This statistic shows the biggest cities in New Zealand in 2022. In 2022, approximately **** million people lived in Auckland, making it the biggest city in New Zealand.

New Zealand NZ: Population in Largest City data was reported at 1,377,309.000 Person in 2017. This records an increase from the previous number of 1,360,422.000 Person for 2016. New Zealand NZ: Population in Largest City data is updated yearly, averaging 851,045.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,377,309.000 Person in 2017 and a record low of 440,164.000 Person in 1960. New Zealand NZ: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Population in largest city in New Zealand was reported at 1692770 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. New Zealand - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

New Zealand NZ: Population in Largest City: as % of Urban Population data was reported at 33.266 % in 2017. This records a decrease from the previous number of 33.580 % for 2016. New Zealand NZ: Population in Largest City: as % of Urban Population data is updated yearly, averaging 30.234 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 34.299 % in 2013 and a record low of 24.415 % in 1961. New Zealand NZ: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

Population in the largest city (% of urban population) in New Zealand was reported at 36.41 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. New Zealand - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Dataset contains counts and measures for individuals 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 the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification.

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The variables for part 2 of the dataset are:

• Individual home ownership for the census usually resident population count aged 15 years and over
• Usual residence 1 year ago indicator
• Usual residence 5 years ago indicator
• Years at usual residence
• Average years at usual residence
• Years since arrival in New Zealand for the overseas-born census usually resident population count
• Average years since arrival in New Zealand for the overseas-born census usually resident population count
• Study participation
• Main means of travel to education, by usual residence address for the census usually resident population who are studying
• Main means of travel to education, by education address for the census usually resident population who are studying
• Highest qualification for the census usually resident population count aged 15 years and over
• Post-school qualification in New Zealand indicator for the census usually resident population count aged 15 years and over
• Highest secondary school qualification for the census usually resident population count aged 15 years and over
• Post-school qualification level of attainment for the census usually resident population count aged 15 years and over
• Sources of personal income (total responses) for the census usually resident population count aged 15 years and over
• Total personal income for the census usually resident population count aged 15 years and over
• Median ($) total personal income for the census usually resident population count aged 15 years and over
• Work and labour force status for the census usually resident population count aged 15 years and over
• Job search methods (total responses) for the unemployed census usually resident population count aged 15 years and over
• Status in employment for the employed census usually resident population count aged 15 years and over
• Unpaid activities (total responses) for the census usually resident population count aged 15 years and over
• Hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Average hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Industry, by usual residence address for the employed census usually resident population count aged 15 years and over
• Industry, by workplace address for the employed census usually resident population count aged 15 years and over
• Occupation, by usual residence address for the employed census usually resident population count aged 15 years and over
• Occupation, by workplace address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by usual residence address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by workplace address for the employed census usually resident population count aged 15 years and over
• Sector of ownership for the employed census usually resident population count aged 15 years and over
• Individual unit data source.

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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

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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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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Study participation time series

In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.

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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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Disability indicator

This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.

Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.

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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

This horizontal bar chart displays rural population (people) by capital city using the aggregation sum in New Zealand. The data is filtered where the date is 2021. The data is about countries per year.

Dataset shows an individual’s statistical area 3 (SA3) of usual residence and the SA3 of their workplace address, for the employed census usually resident population count aged 15 years and over, by main means of travel to work from the 2018 and 2023 Censuses.

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The main means of travel to work categories are:

• Work at home
• Drive a private car, truck, or van
• Drive a company car, truck, or van
• Passenger in a car, truck, van, or company bus
• Public bus
• Train
• Bicycle
• Walk or jog
• Ferry
• Other.

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Main means of travel to work is the usual method which an employed person aged 15 years and over used to travel the longest distance to their place of work.

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Workplace address refers to where someone usually works in their main job, that is the job in which they worked the most hours. For people who work at home, this is the same address as their usual residence address. For people who do not work at home, this could be the address of the business they work for or another address, such as a building site.

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Workplace address is coded to the most detailed geography possible from the available information. This dataset only includes travel to work information for individuals whose workplace address is available at SA3 level. The sum of the counts for each region in this dataset may not equal the total employed census usually resident population count aged 15 years and over for that region. Workplace address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA3 code values:

• Statistical area 3 2023 (generalised)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

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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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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).

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Workplace address time series

Workplace address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Workplace address – 2023 Census: Information by concept has more information.

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Working at home

In the census, working at home captures both remote work, and people whose business is at their home address (e.g. farmers or small business owners operating from their home). The census asks respondents whether they ‘mostly’ work at home or away from home. It does not capture whether someone does both, or how frequently they do one or the other.

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Rows excluded from the dataset

Rows show SA3 of usual residence by SA3 of workplace address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA3-SA3 combinations have commuter flows.

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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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Main means of travel to work quality rating

Main means of travel to work is rated as moderate quality.

Main means of travel to work – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Workplace address quality rating

Workplace address is rated as moderate quality.

Workplace address – 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”.

​

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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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.

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

This horizontal bar chart displays urban population (people) by capital city using the aggregation sum in Australia and New Zealand. The data is filtered where the date is 2021. The data is about countries per year.

NZ：最大城市人口：占城镇人口百分比在12-01-2017达33.266%，相较于12-01-2016的33.580%有所下降。NZ：最大城市人口：占城镇人口百分比数据按年更新，12-01-1960至12-01-2017期间平均值为30.234%，共58份观测结果。该数据的历史最高值出现于12-01-2013，达34.299%，而历史最低值则出现于12-01-1961，为24.415%。CEIC提供的NZ：最大城市人口：占城镇人口百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的新西兰 – 表 NZ.世界银行：人口和城市化进程统计。

This horizontal bar chart displays median age (year) by capital city using the aggregation average, weighted by population in Australia and New Zealand. The data is filtered where the date is 2021. The data is about countries per year.

Background

Intentional self-harm is a common cause of hospital presentations in New Zealand and across the world, and self-poisoning is the most common method of self-harm. Paracetamol (acetaminophen) is frequently used in impulsive intentional overdoses, where ease of access may determine the choice of substance.

Objective

This cross-sectional study aimed to determine how much paracetamol is present and therefore accessible in urban New Zealand households, and sources from where it has been obtained. This information is not currently available through any other means, but could inform New Zealand drug policy on access to paracetamol.

Methods

Random cluster-sampling of households was performed in major urban areas of two cities in New Zealand, and the paracetamol-containing products, quantities, and sources were recorded. Population estimates of proportions of various types of paracetamol products were calculated.

Results

A total of 174 of the 201 study households (86.6%) had at least one paracetamol product. Study households had mostly prescription products (78.2% of total mass), and a median of 24.0 g paracetamol present per household (inter-quartile range 6.0-54.0 g). Prescribed paracetamol was the main source of large stock. Based on the study findings, 53% of New Zealand households had 30 g or more paracetamol present, and 36% had 30 g or more of prescribed paracetamol, specifically.

Conclusions

This study highlights the importance of assessing whether and how much paracetamol is truly needed when prescribing and dispensing it. Convenience of appropriate access to therapeutic paracetamol needs to be balanced with preventing unnecessary accumulation of paracetamol stocks in households and inappropriate access to it. Prescribers and pharmacists need to be aware of the risks of such accumulation and assess the therapeutic needs of their patients. Public initiatives should be rolled out at regular intervals to encourage people to return unused or expired medicines to pharmacies for safe disposal.

Methods The stocks of paracetamol-containing medicines (acetaminophen) held at a single time point in New Zealand households are described in this dataset. These data were collected via a cluster-sampling survey of two cities in New Zealand.

A door-to-door survey study with random, clustered sampling of consenting household members in two cities in New Zealand was designed. A total of 201 households in 40 meshblocks in two Major Urban Areas (MUAs; areas of 100,000 or more residents) of Dunedin and Auckland were sampled. Meshblocks are Statistics NZ’s smallest geographic unit, and roughly correspond to a city block or part of it. Random cluster-sampling of 20 meshblocks in each city was performed by deprivation level, where all eligible MUA meshblocks were stratified by their New Zealand Deprivation Index 2013 (NZDep2013) index scores, which describe the level of area deprivation by taking into account multiple relevant area and household variables. Six meshblocks were randomly selected from each city from NZDep2013 8-10 meshblocks (most deprived), eight from NZDep 4-7, and 6 from NZDep2013 1-2 (least deprived), for a total of 40 meshblocks. This was done to obtain a sample that would be representative of the general New Zealand population by levels of deprivation. Each meshblock was sampled by starting from a random end of the street and then tossing a dice to choose a house to approach, and repeating this until either five households were recruited or there were no more households to sample.

Trained Research Assistants (RAs) knocked on the doors of domiciles in each meshblock to be sampled, chosen by tossing a dice as described. Inclusion criteria: person present and usually residing in a domicile in a meshblock which was sampled, and aged 16 or over. Exclusion criteria: not able to give informed consent (intoxicated, aggressive, otherwise not safe to approach – nobody was excluded for this reason).

Household members aged 16 years and over were eligible to participate, and if consent was obtained, basic demographics were collected about the household (number of people usually residing in the household, their age, sex, ethnicity). Participants were then shown images of paracetamol-containing products (sole and combination), and requested to bring out all paracetamol products of their own, and any that were shared by the household in communal areas of the domicile. Private stock of any other residents of the household who were not present and were therefore unable to consent was not recorded for ethical reasons. If there were no paracetamol products present, that was recorded. If there were paracetamol products present, product type, strength, expiry date, purchase date and means of obtaining (by prescription, pharmacy over-the-counter [OTC], other retailer [i.e. not a pharmacy; e.g. supermarket, petrol station], other, unknown) were recorded.

The data were entered into a main database which is fully de-identified. Meshblock numbers are included in the dataset, but households are only given an identifier derived from the meshblock code. It would not be possible to identify a specific household from the data. Paracetamol product names were cleaned in the dataset (if there were any misspellings), and new variables were calculated to summarise the data (e.g. total household stock of prescribed paracetamol products, etc.).

Urban rural 2023 update

UR 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure UR geographies are relevant and meet criteria before each five-yearly population and dwelling census. UR 2023 contains 13 new rural settlements and 7 new small urban areas. Updates were made to reflect real world change including new subdivisions and motorways, and to improve delineation of urban areas and rural settlements. The Wānaka urban area, whose population has grown to be more than 10,000 based on population estimates, has been reclassified to a medium urban area in the 2023 urban rural indicator.

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In the 2023 classification there are:

• 7 major urban areas
• 13 large urban areas
• 23 medium urban areas
• 152 small urban areas
• 402 rural settlements.

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This dataset is the definitive version of the annually released urban rural (UR) boundaries as at 1 January 2023 as defined by Stats NZ. This version contains 745 UR areas, including 195 urban areas and 402 rural settlements.

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Urban rural (UR) is an output geography that classifies New Zealand into areas that share common urban or rural characteristics and is used to disseminate a broad range of Stats NZ’s social, demographic and economic statistics.

The UR separately identifies urban areas, rural settlements, other rural areas, and water areas. Urban areas and rural settlements are form-based geographies delineated by the inspection of aerial imagery, local government land designations on district plan maps, address registers, property title data, and any other available information. However, because the underlying meshblock pattern is used to define the geographies, boundaries may not align exactly with local government land designations or what can be seen in aerial images. Other rural areas, and bodies of water represent areas not included within an urban area.

Urban areas are built from the statistical area 2 (SA2) geography, while rural and water areas are built from the statistical area 1 (SA1) geography.

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Non-digitised

The following 4 non-digitised UR areas have been aggregated from the 16 non-digitised meshblocks/SA2s.

6901; Oceanic outside region, 6902; Oceanic oil rigs, 6903; Islands outside region, 6904; Ross Dependency outside region.

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UR numbering and naming

Each urban area and rural settlement is a single geographic entity with a name and a numeric code.

Other rural areas, inland water areas, and inlets are defined by territorial authority; oceanic areas are defined by regional council; and each have a name and a numeric code.

Urban rural codes have four digits. North Island locations start with a 1, South Island codes start with a 2, oceanic codes start with a 6 and non-digitised codes start with 69.

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Urban rural indicator (IUR)

The accompanying urban rural indicator (IUR) classifies the urban, rural, and water areas by type. Urban areas are further classified by the size of their estimated resident population:

• major urban area – 100,000 or more residents,
• large urban area – 30,000–99,999 residents,
• medium urban area – 10,000–29,999 residents,
• small urban area – 1,000–9,999 residents.

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This was based on 2018 Census data and 2021 population estimates. Their IUR status (urban area size/rural settlement) may change if the 2023 Census population count moves them up or down a category.

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The indicators, by name, with their codes in brackets, are:

urban area – major urban (11), large urban (12), medium urban (13), small urban (14),

rural area – rural settlement (21), rural other (22),

water – inland water (31), inlet (32), oceanic (33).

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The urban rural indicator complements the urban rural geography and is an attribute in this dataset. Further information on the urban rural indicator is available on the Stats NZ classification and coding tool ARIA.

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

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Generalised version

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

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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.

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

NZ：最大城市人口在12-01-2017达1,377,309.000人，相较于12-01-2016的1,360,422.000人有所增长。NZ：最大城市人口数据按年更新，12-01-1960至12-01-2017期间平均值为851,045.500人，共58份观测结果。该数据的历史最高值出现于12-01-2017，达1,377,309.000人，而历史最低值则出现于12-01-1960，为440,164.000人。CEIC提供的NZ：最大城市人口数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的新西兰 – 表 NZ.世界银行：人口和城市化进程统计。

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New Zealand Traffic Management Market Size 2025-2029

The New Zealand traffic management market size is forecast to increase by USD 209 million at a CAGR of 6.5% between 2024 and 2029.

The market is experiencing significant growth due to several key factors. Urbanization and population growth are major drivers, as cities continue to expand and infrastructure strains under the increased demand for transportation. New product launches by companies are also contributing to market growth, as companies introduce innovative solutions to address the challenges of managing traffic in densely populated areas. However, data privacy and data security concerns are becoming increasingly important in the traffic management industry. To mitigate these challenges, intelligent traffic management systems are being implemented. As cities implement smart traffic management systems, ensuring the security and privacy of sensitive data is crucial to maintaining public trust and compliance with regulations.

What will be the Size of the Market During the Forecast Period?

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The market is a critical aspect of modern infrastructure development, focusing on optimizing the movement of vehicles, pedestrians, and cyclists in urban and suburban areas. This market encompasses various solutions and technologies aimed at enhancing parking facility construction, traffic flow optimization, parking availability, safety, and fuel efficiency. One of the primary drivers in the market is the increasing demand for parking technology. Advanced parking systems, such as parking guidance and reservation, help manage parking facilities more efficiently, reducing congestion and improving accessibility. These systems utilize big data analytics to provide real-time information on parking availability, enabling drivers to make informed decisions and reducing the time spent searching for parking spots.



Another significant trend in the market is the integration of cyclist and pedestrian detection systems. These technologies contribute to promoting sustainable transportation and reducing traffic congestion. Cyclist detection systems ensure the safety of cyclists by recognizing their presence at intersections and adjusting traffic signals accordingly. Pedestrian detection systems, on the other hand, help improve traffic flow and ensure the safety of pedestrians by detecting their presence and prioritizing their movement. Safety is a crucial factor in the market. Safety training programs for traffic enforcement personnel and the implementation of traffic calming measures are essential components of a comprehensive traffic management strategy.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Hardware
  Software
  Services


Type

  Urban traffic management and control
  Adaptive traffic control system
  Journey time management system
  Dynamic traffic management system
  Others


Deployment

  On-premises
  Cloud


Geography

  New Zealand

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period.

The hardware plays a pivotal role in addressing traffic congestion and ensuring optimal connectivity in urban areas such as Auckland, Wellington, and Christchurch. A key component of modern traffic systems is traffic signals and controllers. Traffic signals are essential for managing the movement of vehicles and pedestrians at intersections. Advanced, adaptive traffic signals can dynamically adjust timings based on real-time traffic conditions, thereby reducing congestion during peak hours and improving overall traffic flow. Traffic signal controllers are crucial for coordinating signals at multiple intersections, creating synchronized traffic networks that boost efficiency. The integration of smart mobility solutions, including AI and real-time data analysis, further enhances the capabilities of traffic management systems, addressing safety concerns and ensuring seamless transportation.

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Market Dynamics

Our market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in the adoption of the New Zealand Traffic Management Market?

Urbanization and population growth is the key driver of the market.

The market is experiencing significant growth due to urbanization and population expansion. The population influx puts immense pressure on existing traffi

NZ Suburbs and Localities describes the spatial extent and name of communities in urban areas (suburbs) and rural areas (localities) for navigation and location purposes.

The suburb and locality boundaries cover New Zealand including North Island, South Island, Stewart Island/Rakiura, Chatham Islands, and nearby offshore islands.

Each suburb and locality is assigned a name, major name, Territorial Authority and, if appropriate, additional in use names. A population estimate is provided for each suburb and locality by Stats NZ.

For more information please refer to the NZ Suburbs and Localities Data Dictionary and the LINZ Website

Changes to NZ Suburbs and Localities can be requested by emailing addresses@linz.govt.nz

Change Request Guidance Documents: - Change Request Process - Change Request Principles, Requirements and Rules

APIs and web services

This dataset is available via ArcGIS Online and ArcGIS REST services, as well as our standard APIs. LDS APIs and OGC web services ArcGIS Online map services

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

The main means of travel to education categories are:Study at homeDrive a car, truck, or vanPassenger in a car, truck, or vanBicycleWalk or jogSchool busPublic busTrainFerryOther.Main means of travel to education is the usual method a person used to travel the longest distance to their place of study. Educational institution address is the physical location of the individual’s place of study. Educational institutions include early childhood education, primary school, secondary school, and tertiary education institutions. For individuals who study at home, their educational institution address is the same as their usual residence address.Educational institution address is coded to the most detailed geography possible from the available information. This dataset only includes travel to education information for individuals whose educational institution address is available at SA2 level. The sum of the counts for each region in this dataset may not equal the census usually resident population count who are studying (part time or full time) for that region. Educational institution address – 2023 Census: Information by concept has more information.This dataset can be used in conjunction with the following spatial files by joining on the SA2 code values:Statistical area 2 2023 (generalised)Statistical area 2 2023 (Centroid Inside)FootnotesGeographical 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. 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. 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). Educational institution address time series Educational institution address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Educational institution address – 2023 Census: Information by concept has more information.Rows excluded from the dataset Rows show SA2 of usual residence by SA2 of educational institution address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA2-SA2 combinations have commuter flows. 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 variableThe 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.Main means of travel to education quality ratingMain means of travel to education is rated as moderate quality. Main means of travel to education – 2023 Census: Information by concept has more information, for example, definitions and data quality.Educational institution address quality ratingEducational institution address is rated as moderate quality. Educational institution address – 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.Percentages To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies. Symbol-999 ConfidentialInconsistencies in definitions Please note that there may be differences in definitions between census classifications and those used for other data collections.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in New Zealand. Power Plant emissions from all power plants in New Zealand were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information