total'. The data is by SA1 2016 boundaries. Periodicity: 5-Yearly. 
 Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.
 For more information visit the data source: http://www.abs.gov.au/census.

Tags: 2016, age, census, employment, g51a, gcp, general community profile, industry, sex, work
Organization: Government of the Commonwealth of Australia - Australian Bureau of Statistics
ABS - Regional Population - Summary Statistics (SA2) 2018

This dataset presents the summary preliminary estimates of the resident population by age and sex as at 30 June 2018, this includes population by sex, median age by sex and percentage of the population within a certain age range. The data is aggregated to Statistical Areas Level 2 (SA2), according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).
Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period.
This data is ABS data (catalogue number: 3235.0) available from the Australian Bureau of Statistics.
For more information please visit the Explanatory Notes.


AURIN has spatially enabled the data.


Regions which contain unpublished data have been left blank in the dataset.


Where regions have zero population, the relating ratio and percentage columns have been left blank.



Tags: Demographics, Population
Organization: Government of the Commonwealth of Australia - Australian Bureau of Statistics
Mobility Australia - Queensland Weekly Rail Origin-Destination Flow (SA2) 2019

This dataset estimates human mobility through origin destination (OD) movement flow among the Statistical Area 2 (SA2) regions in Queensland (QLD), connected by public transport (PT) networks. The SA2 regions of Queensland connected by buses, trains, trams and ferries have been used to evaluate OD movement flows. The passenger OD movement data among different stations (or the station-based OD flow) are first estimated using a statistical estimation methodology. The stations-based OD flow data are then translated into region-based OD matrices using the state-of-art method. For more information please see the original metadata file here.
Human mobility data is a key ingredient in various areas and domains of research including epidemiology, policy and administration, criminology, transportation, logistics and supply chains, environmental management and, pollution and contamination. High quality human mobility data provided by telecommunication companies collected from call data records (CDRs) is available at prohibitive cost with restrictive licensing, keeping it out of reach for the majority of research community. On the other hand, there is an abundance of high-quality public data, reporting different aspects of mobility. Examples are the public transport patronage and information about the usage of the Australian road network. These datasets are collected by different organisations and government departments and are presented in various formats. For instance, data may be collected at different spatial (e.g. at state or postcode levels) and temporal scales and be presented in the form of passenger counts or aggregated movement flows. This dataset addresses the general lack of national scale comprehensive human mobility dataset in Australia by transforming available mobility data into a consistent format that is suitable for analysis in a broad range of research areas. Merging the various individual datasets into Australia's first comprehensive, national-scale human mobility data asset drastically improves the quality and coverage of existing datasets.
The Mobility Australia project received investment (https://doi.org/10.47486/DP702) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS).
The original data tables were structured in a matrix-like format. AURIN employed a methodology to merge diverse datasets into a comprehensive one, categorising based on transportation types (e.g., trains, buses, rails, ferries), years (e.g., 2019, 2020, 2021, etc.), and temporal scales (e.g., weekly, monthly, yearly). Subsequently, AURIN spatially enabled the original data by employing the 2021 edition of the Australian Statistical Geography Standard (ASGS). The flow between origin and destination pairs is visually represented using line geometry.

Tags: human mobility, network analyses, transit networks, transport, urban
Organization: CSIRO
AIHW - Cancer Incidence and Mortality Across Regions (CIMAR) - Males Incidence (PHN) 2006-2010

This dataset presents the footprint of male cancer incidence statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, lung, lymphoma, melanoma of the skin, pancreas, prostate and stomach) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).
Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).
For further information about this dataset, please visit:


Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.


Australian Cancer Database 2012 Data Quality Statement.


Please note:


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


Due to changes in geographic classifications over time, long-term trends are not available.


Values assigned to "n.p." in the original data have been removed from the data.


The Australian and jurisdictional totals include people who could not be assigned a PHN. The number of people who could not be assigned a PHN is less than 1% of the total.


The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).


The ACD records all primary cancers except for basal and squamous cell carcinomas of the skin (BCCs and SCCs). These cancers are not notifiable diseases and are not collected by the state and territory cancer registries.


The diseases coded to ICD-10 codes D45-D46, D47.1 and D47.3-D47.5, which cover most of the myelodysplastic and myeloproliferative cancers, were not considered cancer at the time the ICD-10 was first published and were not routinely registered by all Australian cancer registries. The ACD contains all cases of these cancers which were diagnosed from 1982 onwards and which have been registered but the collection is not considered complete until 2003 onwards.


Note that the incidence data presented are for 2006-2010 because 2011 and 2012 data for NSW and ACT were not able to be provided for the 2012 ACD.



Tags: health, population
Organization: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare
Ookla - Network Performance - Fixed (Polygon) Q3 2020

This data set provides fixed broadband network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy.
For more information please see the Ookla Github repository or the Registry of Open Data on AWS.
Ookla licenses data to NGOs and educational institutions to fulfill its mission: to help make the internet better, faster and more accessible for everyone. Ookla hopes to further this mission by distributing the data to make it easier for individuals and organizations to use it for the purposes of bridging the social and economic gaps between those with and without modern Internet access.
AURIN has generated a subset corresponding to the intersection with the extent of the 2016 Greater Capital City Statistical Areas (GCCSA) boundaries from the ABS Australian Statistical Geography Standard (ASGS). It was then reprojected from EPSG 4326 (WGS84) to 4283 (GDA94).
Additional columns have been added corresponding to the matching boundaries of the 2016 ABS Statistical Area Levels 2, 3 and 4, and the 2020 Local Government Areas. These were spatially joined using the centroid of each polygon, and therefore, should only be used as an approximation. Furthermore, grid cells residing outside of these boundaries, such as offshore or over rivers, are assigned a null value in these columns.

Organization: Ookla
PHIDU - Prevalence of Chronic Diseases (PHA) 2011-2012

This dataset, released October 2014, contains for the time period of 2011-2012 the Estimated population, aged 18 years and over with diabetes mellitus; Estimated population aged 18 years and over with high blood cholesterol; Estimated male population with mental and behavioural problems; Estimated female population with mental and behavioural problems; Estimated population with mental and behavioural problems; Estimated population aged 2 years and over with circulatory system diseases; Estimated population with respiratory system diseases; Estimated population with musculoskeletal system diseases. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).
Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.
For more information please see the data source notes on the data. 
Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS;estimates at the LGA and PHN level were derived from the PHA estimates. 
AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Tags: HEALTH SCIENCES, Public health
Organization: Torrens University Australia - Public Health Information Development Unit