This statistic shows the biggest cities in New Zealand in 2022. In 2022, approximately 1.44 million people lived in Auckland, making it the biggest city in New Zealand.

This statistic depicts the distribution of the major cities to the national GDP in New Zealand in 2015. According to the source, in this year, Auckland contributed with 37 percent to the national GDP in New Zealand.

The Population data table is part of NZ Suburbs and Localities Dataset. This table contains the population estimate for each suburb and locality, provided by StatsNZ.

NZ Suburbs and Localities is an easy to use layer generated from the normalised NZ Suburbs and Localities Dataset. It 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 Guidance documents:

Data Dictionary Change Request Process Change Request Principles, Requirements and Rules Changes to NZ Suburbs and Localities can be requested by emailing addresses@linz.govt.nz

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the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated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

In 2023, the share of urban population in New Zealand remained nearly unchanged at around 86.98 percent. Nevertheless, 2023 still represents a peak in the share in New Zealand with 86.98 percent. A population may be defined as urban depending on the size (population or area) or population density of the village, town, or city. The urbanization rate then refers to the share of the total population who live in an urban setting. International comparisons may be inconsistent due to differing parameters for what constitutes an urban center.Find more key insights for the share of urban population in countries like Micronesia and Tonga.

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

This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 2,395 SA2s (2,379 digitised and 16 with empty or null geometries (non-digitised)).

SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations.

The SA2 should:

form a contiguous cluster of one or more SA1s,

excluding exceptions below, allow the release of multivariate statistics with minimal data suppression,

capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area,

be socially homogeneous and capture a community of interest. It may have, for example:

• a shared road network,
• shared community facilities,
• shared historical or social links, or
• socio-economic similarity,

​

form a nested hierarchy with statistical output geographies and administrative boundaries. It must:

• be built from SA1s,
• either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils.

SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents.

In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area.

SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns.

In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area.

To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2.

Zero or nominal population SA2s

To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include:

• SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara.
• SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas
• SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council.
• SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name):

400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency.

SA2 numbering and naming

Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City).

SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change.

​

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.

​

Macrons

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

​

Digital data

Digital boundary data became freely available on 1 July 2007.

​

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

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

In 2024, the main center in New Zealand with the highest number of sunshine hours was Tauranga, with 2,734 hours. The average temperature for Tauranga was 15.6 degrees Celsius in that year.

Malaysia Tourist Arrival: Sightseeing In Cities: New Zealand data was reported at 92.200 % in 2015. This records an increase from the previous number of 82.600 % for 2014. Malaysia Tourist Arrival: Sightseeing In Cities: New Zealand data is updated yearly, averaging 87.000 % from Dec 2001 (Median) to 2015, with 15 observations. The data reached an all-time high of 95.600 % in 2013 and a record low of 45.100 % in 2003. Malaysia Tourist Arrival: Sightseeing In Cities: New Zealand data remains active status in CEIC and is reported by Tourism Malaysia. The data is categorized under Global Database’s Malaysia – Table MY.Q009: Tourist Arrivals By Major Activities Engaged.

The price of residential property in New Zealand was the highest in the Auckland region in December 2024, with an average sale price of around one million New Zealand dollars. The most populated city in the country, Auckland, has consistently reported higher house prices compared to most other regions. Buying property in New Zealand, particularly in its major cities, is expensive. The nation has one of the highest house-price-to-income ratios in the world. Auckland residential market The residential housing market in Auckland is competitive. Prices have been slowly decreasing; the Auckland region experienced an annual decrease in the average residential house price in December 2024 compared to the same month in the previous year. The price of residential property in Auckland was the highest in the North Shore City district, with an average sale price of around 1.21 million New Zealand dollars. Home financing Due to the rising cost of real estate, an increasing number of New Zealanders who want to own their own property are taking on mortgages. Most residential mortgage lending in New Zealand went to owner-occupier borrowers, followed by first home buyers. In addition to mortgage lending, previously under the KiwiSaver HomeStart initiative, first-home buyers in New Zealand were able to apply to withdraw all or part of their KiwiSaver retirement savings to assist with purchasing a first home. Nonetheless, the scheme was discontinued in May 2024. Furthermore, even with a large initial deposit, it may take decades for many borrowers to pay off their mortgage.

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。

This dataset is the definitive set of statistical area 2 (SA2) boundaries for 2022 as defined by Stats NZ (the custodian). This version contains 2,260 SA2 features.

SA2s were introduced as part of the Statistical Standard for Geographic Areas 2018 (SSGA2018) which replaced the New Zealand Standard Areas Classification (NZSAC1992). The SA2 geography replaces the (NZSAC1992) area unit geography.

SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations.

SA2s are built from SA1s and either define or aggregate to define urban rural areas, territorial authorities, and regional councils. SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents. In rural areas, many SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area.

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

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.

In 2019, Auckland was the region which had the highest value of building work in New Zealand, in which there was over 10.4 billion New Zealand dollars worth of building work. Wellington, New Zealand's capital, had approximately only two billion New Zealand dollars worth of building work in comparison. Although Wellington is New Zealand's capital city, Auckland is the most populous city of New Zealand.

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

Statistical Area 2 2023 update

SA2 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure SA2s are relevant and meet criteria before each five-yearly population and dwelling census. SA2 2023 contains 135 new SA2s. Updates were made to reflect real world change ofpopulation and dwelling growthmainly in urban areas, and to make some improvements to their delineation of communities of interest.

​

Description

This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2023 as defined by Stats NZ (the custodian), 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 clipped version contains 2,311 SA2 areas.

SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations.

​

The SA2 should:

form a contiguous cluster of one or more SA1s,

excluding exceptions below, allow the release of multivariate statistics with minimal data suppression,

capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area,

be socially homogeneous and capture a community of interest. It may have, for example:

· a shared road network,

· shared community facilities,

· shared historical or social links, or

· socio-economic similarity,

form a nested hierarchy with statistical output geographies and administrative boundaries. It must:

· be built from SA1s,

· either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils.

SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents.

In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area.

SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns.

In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area.

To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2.

​

Zero or nominal population SA2s

To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include:

· SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara.

· SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas

· SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council.

· SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name):

400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency.

​

SA2 numbering and naming

Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City).

SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change.

​

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

​

Macrons

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

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

The smart city platforms market analysis report by Future Market Insights reveals that global sales of the smart city platforms market in 2022 were held at US$ 157.6 billion. The projected market growth from 2023 to 2033 is expected to be 11%.

Attributes	Details
Global Smart City Platforms Market Size (2023)	US$ 175 billion
Global Smart City Platforms Market Size (2033)	US$ 496.9 billion
Global Smart City Platforms Market CAGR (2023 to 2033)	11%

Scope of Report

Report Attributes	Details
Growth Rate	CAGR of 11% from 2023 to 2033
Base Year for Estimation	2023
Historical Data	2018 to 2022
Forecast Period	2023 to 2033
Global Smart City Platforms Market Size (2023)	US$ 175 billion
Global Smart City Platforms Market Size (2033)	US$ 496.9 billion
Quantitative Units	Revenue in US$ million and CAGR from 2023 to 2033
Report Coverage	Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis
Segments Covered	Offering Delivery Model Application Region
Regions Covered	North America Latin America Western Europe Eastern Europe Asia Pacific excluding Japan Japan The Middle East and Africa
Key Countries Profiled	The United States Canada Brazil Mexico Germany The United Kingdom France Spain Italy Poland Russia Czech Republic India Bangladesh Australia New Zealand China Japan South Korea GCC Countries South Africa Israel
Customization	Available Upon Request

In 2019, Auckland in New Zealand was visited by 1,829,526 international visitors. Queenstown on the south island was the next most popular destination with just over a million international visitors in that year.

Auckland's leading inbound travel market was Australia in the year ended June 2024, with over 708,000 international arrivals from Australia visiting New Zealand's most populous city. Visitors from the United States were also a key inbound travel market for Auckland, with around 309,000 U.S. tourists visiting the city that year.

Herne Bay in Auckland was the most expensive residential housing suburb in New Zealand in 2023, with a median property value of over 3.1 million New Zealand dollars. In comparison, the median property value in the Flagstaff suburb of Hamilton came to around 1.07 million New Zealand dollars.

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.