Chart and table of population level and growth rate for the Canberra, Australia metro area from 1950 to 2025.

The projections are based upon actual values obtained in 2015, and estimates obtained for 2016. A full list of all projections, including historical projections, can be found at http://apps.treasury.act.gov.au/demography/projections/act.

These population projections are not intended to present predictions of the demographic future to any degree of reliability or precision. The population projections contained here are the projected population resulting from certain assumptions about future trends in fertility, mortality and migration trends.

Future population trends are influenced by a variety of social, economic and political factors, with significant fluctuation in short-term population growth rates as well as in the underlying social, economic and political influencers. Numerous behavioural assumptions are required to be made for each age cohort and sex. Many of these assumptions will be swamped by the random impacts on the future movements of individuals through births, deaths, and relocation. Neither the authors nor the ACT Government give warranty in relation to these projections, and no liability is accepted by the authors or the Government or any other person who assisted in the preparation of the publication, for errors and omissions, loss or damage suffered as a result of any person acting in reliance thereon.

As of June 2023, in the Australian Capital Territory, about 8.9percent of the population was between 30 and 34 years old. In comparison, just 1.6 percent of the population was over the age of 85.

In this omnibus survey, topics investigated were tenure, duration and location of residence in current and previous dwelling, occurrence of disability in household members, opinions on child care, ideal family size, contraception, family planning services and abortion advice services. Additional topics investigated were ratings and use of Canberra radio, attitudes to proposals for the constitutional development of the Australian Capital Territory, voting intentions in Legislative Assembly and House of Representative elections, opinions on local and Federal political leaders, current and future economic conditions, and newspaper readership. Background variables are age, sex, marital status and birthplace for the workforce data, and income and educational level for the individual data.

In this omnibus survey, topics investigated were dwelling and household characteristics; occurrence of disability in household members; employment history and conditions; life satisfaction; attitudes to and knowledge and personal experience of cancer; opinions on political leaders, the world economy, ACT self-government, and marijuana and its legalisation. Voting intentions in a federal election and the use of alcohol, tobacco and drugs, including patent medicines, were also examined. The drug questions were asked using a randomised response technique. Background variables are age, sex, marital status, birthplace, and residence in Australia for the workforce data, and educational level and income for the individual data.

During an omnibus survey respondents were questioned concerning their use and frequency of use of alcohol, tobacco and other drugs including pain killers, stomach settlers, sedatives and tranquillisers. Marijuana use was investigated using a randomised response technique. Further questions involved the respondents' approval of changes to laws relating to marijuana use and supply. Background variables included age, education, qualifications and income.

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.

This dataset is the June 2022 release of Geoscape Buildings for a single SA2 area (Canberra) with SA2 code (81113). 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 Canberra East SA2 by AURIN.

This table contains shelter indicators (homelessness of city population) for ACT (SA3) and surrounding NSW Councils (LGA) from ABS estimate of homelessness based on the 2011 Census of Population and Housing.

This table contains education indicators (public primary school student teacher ratio, enrollment rate, high educated persons) for ACT (SA3) and surrounding NSW Councils (LGA) from various sources such as ACARA, ACT Department of Education and Population Census.

This table contains health indicators (fertility and death rate, medical practitioners and other key medical specialists) for ACT (SA3) and surrounding NSW Councils (LGA) from ABS National Regional Profile 2012 and 2011 ABS Census of Population and Housing.

NATSEM indicators of child well-being variables of SLAs, excluding SLAs in Brisbane and Canberra, in Australia (2006). These data were provided by NATSEM, University of Canberra, and are based on data from the 2006 Census of Population and Housing supplied by the Australian Bureau of Statistics. The data were developed as part of a project funded by a Discovery Grant from the Australian Research Council (DP664429: Opportunity and Disadvantage: Differences in Wellbeing among Australia's Adults and Children at a Small Area Level.

Total hospitalization episodes and populations (person-years-lived) for males and females, Canberra, 2010–2012.

This data collection contains all currently published nucleotide (DNA/RNA) and protein sequences from Australian Calamoecia canberra. Other information about this group:

The nucleotide (DNA/RNA) and protein sequences have been sourced through the European Nucleotide Archive (ENA) and Universal Protein Resource (UniProt), databases that contains comprehensive sets of nucleotide (DNA/RNA) and protein sequences from all organisms that have been published by the International Research Community.

The identification of species in Calamoecia canberra as Australian dwelling organisms has been achieved by accessing the Australian Plant Census (APC) or Australian Faunal Directory (AFD) through the Atlas of Living Australia.

With 109.9 men per one hundred women in the city, the greater Darwin area in Australia has the highest sex ratio. This is in stark contrast to the demographics of the other major cities in Australia which have more women than men. This is consistent with the fact that more than two thirds of all women between 25 and 64 participating in the workforce.Despite this fact, there is still some disparity between men and women in high level position as women are multiple times more likely to be sexually assaulted while men are much more likely to be victims of murder.The perpetrators of crimes are also much more likely to be men as there are

This data collection contains all currently published nucleotide (DNA/RNA) and protein sequences from the Australian research institution,University of Canberra.The nucleotide (DNA/RNA) and protein sequences have been sourced through the European Nucleotide Archive (ENA) and Universal Protein Resource (UniProt), databases that contains comprehensive sets of nucleotide (DNA/RNA) and protein sequences from all organisms that have been published by the International Research Community.

Abstract

The objective of the ENIGH is to provide a statistical overview of the behavior of household income and expenditure in terms of its amount, origin and distribution; it also offers information on the occupational and sociodemographic characteristics of the members of the household, as well as the characteristics of the housing infrastructure and household equipment.

From 1984, when INEGI began to carry out the survey, until today, new methodologies have been developed, international recommendations have been issued and good practices have been documented for the generation of information on household income and expenditure through surveys.

During this period, additions have been made to the subject matter of the survey, methodological updates and innovations in the processes, to obtain results that reflect reality, taking into account international recommendations and the information requirements of the different users. When the adoption of recommendations and good practices implied a break in the comparability of results, it was preferred to maintain historical comparability.

As of 2008, INEGI decided to publish the results of the ENIGH, whose variables have been constructed and presented in accordance with the recommendations of the UN, specifically, those issued at the 17th International Conference of Labor Statisticians and in the Report of the Canberra Group. This new construction is also the one used in the database of the Socio-Economic Conditions Module of the ENIGH, which is the source of information for the multidimensional measurement of poverty carried out by the National Council for the Evaluation of Social Development Policy (CONEVAL).

Now, in addition to mentioning and analyzing the international recommendations that were put into practice at the ENIGH 2020, this document also recounts the background of the ENIGH, how it emerged and the significant changes it has undergone since then; the objectives of the survey and the recruitment instruments used are mentioned; likewise, as the main axis, there is the description of income and expenditure, their sources, their correlation and implications, this, as the main indicators of household well-being; another chapter lists the main users of the survey information; and finally, the schemes of the topics, categories and variables used in the ENIGH 2020 are presented. Periodicity: Since 1992 it has been carried out biennially (every two years) with the exception of 2005 when an extraordinary survey was carried out.

Target population: It is made up of the households of nationals or foreigners, who usually reside in private homes within the national territory.

Selection Unit: Private home. The dwellings are chosen through a meticulous statistical process that guarantees that the results obtained from only a part of the population (sample) can be generalized to the total.

Sampling Frame: The sampling frame used is the multipurpose framework of the INEGI, it is constituted with the demographic and cartographic information obtained from the Population Census and Housing 2010. Effective sample size: 105,483 households. Observation unit: The home.

Unit of analysis: The household, the dwelling and the members of the household.

Thematic coverage:

Characteristics of the house. Residents and identification of households in the dwelling. Sociodemographic characteristics of the residents of the dwelling. Home equipment, services. Activity condition and occupational characteristics of household members aged 12 and over. Total current income (monetary and non-monetary) of households. Financial and capital perceptions of households and their members. Current monetary expenditure of households. Financial and capital expenditures of households.

The different concepts of the ENIGH are governed by recommendations agreed upon in international conventions, for example:

The resolutions and reports of the 18 International Conferences on Labour Statistics, of the International Labour Organization (ILO).

The final report and recommendations of the Canberra Group, an expert group on "Household Income Statistics".

Manual of Household Surveys. Department of International Economic and Social Affairs, Bureau of Statistics. United Nations, New York, 1987.

They are also articulated with the System of National Accounts and with the Household Surveys carried out by INEGI.

Sample size: At the national level, including the ten-one, there are 93,186 private homes.

Workload: According to the meticulousness in the recording of information in this project, a load of six interviews in private homes per dozen has been defined for each interviewer. The number of interviews may decrease or increase according to several factors: non-response, recovery from non-response, or additional households.

Geographic coverage

National and at the state level - Urban: localities with 2,500 or more inhabitants - Rural: localities with less than 2,500 inhabitants

Analysis unit

The household, the dwelling and the members of the household.

Universe

The survey is aimed at households in the national territory.

Kind of data

Probabilistic household survey

Sampling procedure

The design of the subsample for ENIGH-2020 is characterized by being probabilistic; Consequently, the results obtained from the survey are generalized to the entire population of the study domain, in turn it is two-stage, stratified and by clusters, where the ultimate unit of selection is the dwelling and the unit of observation is the household.

The ENIGH-2020 subsample was selected from the 2012 INEGI master sample, this master sample was designed and selected from the 2012 Master Sampling Framework (Marco Maestro de Muestreo (MMM)) which was made up of housing clusters called Primary Sampling Units (PSUs), built from the cartographic and demographic information obtained from the 2010 Population and Housing Census. The master sample allows the selection of subsamples for all housing surveys carried out by INEGI; Its design is probabilistic, stratified, single-stage and by clusters, since it is in them that the dwellings that make up the subsamples of the different surveys were selected in a second stage. The design of the MMM was built as follows:

Formation of the primary sampling units (PSUs) First, the set of PSUs that will cover the national territory is built. The primary sampling units are made up of groups of dwellings with differentiated characteristics depending on the area to which they belong, as specified below:

In high urban areas The minimum size of a PSU is 80 inhabited dwellings and the maximum is 160. They can be made up of: · A block · The union of two or more contiguous blocks of the same AGEB. · The union of two or more contiguous blocks of different AGEBs in the same locality. · The union of two or more contiguous blocks of different localities, which belong to the same size of locality.

In urban complement The minimum size of a PSU is 160 inhabited homes and the maximum is 300. They can be made up of: · A block · The union of two or more contiguous blocks of the same AGEB · The union of two or more contiguous blocks of different AGEBs in the same locality. · The union of two or more contiguous blocks of different AGEBs and localities, but of the same municipality.

In rural areas The minimum size of a PSU is 160 inhabited homes and the maximum is 300. They can be made up of: · An AGEB. · Part of an AGEB. · The union of two or more adjoining AGEBs in the same municipality. · The union of an AGEB with a part of another adjoining AGEB in the same municipality.

In this way, each PSU was classified into a single geographical and a sociodemographic stratum. As a result, a total of 683 strata were obtained throughout the country.

The sample size for the ENIGH 2020 was calculated at the Entity level with urban and rural scope considering the variables and non-response rates mentioned above.

At the Entity level with an urban area, in the case of the variable Quarterly Average Current Income, there is a variation between 31 554.58 and 91 003.53, with a variance that is the between 609 706 543.70 and 570 110 356 234.59, and a design effect that fluctuates between 1.09 and 4.24.

At the Entity level with a rural environment, the variable Quarterly Average Current Income varies between 14 115.33 and 44 778.03, with a variance that ranges between 156 406 519.01 and 12 108 216 477.28, and a design effect that fluctuates between 1.00 and 9.43. Annex C presents the sample sizes for urban and rural entity areas.

Integrating the sample sizes at the national level, there is a sample size of 87,826 households, which guarantees an error of 4,485% at the national level for the variable quarterly average current income.

Due to the openness to go out into the field to conduct face-to-face interviews in the selected inhabited homes during the time of the COVID-19 pandemic, there were indications of having a greater refusal of households to accept the interviews, it was decided to take precautionary measures to increase the non-response rate to an additional 20%, due to these adjustments, the resulting sample was 105,483 households nationwide.

Sampling deviation

The ENIGH 2020, were raised in a national sample of 105,483 selected homes.

The sample allows information to be obtained at the national level, with a breakdown for localities of 2,500 and more inhabitants and localities of less than 2,500 inhabitants.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six collection instruments will be used for the collection of information in each household, four of which concentrate

This record describes, and links to a working paper produced through the Crawford School of Economics and Government at The Australian National University in Canberra. The paper analyses the …Show full descriptionThis record describes, and links to a working paper produced through the Crawford School of Economics and Government at The Australian National University in Canberra. The paper analyses the economic payoffs from marine reserves using a stochastic optimal control model. The results show that even if the reserve and harvested populations face the same negative shocks, harvesting is optimal, the population is persistent and with no uncertainty over current stock size, a reserve can increase resource rents. Using actual fishery data we demonstrate that the payoffs from a reserve, and also optimum reserve size, increase the larger is the magnitude of the negative shock, the greater its frequency, and the larger its relative impact on the harvested population.

Map showing dust storm activity across Australia based on 356 stations, in 2003. Underpinning data provided by Dustwatch Australia. For further information see: http://www.dustwatch.edu.au/index.php/modelled-wind-erosion/continental-erosion AND McTainsh G, Leys J, O’Loingsigh T, Strong C. Wind erosion and land management in Australia during 1940-1949 and 2000-2009. Report prepared by the Australian Government Department of Sustainability, Environment, Water, Population and Communities on behalf of the State of the Environment 2011 Committee. Canberra: DSEWPaC, 2011.

Map prepared by the Department of Environment and Energy in order to produce Figure LAN24 (2003) in the Land theme of Australia State of the Environment 2016 available at http://www.soe.environment.gov.au

The map service can be viewed at: http://soe.terria.io/#share=s-bhv2xq1BuI2UHL9jr66OWLcGuOa

Downloadable spatial data also available below.

This dataset contains two different types of data that can be employed for transient hygrothermal analysis: MRYs for 30 locations across Australia completed with the climatic data necessary to generate the file, and 10 consecutive years of hourly climate parameters for Brisbane, Cairns, Melbourne, Darwin, Hobart, Sydney, and Canberra cities, representing those locations where most of the population live. These two types of data provide the input for hygrothermal assessment as defined by the ASHRAE 160-2016