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20 Birth and death registration data contributing to preliminary estimates which are higher or lower than usual at the state/territory level are noted below along with any explanations provided by the relevant state or territory registries:
21 The movement of people between and within Australia's states and territories cannot be directly measured and is instead estimated using administrative data. The main source of data used to do this is Medicare change of address information provided to the ABS by the Department of Human Services. The Medicare data used is coded directly to the ASGS and aggregated to the SA2 and LGA levels. Interstate moves are constrained to published estimates of interstate migration. The resulting estimates are known as regional internal migration estimates (RIME).
22 Medicare data is lagged by three months to account for the time between a person moving address and updating their details with Medicare. This lag is consistent with the three month lag assumption used to estimate interstate migration. Data from September of the previous year to September of the reference year is used to reflect movements that have occurred in the financial year.
23 There is recognised undercoverage of men and women of certain age groups in the Medicare data used for RIME. Expansion factors are applied to account for this undercoverage by age and sex, in line with those applied in the estimation of interstate migration at the state/territory level. The expansion factors used in the estimation of interstate migration at the state/territory level are calibrated using migration data from the most recent Census. Estimates of RIME make use of the most up-to-date expansion factors available at the time of release.
24 Medicare theoretically covers the vast majority of Australian residents and non-Australian residents granted temporary Medicare registration. However, some Australian residents do not access the Medicare system, such as temporary migrants or those who have access to other health services. One such population group is defence force personnel. As such, Medicare data is supplemented with information on defence force movements provided by the Department of Defence. This data is considered to be reflective of the time of move, and therefore are not lagged (like the Medicare data). It is converted from postcode to SA2/LGA using a correspondence based on the distribution of persons in Defence occupations (from the previous Census).
25 RIME was previously prepared and released in Migration, Australia (cat. no. 3412.0) for financial years up to 2015-16. Users should exercise a degree of caution when comparing these estimates with the current series of RIME, due to some differences in the methodologies used to prepare each. The old series of RIME (for years up to 2015-16) was prepared independently of and is not directly comparable with ERP, due to the different methods and source data used. The combination of natural increase and net migration (internal and overseas) therefore may not correspond with change in ERP over this time period. The old RIME series was also prepared using quarterly postcode-based Medicare change of address data. This postcode-based data was converted to SA2/LGA, which had implications for accuracy. Further, the use of quarterly data meant that a person could record up to four moves in a financial year. The current series of RIME uses annual change of address data, consistent with the definition of population change over a financial year reference period, and is coded directly to the ASGS, removing the need to convert data from one geographical region to another.
26 Further detail on the method used to prepare postcode-based RIME for years up to 2015-16, including the use of expansion factors and defence force movements, is available in Discussion Paper: Assessment of Methods for Developing Experimental Historical Estimates of Regional Internal Migration (cat. no. 3405.0.55.001).
27 Regional overseas migration estimates (ROME) are prepared by breaking down state/territory level net overseas migration (NOM) arrivals and departures into sub-state areas, using information from the most recent Census. For the purposes of NOM, a person is regarded as a usual resident if they have been (or expect to be) residing in Australia for a period of 12 months or more. This 12-month period does not have to be continuous and is measured over a 16-month period. It includes all people, regardless of nationality, citizenship or legal status, who usually live in Australia, with the exception of foreign diplomatic personnel and their families.
28 Overseas arrivals are estimated based on counts of people who identified in the Census that they were living overseas one year ago. This distribution is used to break down state/territory NOM arrivals each year up until the next Census. To account for any changes to the distribution of overseas arrivals within a state/territory between Censuses (e.g. in high growth areas or inner-city areas with changing numbers of temporary migrants), adjustments may be made based on more up-to-date indicator data sources including counts of 457 visa holders and overseas students.
29 A model is used to distribute state/territory NOM departures within each state/territory. This model is based on a range of information from the Census, mainly the number of people who arrived in an area from overseas in the last year. More weighting is given to areas that have high SEIFA Index of Education and Occupation scores and more than 20% of their total population born overseas. Of all the models evaluated, this model was selected as it best estimated population change over the previous intercensal period. As with overseas arrivals, overseas departures may be adjusted based on additional information sources.
30 LGA estimates of ROME are prepared by converting SA2 ROME, using a total population-weighted correspondence.
31 Preliminary ROME is prepared by breaking down preliminary NOM, which is required six months after the 30 June reference period for the production of quarterly estimates of the population of Australia and the states and territories. At that time, complete traveller histories for the 16 months following a reference quarter can not be produced. To estimate preliminary NOM, a propensity model is applied that estimates a traveller's propensity to contribute to NOM using the observed behaviour of similar travellers from one year earlier. Travellers with similar characteristics are grouped according to specific variables (age, country of citizenship, direction of first and last movement in the reference quarter, initial ERP status, time spent out of Australia, and visa group). It is with final estimates of NOM that the 12/16 month rule can be fully applied. When preliminary estimates of NOM are finalised at the state/territory level, ROME estimates are revised accordingly and released in the next scheduled issue of this product.
ACCURACY OF SUB-STATE POPULATION ESTIMATES
32 An indication of the accuracy of ERP can be gauged by assessing the size and direction of intercensal differences - the difference between preliminary ERP for a Census year (updated from the previous Census) and rebased ERP (based on the current Census). For Australia, the preliminary (unrebased) June 2016 ERP under-estimated the final rebased June 2016 ERP by 0.1% (24,900 people). For the states and territories, the 2016 intercensal differences ranged from -1.4% (Victoria) to +2.0% (Northern Territory).
33 Summary statistics of the absolute values of these errors can be used to assess the accuracy of sub-state population estimates. To give an indication of the quality of SA2-based estimates prepared using the component method, a set of experimental estimates was prepared, updated from 2011 Census-based estimates using the components of population change, and compared with final rebased 2016 estimates. The average absolute value of the intercensal differences for this series of SA2 component-based estimates (excluding areas with less than 1,000 people) was 3.4%. This was slightly lower than the average absolute value of intercensal differences for regression-based estimates over the same period, at 3.5%.
34 Average absolute intercensal differences for the 2016 experimental component-based SA2 estimates generally decreased with increasing population size; that is, SA2s with large populations recorded the smallest percentage differences while small SA2s had the largest percentage differences.
35 In recognition of the inherent inaccuracy involved in estimating population, population figures in commentary text published by the ABS are generally rounded. In the commentary for this product, figures less than 1,000 are rounded to the nearest ten, figures over 1,000 are rounded to the nearest hundred, and figures over 1 million are rounded to the nearest 10,000 or 100,000. While unrounded figures are provided in summary tables and the detailed spreadsheets, accuracy to the last digit should not be assumed. Estimates of change in population are based on unrounded numbers.
36 A procedure is applied to confidentialise sub-state ERP and all components, which are also subsequently constrained so that they add to the relevant state/territory population estimates. As a result of this confidentialisation method, and forced additivity, estimates of under three people should be regarded as synthetic and only exist to ensure additivity to higher levels.
AUSTRALIAN STATISTICAL AREAS
37 This publication contains data presented according to the 2016 edition of the Australian Statistical Geography Standard (ASGS), which refers to boundaries as defined at 1 July 2016. Under this classification, statistical areas are defined as follows:
38 This product also contains data presented according to the 2017 edition of the Australian Statistical Geography Standard (ASGS) - Non ABS Structures:
39 Further information on these statistical areas is contained in:
Australian Statistical Geography Standard: Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016 (cat. no. 1270.0.55.001)
Australian Statistical Geography Standard: Volume 3 - Non ABS Structures, July 2017 (cat. no. 1270.0.55.003)
Australian Statistical Geography Standard: Volume 4 - Significant Urban Areas, Urban Centres and Localities, Sections of State, July 2016 (cat. no. 1270.0.55.004)
Australian Statistical Geography Standard: Volume 5 - Remoteness Structure, July 2016 (cat. no. 1270.0.55.005)
40 Maps for Australian statistical areas are available in the online mapping tool ABS Maps. A complete series of SA2 maps is available in Australian Statistical Geography Standard: Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016 (cat. no. 1270.0.55.001).
RANKING POPULATION CHANGE
41 This product ranks regions according to both 'largest' and 'fastest' growth, identifying areas with significant changes in population. Largest growth is based on the absolute change in population between June 2016 and June 2017, while fastest growth is based on the rate of change in population (expressed as a percentage). Regions with populations of less than 1,000 people at June 2016 have been excluded from the fastest growth rankings. The commentary in this issue ranks population growth based on change between preliminary rebased 2016 and preliminary 2017 population estimates.
42 Due to the inherent imprecision of regional population estimates and variation in population size, rankings should be considered indicative of relative growth between regions, not definitive.
CENTRE OF POPULATION
43 The centre of population is a measure used to describe the spatial distribution of a population. The method used to calculate centres of population in this product is based on the centroid and population of each Statistical Area Level 1 (SA1). To calculate the centre of population for an area, the latitude and longitude coordinates of the centroid of each SA1 in that area are multiplied by the SA1's ERP to obtain weighted latitudes and longitudes for each SA1. These are summed to obtain a weighted latitude and longitude coordinate for the area, then divided by the total population of the area to obtain a single latitude and longitude coordinate. The centres of population included in this issue are based on final 2007 and preliminary 2017 ERP.
44 Due to the inherent imprecision in small area estimates, the centre of population should be considered indicative only of the distribution of population, and cannot be ascribed to an exact location. The use of different geographical level data can result in different centres of population.
CALCULATION OF AREAS AND POPULATION DENSITY
45 The area figures used in this issue are based upon the SA2 level of the 2016 edition of the ASGS. The areas of the SA2s were calculated using ABS standard Geographic Information Systems software from the digital boundaries of this ASGS edition. Higher level spatial unit area figures are aggregations of the relevant SA2 areas. These areas are included in the SA2-based ERP spreadsheet accompanying this release. Area figures are also provided for LGAs based on the 2017 edition of the ASGS and can be found in the LGA-based ERP spreadsheet.
46 The population density of an area as featured in the Excel spreadsheets in this product have been calculated by dividing its estimated resident population by its area in square kilometres. The result is expressed as a number of people per square kilometre.
47 In this release, estimated resident population data has also been published in 1km˛ grid format. The population grid offers a consistently sized spatial unit and gives a refined model of population distribution, particularly for the non-urban areas of Australia. It is also an established, easy to understand and readily comparable international standard which will enable users to make local, national and international comparisons of population density.
48 The population grid initially released in this issue on 24 April 2018 was modelled using preliminary 2017 SA1 ERP. On 31 August 2018, a population grid modelled using revised 2017 SA1 ERP was added. All SA1s with an ERP greater than zero were identified. Within these SA1s all known residential dwelling locations were identified using a variety of sources including the Geocoded National Address File (GNAF).Within each populated SA1 the 2017 SA1 ERP was distributed equally across all the residential dwellings. The average value assigned to each dwelling was then summed within each 1km˛ grid cell across the country.
49 The population grid is provided in three formats:
50 ABS publications draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated; without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act 1905.
51 Other ABS releases that are freely available on the ABS website and may be of interest to users of this product include:
Australian Demographic Statistics (cat. no. 3101.0)
Population by Age and Sex, Regions of Australia (cat. no. 3235.0)
Births, Australia (cat. no. 3301.0)
Deaths, Australia (cat. no. 3302.0)
Migration, Australia (cat. no. 3412.0)
Australian Historical Population Statistics (cat. no. 3105.0.65.001)
Information Paper: Population Concepts (cat. no. 3107.0.55.006)
Population Estimates: Concepts, Sources and Methods (cat. no. 3228.0.55.001)
Quality Assurance of Rebased Population Estimates, 2016 (cat. no. 3250.0.55.001)
Data by Region
ADDITIONAL STATISTICS AVAILABLE
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