Gaming machine profits (GMP), or gambling expenditure, for Class 4 gambling (in pubs and clubs) in New Zealand. Data is provided by district (Territorial Authority) and at quarterly intervals from June 2007. Caveat Notes: Data for Auckland City includes all Boards and Wards since 2015. Data for Auckland City prior to 2015 includes all seven Districts in Auckland (Auckland City, Franklin District, Manuaku City, North Shore City, Papakura District, Rodney District, Waitakere City).

Area of dry or relatively dry land surrounded by water or low wetland

Data Dictionary for island_poly: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-island_poly.html

This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore Islands, at 1:50,000 scale.

Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

Liquefaction Assessment Study Level of Detail:https://www.building.govt.nz/building-code-compliance/geotechnical-educationLevel A - Basic Desktop AssessmentConsiders only the most basic information about geology, groundwater and seismic hazard to assess the potential for liquefaction to occur. This has been completed as a simple ‘desktop study’, based on existing information (geological and topographic maps) and local knowledge.Residual uncertainty: The primary focus is identification of land where there is a high degree of certainty that Liquefaction Damage is Unlikely. For other areas, substantial uncertainty will likely remain regarding the level of risk.Level B - Calibrated desktop assessmentA high-level ‘calibration’ of geological/geomorphic maps. Qualitative assessment of a small number of subsurface investigations provides a better understanding of liquefaction susceptibility and triggering for the mapped deposits and underlying ground profile.Residual uncertainty: Because of the limited amount of subsurface ground information, significant uncertainty remains regarding the level of liquefaction-related risk, how it varies across each mapped area, and the delineation of boundaries between different areasVariabilityThere is considerable uncertainty involved in estimating liquefaction-induced ground damage. These categories are intended to provide a general indication of the damage that might typically be expected. However there can be wide variation in land performance, even where ground conditions appear to be similar, with damage in some cases being much greater or less than inferred from the damage category.Description of uncertaintiesThis assessment has been made at a broad scale across the entire Auckland Council territorial authority area and is intended to approximately describe the typical range of liquefaction across neighborhood-sized areas. It is not intended to precisely describe liquefaction at individual property scale. This information is general in nature, and more detailed site-specific liquefaction assessment will be required for some purposes (e.g. for design of building foundations).Liquefaction CategoryDescription Liquefaction Category is Undetermined A liquefaction vulnerability category has not been assigned at this stage, either because a liquefaction assessment has not been undertaken for this area, or there is not enough information to determine the appropriate category with the required level of confidence. Liquefaction Damage is Unlikely There is a probability of more than 85 percent that liquefaction-induced ground damage will be None to Minor for 500-year shaking. At this stage there is not enough information to distinguish between Very Low and Low. More detailed assessment would be required to assign a more specific liquefaction category. Liquefaction Damage is Possible There is a probability of more than 15 percent that liquefaction-induced ground damage will be Minor to Moderate (or more) for 500-year shaking. At this stage there is not enough information to distinguish between Medium and High. More detailed assessment would be required to assign a more specific liquefaction category. Very Low Liquefaction VulnerabilityThere is a probability of more than 99 percent that liquefaction-induced ground damage will be None to Minor for 500-year shaking. Low Liquefaction Vulnerability There is a probability of more than 85 percent that liquefaction-induced ground damage will be None to Minor for 500-year shaking. Medium Liquefaction Vulnerability There is a probability of more than 50 percent that liquefaction-induced ground damage will be: Minor to Moderate (or less) for 500-year shaking; and None to Minor for 100-year shaking. High Liquefaction Vulnerability There is a probability of more than 50 percent that liquefaction-induced ground damage will be: Moderate to Severe for 500-year shaking; and/or Minor to Moderate (or more) for 100-year shaking. Terms of use:It is important to recognize that these maps are prepared to a city-wide scale and are not intended to provide assessment specific to any one property. Nor does it replace the need for site-specific investigations required for land and building development processes under the Resource Management Act and Building Act.The maps are prepared based on an assessment of natural ground conditions and therefore do not take into account the influence of recent human activities that may influence liquefaction response (i.e. earthworks, ground improvement, foundation design), unless specifically stated within the technical reports. As such, degree of land damage may be less than predicted for a given property where liquefaction risk was addressed during landform or building foundation design.

A wet or moist region with water standing on or just below the surface of the ground, and usually covered by a growth of vegetation Data Dictionary for swamp_pnt: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-swamp_pnt.html This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore Islands, at 1:50,000 scale. Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

A float moored or anchored in water.

Data Dictionary for buoy_pnt: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-buoy_pnt.html

This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore islands, at 1:50,000 scale.

Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

This dataset is the definitive set of annually released statistical area 1 (SA1) boundaries for 2020 as defined by Stats NZ. This version contains 29,895 SA1 categories.

SA1s were introduced as part of the Statistical Standard for Geographic Areas 2018 (SSGA18) which replaced the New Zealand Standard Areas Classification (NZSAC92). SA1 is a new output geography that allows the release of more detailed information about population characteristics than is available at the meshblock level.

Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables. SA1s either define or aggregate to define SA2s, urban rural areas, territorial authorities, and regional councils. Some SA1s that contain apartment blocks, retirement villages, and large non-residential facilities have more than 500 residents.

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

Digital boundary data became freely available on 1 July 2007.

The SA1 classification can also be downloaded from the Stats NZ classification and concordance tool Ariā.

Refer to the current geographies boundaries table for a list of all current geographies and recent updates.

Territorial authorities

Territorial Authority Local Board (TALB) is a derived classification. TALB is derived from the definitive version of the annually released local boards for Auckland and territorial authorities for the rest of New Zealand as at 1 January 2025, as defined by the territorial authorities and/or Local Government Commission and maintained by Stats NZ, clipped to the coastline. This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. This version contains 21 local boards in the Auckland Council and 66 territorial authority boundaries for the rest New Zealand.

Territorial authorities are the second tier of local government in New Zealand, below regional councils. They are defined under schedule 2, part 1 of the Local Government Act 2002 as city councils or district councils. Territorial authorities were established in 1989 when 205 territorial local authorities were replaced by 75 territorial authorities. Territorial boundaries must coincide with meshblock boundaries under schedule 3, clause 17 of the Local Government Act 2002.

Local boards

Local boards share governance with a council’s governing body and each has complementary responsibilities, guaranteed by legislation. Local boards can propose bylaws and they gather community views on local and regional matters. Legislation enacted in 2012 allows for the establishment of local boards in areas of new unitary authorities that are predominantly urban and have a population of more than 400,000. The boundaries of local boards cannot be abolished or changed except through a reorganisation process. If new local boards are created they will be incorporated into this classification.

Local boards are defined at meshblock level. Stats NZ must be consulted if there is a proposed boundary change that does not align with the meshblock pattern. Local boards do not coincide with the statistical area 1 (SA1), statistical area 2 (SA2) geographies, or statistical area 3 (SA3) geographies.

Auckland Council local boards

The Auckland Council was established in November 2010 under the Local Government (Tamaki Makaurau Reorganisation) Act 2009. Seven territorial authorities within the Auckland Region were abolished and replaced by the unitary authority Auckland Council. Local boards fall within the community board classification. Changes were reflected in the 2011 and subsequent community board classifications.

For statistical outputs that use territorial authorities to aggregate and report data Auckland Council is treated as a single geographic entity, whereas previously data was provided for the seven territorial authorities. Presenting data for this single territorial authority hides meaningful patterns and trends for a significant portion of the population. A solution was to create a new classification of territorial authorities that includes the local boards for Auckland.

Numbering

TALB is a flat classification. Each category has a unique five-digit code. The first three digits represent the territorial authority code, ranging from 001 to 076 (with 999 being Area Outside Territorial Authority). The last two digits indicate if the territorial authority is further defined at local board level: 00 indicates the territorial authority is “not further defined”. Auckland retains sequential codes from the community board classification.

The names for the classification are retained from the territorial authority and community board classifications.

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Clipped Version

This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries.

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High-definition version

This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre.

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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Digital data

Digital boundary data became freely available on 1 July 2007.

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Further information

To download geographic classifications in table formats such as CSV please use Ariā

For more information please refer to the Statistical standard for geographic areas 2023.

Contact: geography@stats.govt.nz

Descriptive text; object used to hold text. Data Dictionary for descriptive_text: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-descriptive_text.html This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore Islands, at 1:50,000 scale. Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

The Land Cover vector tile layer is primarily for visualisation. For a downloadable dataset, use Land Cover 2017.Land Cover 2017 is the companion dataset to Impervious Surfaces 2017, and classifies land cover into five surface types: • Grass (open space without trees and shrubs) (Class Name = 1)• Scrub/shrub (rough grass, rushes, low profile vegetation often around wetlands) (Class Name = 4)• Sand/Gravel/Bare Earth (Class Name = 5)• High vegetation (trees and shrubs) (Class Name = 6)• Water (Class Name = 7)Legend:Image processing was conducted by Lynker Analytics using machine learning techniques on Auckland Council’s most recent orthophotography—7.5 cm pixel resolution (2017) covering all the region’s urban settlements and beyond; and 50 cm pixel resolution (2010-2012) covering the region’s remaining, predominantly rural areas—to produce two separate (urban and rural) land cover datasets.A copy of the full background report can be obtained from the Regional Planning team in Auckland Council’s Healthy Waters department.

Dataset contains Māori descent indicator census usually resident population counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the Māori descent indicator counts between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by regional council.

Māori descent indicator categories are:

• Māori descent
• No Māori descent
• Don’t know.

Map shows the percentage change in the Māori descent census usually resident population count between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service.

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Footnotes

Te Whata

Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Māori descent concept quality rating

Māori descent is rated as very high quality.

Māori descent – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Symbol

-998 Not applicable

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Building in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.The model is trained with classified LiDAR that follows the The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 6 BuildingApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

Any formed all weather route suitable for the passage of any vehicle.

Data Dictionary for road_cl: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-road_cl.html

This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore islands, at 1:50,000 scale.

Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

The Impervious Surfaces vector tile layer is primarily for visualisation. For a downloadable dataset, use Impervious Surfaces 2017.Impervious surfaces—mainly artificial structures such as pavements, roads, sidewalks, driveways and parking lots, as well as industrial areas such as airports, ports and logistics and distribution centres that are covered by water-resistant materials such as asphalt, concrete, brick, stone—including rooftops, were mapped along with other surface types to produce the following categories:• Buildings (Class Name = 0)• Other impervious (driveways, carparks, etc) (Class Name = 2)• Roads (Class Name = 3)Legend:Image processing was conducted by Lynker Analytics using machine learning techniques on Auckland Council’s most recent orthophotography—7.5 cm pixel resolution (2017) covering all the region’s urban settlements and beyond; and 50 cm pixel resolution (2010-2012) covering the region’s remaining, predominantly rural areas—to produce two separate (urban and rural) land cover datasets.Land Cover 2017 is the companion dataset to Impervious Surfaces.A copy of the full background report can be obtained from the Regional Planning team in Auckland Council’s Healthy Waters department.

A deliberately formed route of a lesser quality than a road Data Dictionary for track_cl: http://apps.linz.govt.nz/topo-data-dictionary/index.aspx?page=class-track_cl This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore Islands, at 1:50,000 scale. Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

Impervious surfaces—mainly artificial structures such as pavements, roads, sidewalks, driveways and parking lots, as well as industrial areas such as airports, ports and logistics and distribution centres that are covered by water-resistant materials such as asphalt, concrete, brick, stone—including rooftops, were mapped along with other surface types to produce the following categories:• Buildings (Class Name = 0)• Other impervious (driveways, carparks, etc) (Class Name = 2)• Roads (Class Name = 3)Image processing was conducted by Lynker Analytics using machine learning techniques on Auckland Council’s most recent orthophotography—7.5 cm pixel resolution (2017) covering all the region’s urban settlements and beyond; and 50 cm pixel resolution (2010-2012) covering the region’s remaining, predominantly rural areas—to produce two separate (urban and rural) land cover datasets.Land Cover 2017 is the companion dataset to Impervious Surfaces.A copy of the full background report can be obtained from the Regional Planning team in Auckland Council’s Healthy Waters department.

A wet or moist region with water standing on or just below the surface of the ground, and usually covered by a growth of vegetation Data Dictionary for swamp_poly: http://apps.linz.govt.nz/topo-data-dictionary/index.aspx?page=class-swamp_poly This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore Islands, at 1:50,000 scale. Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

A natural, flowing body of water emptying into an ocean, lake or other body of water and usually fed along it's course by converging tributaries.

Data Dictionary for river_cl: http://apps.linz.govt.nz/topo-data-dictionary/index.aspx?page=class-river_cl

This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore Islands, at 1:50,000 scale.

Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

An imaginary line that connects points of equal height value eg the elevation of the land surface above or below a vertical datum, in this case of LINZ topographic mapping, this is Mean Sea Level.

Data Dictionary for contour: http://apps.linz.govt.nz/topo-data-dictionary/index.aspx?page=class-contour

This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore Islands, at 1:50,000 scale.

Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

Dataset contains ethnic group census usually resident population counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the ethnic group population count between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by territorial authority and Auckland local board.

The ethnic groups are:

• European
• Māori
• Pacific peoples
• Asian
• Middle Eastern/Latin American/African
• Other ethnicity.

Map shows percentage change in the census usually resident population count for ethnic groups between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

​

Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

​

Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

​

About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

​

Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

​

Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

​

Ethnicity concept quality rating

Ethnicity is rated as high quality.

Ethnicity – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

​

Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

​

Symbol

-998 Not applicable

​

Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

The HLA genes are regulated by at least 10 AiD associated SNPs. Functional module containing HLA genes is highly enriched for immune system related pathways and biological processes. The shared central genes in the module play crucial roles in the biological processes linked to immune system. Here, we provided the details of proteins, GO and KEGG pathways enrichment results of the HLA module. We have also provided the SNPs targetting shared central HLA genes.