Māori population estimates and projections for the Aotea/Great Barrier Local Board Area, Auckland, New Zealand
As at June 2018–2043, number of people
| Year as at 30 June | Projection | Number of people |
|---|---|---|
| 2018 | Low | 200.0 |
| 2018 | High | 200.0 |
| 2023 | High | 240.0 |
| 2028 | High | 270.0 |
| 2033 | High | 310.0 |
| 2038 | High | 360.0 |
| 2043 | High | 410.0 |
| 2023 | Low | 210.0 |
| 2028 | Low | 210.0 |
| 2033 | Low | 210.0 |
| 2038 | Low | 210.0 |
| 2043 | Low | 210.0 |
| 2018 | Medium | 200.0 |
| 2023 | Medium | 220.0 |
| 2028 | Medium | 240.0 |
| 2033 | Medium | 260.0 |
| 2038 | Medium | 280.0 |
| 2043 | Medium | 300.0 |
Definitions
Medium projection: Assuming medium fertility, medium mortality, and medium net migration.
High projection: Assuming high fertility, low mortality, and high net migration.
Low projection: Assuming low fertility, high mortality, and low net migration.
Natural increase: live births minus deaths
Net migration: Long-term arrivals to the area minus long-term departures from the area. This includes migration within New Zealand
Limitations of the data
The projections are not predictions. The projections are designed to meet both short-term and long-term planning needs, but are not designed to be exact forecasts or to project specific annual variation. These projections are based on assumptions made about future fertility, mortality, migration, and inter-ethnic mobility patterns of the population. Although the assumptions are carefully formulated to represent future trends, they are subject to uncertainty. Therefore, the projections and associated probability intervals should be used as guidelines and an indication of the overall trend, rather than as exact forecasts.
This data was produced in 2024 and is due for further revisions/updates in light of the results of the 2023 Census.
Data provided by
Dataset name
Estimates and Projections: Subnational ethnic population projections, characteristics, 2018(base)-2043 (2024 update)
Webpage:
How to find the data
Data is displayed at URL provided. Download "unfiltered data in tabular text (CSV)'
Import & extraction details
File as imported: Estimates and Projections: Subnational ethnic population projections, characteristics, 2018(base)-2043 (2024 update)
From the dataset Estimates and Projections: Subnational ethnic population projections, characteristics, 2018(base)-2043 (2024 update), this data was extracted:
- Rows: 2-57,760
- Column: 15
- Provided: 11,880 data points
This data forms the table Population - Components of projected population change and median age in Auckland by ethnic group and local board area 2018(base)–2043.
Dataset originally released on:
February 2024
Method of collection/Data provider
These projections have as a base the estimated resident population of each area at 30 June 2018. This population was based on the census usually resident population count of each area at 6 March 2018 and adjusted for:
a. net census undercount
b. residents temporarily overseas on census night
c. births, deaths, and net migration between census night (6 March 2018) and 30 June 2018
d. reconciliation with demographic estimates at ages 0–14 years.
The estimated and projected resident population is not directly comparable with the census usually resident population because of these adjustments.
Three alternative projections (designated low, medium, and high) have been produced for each area using different fertility, mortality, and migration assumptions. At the time of release, the medium projection is considered the most suitable for assessing future population change.
The low and high projections allow users to assess the impact on population size and structure resulting from lower growth and higher growth scenarios, respectively. The low projection uses low fertility, high mortality, and low net migration for each area. The high projection uses high fertility, low mortality, and high net migration for each area. The low and high projections are independent of the national population projections as they represent plausible alternative scenarios for each area.