This web map is provides the data and maps used in the story map Population density and diversity in New Zealand, created by Stats NZ. It uses Statistical Area 1 (SA1) data collected and published as part of the 2018 Census. The web map uses a mapping technique called multi-variate dot density mapping. The data used in the map can be found at this web service - 2018 Census Individual part 1 data by SA1.For questions or comments on the data or maps, please contact info@stats.govt.nz Census Data Quality Notes:We combined data from the census forms with administrative data to create the 2018 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 should be counted but hadn’t completed a census form. We also used data from the 2013 Census and administrative sources and statistical imputation methods to fill in some missing characteristics of people and dwellings.Data quality for 2018 Census provides more information on the quality of the 2018 Census data.An independent panel of experts has assessed the quality of the 2018 Census dataset. The panel has endorsed Stats NZ’s overall methods and concluded that the use of government administrative records has improved the coverage of key variables such as age, sex, ethnicity, and place. The panel’s Initial Report of the 2018 Census External Data Quality Panel (September 2019), assessed the methodologies used by Stats NZ to produce the final dataset, as well as the quality of some of the key variables. Its second report 2018 Census External Data Quality Panel: Assessment of variables (December 2019) assessed an additional 31 variables. In its third report, Final report of the 2018 Census External Data Quality Panel (February 2020), the panel made 24 recommendations, several relating to preparations for the 2023 Census. Along with this report, the panel, supported by Stats NZ, produced a series of graphs summarising the sources of data for key 2018 Census individual variables, 2018 Census External Data Quality Panel: Data sources for key 2018 Census individual variables.The Quick guide to the 2018 Census outlines the key changes we introduced as we prepared for the 2018 Census, and the changes we made once collection was complete.The geographic boundaries are as at 1 January 2018. See Statistical standard for geographic areas 2018.2018 Census – DataInfo+ provides information about methods, and related metadata.Data quality ratings for 2018 Census variables provides information on data quality ratings.

1 kilometer grid square across country showing population density using data from LINZ, and Stats NZ.

A Choropleth map created from population density figures released by Statistics New Zealand after the 2013 census. All data has been generalized so no one person can be identified.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

Students will explore the patterns of world population in terms of total population, arithmetic density, total fertility rate, natural increase rate, and infant mortality rate. The activity uses a web-based map.Learning outcomes:Students will be able to identify and explain the spatial patterns and distribution of world population based on total population, density, total fertility rate, natural increase rate, and infant mortality rate.Other New Zealand GeoInquiry instructional material freely available at https://arcg.is/1GPDXe

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 urban rural (UR) boundaries as at 1 January 2025 as defined by Stats NZ, clipped to the coastline. This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. This version contains 689 UR areas, including 195 urban areas and 402 rural settlements.

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.

Urban areas

Urban areas are statistically defined areas with no administrative or legal basis. They are characterised by high population density with many built environment features where people and buildings are located close together for residential, cultural, productive, trade and social purposes.

Urban areas are delineated using the following criteria. They:

form a contiguous cluster of one or more SA2s,

contain an estimated resident population of more than 1,000 people and usually have a population density of more than 400 residents or 200 address points per square kilometre,

have a high coverage of built physical structures and artificial landscapes such as:

• residential dwellings and apartments,

• commercial structures, such as factories, office complexes, and shopping centres,

• transport and communication facilities, such as airports, ports and port facilities, railway stations, bus stations and similar transport hubs, and communications infrastructure,

• medical, education, and community facilities,

• tourist attractions and accommodation facilities,

• waste disposal and sewerage facilities,

• cemeteries,

• sports and recreation facilities, such as stadiums, golf courses, racecourses, showgrounds, and fitness centres,

• green spaces, such as community parks, gardens, and reserves,

have strong economic ties where people gather together to work, and for social, cultural, and recreational interaction,

have planned development within the next 5–8 years.

Urban boundaries are independent of local government and other administrative boundaries. However, the Richmond urban area, which is mainly in the Tasman District, is the only urban area that crosses territorial authority boundaries

Rural areas

Rural areas are classified as rural settlements or other rural.

Rural settlements

Rural settlements are statistically defined areas with no administrative or legal basis. A rural settlement is a cluster of residential dwellings about a place that usually contains at least one community or public building.

Rural settlements are delineated using the following criteria. They:

form a contiguous cluster of one or more SA1s,

contain an estimated resident population of 200–1,000, or at least 40 residential dwellings,

represent a reasonably compact area or have a visible centre of population with a population density of at least 200 residents per square kilometre or 100 address points per square kilometre,

contain at least one community or public building, such as a church, school, or shop.

To reach the target SA2 population size of more than 1,000 residents, rural settlements are usually included with other rural SA1s to form an SA2. In some instances, the settlement and the SA2 have the same name, for example, Kirwee rural settlement is part of the Kirwee SA2.

Some rural settlements whose populations are just under 1,000 are a single SA2. Creating separate SA2s for these rural settlements allows for easy reclassification to urban areas if their populations grow beyond 1,000.

Other rural

Other rural areas are the mainland areas and islands located outside urban areas or rural settlements. Other rural areas include land used for agriculture and forestry, conservation areas, and regional and national parks. Other rural areas are defined by territorial authority.

Water

Bodies of water are classified separately, using the land/water demarcation classification described in the Statistical standard for meshblock. These water areas are not named and are defined by territorial authority or regional council.

The water classes include:

inland water – non-contiguous, defined by territorial authority,

inlets (which also includes tidal areas and harbours) – non-contiguous, defined by territorial authority,

oceanic – non-contiguous, defined by regional council.

To minimise suppression of population data, separate meshblocks have been created for marinas. These meshblocks are attached to adjacent land in the UR geography.

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.

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.

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.

This was based on 2018 Census data and 2021 population estimates. Their IUR status (urban area size/rural settlement) may change if the 2025 Census population count moves them up or down a category.

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).

Clipped Version

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

​

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’.

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Digital data

Digital boundary data became freely available on 1 July 2007.

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Further information

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

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

Contact: geography@stats.govt.nz

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 of population and dwelling growth mainly in urban areas, and to make some improvements to their delineation of communities of interest.

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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. This version contains 2,395 SA2s (2,379 digitised and 16 with empty or null geometries (non-digitised)).

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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.

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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:

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

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

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

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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.

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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.

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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.

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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.

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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.

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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:

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• 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.

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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).

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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.

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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’.

​

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

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 urban rural (UR) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 689 UR areas, including 195 urban areas and 402 rural settlements.

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.

Urban areas

Urban areas are statistically defined areas with no administrative or legal basis. They are characterised by high population density with many built environment features where people and buildings are located close together for residential, cultural, productive, trade and social purposes.

Urban areas are delineated using the following criteria. They:

form a contiguous cluster of one or more SA2s,

contain an estimated resident population of more than 1,000 people and usually have a population density of more than 400 residents or 200 address points per square kilometre,

have a high coverage of built physical structures and artificial landscapes such as:

• residential dwellings and apartments,
• commercial structures, such as factories, office complexes, and shopping centres,
• transport and communication facilities, such as airports, ports and port facilities, railway stations, bus stations and similar transport hubs, and communications infrastructure,
• medical, education, and community facilities,
• tourist attractions and accommodation facilities,
• waste disposal and sewerage facilities,
• cemeteries,
• sports and recreation facilities, such as stadiums, golf courses, racecourses, showgrounds, and fitness centres,
• green spaces, such as community parks, gardens, and reserves,

have strong economic ties where people gather together to work, and for social, cultural, and recreational interaction,

have planned development within the next 5–8 years.

Urban boundaries are independent of local government and other administrative boundaries. However, the Richmond urban area, which is mainly in the Tasman District, is the only urban area that crosses territorial authority boundaries

Rural areas

Rural areas are classified as rural settlements or other rural.

Rural settlements

Rural settlements are statistically defined areas with no administrative or legal basis. A rural settlement is a cluster of residential dwellings about a place that usually contains at least one community or public building.

Rural settlements are delineated using the following criteria. They:

form a contiguous cluster of one or more SA1s,

contain an estimated resident population of 200–1,000, or at least 40 residential dwellings,

represent a reasonably compact area or have a visible centre of population with a population density of at least 200 residents per square kilometre or 100 address points per square kilometre,

contain at least one community or public building, such as a church, school, or shop.

To reach the target SA2 population size of more than 1,000 residents, rural settlements are usually included with other rural SA1s to form an SA2. In some instances, the settlement and the SA2 have the same name, for example, Kirwee rural settlement is part of the Kirwee SA2.

Some rural settlements whose populations are just under 1,000 are a single SA2. Creating separate SA2s for these rural settlements allows for easy reclassification to urban areas if their populations grow beyond 1,000.

Other rural

Other rural areas are the mainland areas and islands located outside urban areas or rural settlements. Other rural areas include land used for agriculture and forestry, conservation areas, and regional and national parks. Other rural areas are defined by territorial authority.

Water

Bodies of water are classified separately, using the land/water demarcation classification described in the Statistical standard for meshblock. These water areas are not named and are defined by territorial authority or regional council.

The water classes include:

inland water – non-contiguous, defined by territorial authority,

inlets (which also includes tidal areas and harbours) – non-contiguous, defined by territorial authority,

oceanic – non-contiguous, defined by regional council.

To minimise suppression of population data, separate meshblocks have been created for marinas. These meshblocks are attached to adjacent land in the UR geography.

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.

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.

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.

This was based on 2018 Census data and 2021 population estimates. Their IUR status (urban area size/rural settlement) may change if the 2025 Census population count moves them up or down a category.

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).

​

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

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 dataset represents the terrestrial distribution of Possum (Trichosurus vulpecula) in New Zealand (mainland New Zealand and Chatham Islands) as at 1 July 2007. It represents this species' final layer as part of the Natural Heritage Management Strategy of the Department of Conservation. Spatial and textual data were collected at the Area level by Department of Conservation Area Office and Conservancy Office staff. Polygons representing the distribution were hand-drawn onto LINZ 260 series topo maps at the 1:250,000 scale. Digitisation of the hand-drawn maps was contracted to Geographx (NZ) Limited. Freshwater lakes, ponds and rivers were clipped from the layer to represent species absence. Further digitisation work was carried out by the DOC R GIS team in Christchurch to re-define selected polygons by species specific distribution drivers; i.e. altitude and habitat guided by LCBD 2. The attribute table may contain references to quantitative datasets of species abundance (where such datasets exist).

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.

​

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.

​

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.

​

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.

​

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.

​

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

We conducted a replicated density perturbation experiment to manipulate late instar larvae of nine populations of a stream caddisfly, Zelandopsyche ingens, and measured the resulting abundance of the next generation over two years covering the complete lifecycle of one cohort to evaluate influences on their population dynamics. Two datasets are included that cover Z. ingens abundance at (a) the manipulation and (b) patterns of the generation after the manipulation. The locations of the streams used around the University of Canterbury's Cass Mountain Research Field Station in the upper Waimakariri River catchment, New Zealand are described in a map and table in the associated publication.

1. The identification of geographic areas where the densities of animals are highest across their annual cycles is a crucial step in conservation planning. In marine environments, however, it can be particularly difficult to map the distribution of species, and the methods used are usually biased towards adults, neglecting the distribution of other life-history stages even though they can represent a substantial proportion of the total population.

2. Here we develop a methodological framework for estimating population-level density distributions of seabirds, integrating tracking data across the main life-history stages (adult breeders and non-breeders, juveniles and immatures). We incorporate demographic information (adult and juvenile/immature survival, breeding frequency and success, age at first breeding) and phenological data (average timing of breeding and migration) to weight distribution maps according to the proportion of the population represented by each life-history stage.

3. We demonstrate the utility of this framework by applying it to 22 species of albatrosses and petrels that are of conservation concern due to interactions with fisheries. Because juveniles, immatures and non-breeding adults account for 47–81% of all individuals of the populations analysed, ignoring the distributions of birds in these stages leads to biased estimates of overlap with threats, and may misdirect management and conservation efforts. Population-level distribution maps using only adult distributions underestimated exposure to longline fishing effort by 18–42%, compared with overlap scores based on data from all life-history stages.

4. Synthesis and applications. Our framework synthesises and improves on previous approaches to estimate seabird densities at sea, is applicable for data-poor situations, and provides a standard and repeatable method that can be easily updated as new tracking and demographic data become available. We provide scripts in the R language and a Shiny app to facilitate future applications of our approach. We recommend that where sufficient tracking data are available, this framework be used to assess overlap of seabirds with at-sea threats such as overharvesting, fisheries bycatch, shipping, offshore industry and pollutants. Based on such an analysis, conservation interventions could be directed towards areas where they have the greatest impact on populations.

Methods Quarterly and annual population-level density distributions for 22 species of albatrosses and petrels (36 populations) breeding in the Southern Ocean based on tracking, phenology and demography data. The raster files have the number of individuals for each population in each 5 x 5 degree cell during a given season or year. The number of individuals represents the sum of all life-history stages. See Carneiro et al., (2020) for a detailed description of the methods.

Highbush blueberry (Vaccinium corymbosum, 2n = 4x = 48) is the most cultivated type of blueberry, both in New Zealand and overseas. Its perceived nutritional value is conferred by phytonutrients, particularly anthocyanins. Identifying the genetic mechanisms that control the biosynthesis of these metabolites would enable faster development of cultivars with improved fruit qualities. Here, we used recently released tools for genetic mapping in autotetraploids to build a high-density linkage map in highbush blueberry and to detect quantitative trait loci (QTLs) for fruit anthocyanin content. Genotyping was performed by target sequencing, with ∼18,000 single nucleotide polymorphism (SNP) markers being mapped into 12 phased linkage groups (LGs). Fruits were harvested when ripe for two seasons and analyzed with high-performance liquid chromatography-mass spectrometry (HPLC-MS): 25 different anthocyanin compounds were identified and quantified. Two major QTLs that were stable across years were discovered, one on LG2 and one on LG4, and the underlying candidate genes were identified. Interestingly, the presence of anthocyanins containing acylated sugars appeared to be under strong genetic control. Information gained in this study will enable the design of molecular markers for marker-assisted selection and will help build a better understanding of the genetic control of anthocyanin biosynthesis in this crop.

Highbush blueberry (Vaccinium corymbosum, 2n = 4x = 48) is the most cultivated type of blueberry, both in New Zealand and overseas. Its perceived nutritional value is conferred by phytonutrients, particularly anthocyanins. Identifying the genetic mechanisms that control the biosynthesis of these metabolites would enable faster development of cultivars with improved fruit qualities. Here, we used recently released tools for genetic mapping in autotetraploids to build a high-density linkage map in highbush blueberry and to detect quantitative trait loci (QTLs) for fruit anthocyanin content. Genotyping was performed by target sequencing, with ∼18,000 single nucleotide polymorphism (SNP) markers being mapped into 12 phased linkage groups (LGs). Fruits were harvested when ripe for two seasons and analyzed with high-performance liquid chromatography-mass spectrometry (HPLC-MS): 25 different anthocyanin compounds were identified and quantified. Two major QTLs that were stable across years were discovered, one on LG2 and one on LG4, and the underlying candidate genes were identified. Interestingly, the presence of anthocyanins containing acylated sugars appeared to be under strong genetic control. Information gained in this study will enable the design of molecular markers for marker-assisted selection and will help build a better understanding of the genetic control of anthocyanin biosynthesis in this crop.