The New Zealand Census of Population and Dwellings is the official count of how many people and dwellings there are in New Zealand. It provides a snapshot of our society at a point in time and helps to tell the story of its social and economic change. The 2023 Census, held on Tuesday 7 March, was the 35th New Zealand Census of Population and Dwellings. The first official census was run in 1851, and since 1877 there has been a census every five years, with only four exceptions.

Seed quality of NPGS Senegal sorghum accessions resistant to grain mold.

The New Zealand Census of Population and Dwellings is the official count of how many people and dwellings there are in New Zealand. It provides a snapshot of our society at a point in time and helps to tell the story of its social and economic change. The 2023 Census, held on Tuesday 7 March, was the 35th New Zealand Census of Population and Dwellings. The first official census was run in 1851, and since 1877 there has been a census every five years, with only four exceptions.

Phenotype analysis of NPGS Senegal sorghum germplasm populations.

IntroductionSclerotinia sclerotiorum is a necrotrophic fungal pathogen causing disease and economic loss on numerous crop plants. This fungus has a broad host range and can infect over 400 plant species, including important oilseed crops such as soybean, canola, and sunflower. S. sclerotiorum isolates vary in aggressiveness of lesion formation on plant tissues. However, the genetic basis for this variation remains to be determined. The aims of this study were to evaluate a diverse collection of S. sclerotiorum isolates collected from numerous hosts and U.S. states for aggressiveness of stem lesion formation on sunflower, to evaluate the population characteristics, and to identify loci associated with isolate aggressiveness using genome-wide association mapping.MethodsA total of 219 S. sclerotiorum isolates were evaluated for stem lesion formation on two sunflower inbred lines and genotyped using genotyping-by-sequencing. DNA markers were used to assess population differentiation across hosts, regions, and climatic conditions and to perform a genome-wide association study of isolate aggressiveness.Results and discussionWe observed a broad range of aggressiveness for lesion formation on sunflower stems, and only a moderate correlation between aggressiveness on the two lines. Population genetic evaluations revealed differentiation between populations from warmer climate regions compared to cooler regions. Finally, a genome-wide association study of isolate aggressiveness identified three loci significantly associated with aggressiveness on sunflower. Functional characterization of candidate genes at these loci will likely improve our understanding of the virulence strategies used by this pathogen to cause disease on a wide array of agriculturally important host plants.

Dataset contains counts and measures for dwellings from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 2.

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The variables included in this dataset are for occupied private dwellings (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated):

• Access to basic amenities (total responses)
• Dwelling dampness
• Dwelling mould
• Dwelling occupancy status for all dwellings for levels 1 and 2
• Dwelling type for occupied dwellings for levels 1 and 2
• Fuel types used to heat dwellings (total responses)
• Main types of heating used (total responses)
• Number of bedrooms
• Average number of bedrooms
• Number of rooms
• Average number of rooms.

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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.

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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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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, 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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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.

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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.

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Symbol

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

In Chapter 16, Reid considers the impact that a return to questions about migration, population movement, and ethnic identity has on the interpretation of Arizona’s prehistory. He begins with an intellectual history of migration research in the Southwest, and offers perspective on the strength of making inferences about migration with archaeological data. He uses the arguments for migration at Grasshopper as an example of building such inferences. Reid then advances into a discussion of population mobility in the Southwest. He uses mobility as a theme to consider ethnic identity, ethnic co-residence, and ethnogenesis in the prehistoric American Southwest. Reid concludes with observations on how the LVAP re-oriented its theoretical perspectives to incorporate many of these contemporary research agendas, particularly the notion of cultural landscapes.

Dataset for the maps accompanying the Housing in Aotearoa New Zealand: 2025 report. This dataset contains counts and measures for:

• average number of private dwellings per square kilometre
• severe housing deprivation
• home ownership rates
• mould and damp.

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Data is available by territorial authority and Auckland local board.

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Average number of private dwellings per square kilometre has data for occupied, unoccupied, and total private dwellings from the 2013, 2018, and 2023 Censuses, including:

• dwelling counts
• percentage change in the count of dwellings
• average number of dwellings per square kilometre.

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Severe housing deprivation has data for the census usually resident population from the 2018 and 2023 Censuses, including:

• estimated prevalence rate of severe housing deprivation (per 10,000 people)
• estimated rate for those; without shelter, in temporary accommodation, sharing someone else’s private dwelling, in uninhabitable housing, for whom it could not be determined whether they were severely housing deprived or not.

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Home ownership rates has data for households in occupied private dwellings from the 2013, 2018, and 2023 Censuses, including:

• counts and percentages for households that owned their home or held it in a family trust, or did not own their home
• percentage change in the count of households that owned their home or held it in a family trust, or did not own their home.

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Mould and damp has data for occupied private dwellings from the 2018 and 2023 Censuses, including:

• counts and percentages for dwellings with or without mould or damp
• percentage change in the count of dwellings with or without mould or damp.

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Map shows the average number of private dwellings per square kilometre for the 2023 Census

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Map shows the estimated prevalence rate of severe housing deprivation (per 10,000 people) for the census usually resident population for the 2023 Census.

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Map shows the percentage of households in occupied private dwellings that owned their home or held it in a family trust for the 2023 Census.

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Map shows the percentage of occupied private dwellings that were damp or mouldy for the 2023 Census.

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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.

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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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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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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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Severe housing deprivation time series

The 2018 estimates of severe housing deprivation have been updated using the 2023 methodology for estimating severe housing deprivation. Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information.

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Severe housing deprivation

Figures in this map and geospatial file exclude Women’s refuge data, as well as estimates for children living in non-private dwellings. Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information.

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Dwelling density

This data shows the average number of private dwellings (occupied and unoccupied) per square kilometre of land for an area. This is a measure of dwelling density.

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About the 2023 Census dataset

For information on the 2023 Census 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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Census usually resident population count concept quality rating

The census usually resident population count is rated as very high quality.

Census usually resident population count – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Quality of severe housing deprivation data

Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information on the data quality of this variable.

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Dwelling occupancy status quality rating

Dwelling occupancy status is rated as high quality.

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

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Dwelling type quality rating

Dwelling type is rated as moderate quality.

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

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Tenure of household quality rating

Tenure of household is rated as moderate quality.

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

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Dwelling dampness indicator quality rating

Dwelling dampness indicator is rated as moderate quality.

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

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Dwelling mould indicator quality rating

Dwelling mould indicator is rated as moderate quality.

Housing quality – 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

Leaf mold of tomato is caused by the biotrophic fungus Cladosporium fulvum which complies with the gene-for-gene system. The disease was first reported in Japan in the 1920s and has since been frequently observed. Initially only race 0 isolates were reported, but since the consecutive introduction of resistance genes Cf-2, Cf-4, Cf-5 and Cf-9 new races have evolved. Here we first determined the virulence spectrum of 133 C. fulvum isolates collected from 22 prefectures in Japan, and subsequently sequenced the avirulence (Avr) genes Avr2, Avr4, Avr4E, Avr5 and Avr9 to determine the molecular basis of overcoming Cf genes. Twelve races of C. fulvum with a different virulence spectrum were identified, of which races 9, 2.9, 4.9, 4.5.9 and 4.9.11 occur only in Japan. The Avr genes in many of these races contain unique mutations not observed in races identified elsewhere in the world including (i) frameshift mutations and (ii) transposon insertions in Avr2, (iii) point mutations in Avr4 and Avr4E, and (iv) deletions of Avr4E, Avr5 and Avr9. New races have developed by selection pressure imposed by consecutive introductions of Cf-2, Cf-4, Cf-5 and Cf-9 genes in commercially grown tomato cultivars. Our study shows that molecular variations to adapt to different Cf genes in an isolated C. fulvum population in Japan are novel but overall follow similar patterns as those observed in populations from other parts of the world. Implications for breeding of more durable C. fulvum resistant varieties are discussed.

Summary of apple fruit harvested from progeny of the GMAL4593 mapping population and used for the evaluation of resistance to blue mold postharvest decay.

Analysis of molecular variance comparing each population among regions and among each clades

Demographic characteristics of retrospective cohort.

Elucidating the genomic architecture of quantitative traits is essential for our understanding of adaptation and for breeding in domesticated organisms. Penicillium roqueforti is the mold used worldwide for the blue cheese maturation, contributing to flavors through proteolytic and lipolytic activities. The two domesticated cheese populations display very little genetic diversity, but are differentiated and carry opposite mating types. We produced haploid F1 progenies from five crosses, using parents belonging to cheese and non-cheese populations. Analyses of high-quality genome assemblies of the parental strains revealed five large translocations, two having occurred via a circular intermediate, one with footprints of Starship giant mobile elements. Offspring genotyping with genotype-by-sequencing (GBS) revealed several genomic regions with segregation distortion, possibly linked to degeneration in cheese lineages. We found transgressions for several traits relevant for cheese making, with offspring having more extreme trait values than parental strains. We identified quantitative trait loci (QTLs) for colony color, lipolysis, proteolysis, extrolite production, including mycotoxins, but not for growth rates. Some genomic regions appeared rich in QTLs for both lipid and protein metabolism, and other regions for the production of multiple extrolites, indicating that QTLs have pleiotropic effects. Some QTLs corresponded to known biosynthetic gene clusters, e.g., for the production of melanin or extrolites. F1 hybrids constitute valuable strains for cheese producers, with new traits and new allelic combinations, and allowed identifying target genomic regions for traits important in cheese making, paving the way for strain improvement. The findings further contribute to our understanding of the genetic mechanisms underlying rapid adaptation, revealing convergent adaptation targeting major gene regulators.