SIA23 - Nominal Median and Nominal Mean Income Measures. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Nominal Median and Nominal Mean Income Measures...

TAH28 - Mean and Median equivalised nominal disposable income. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Mean and Median equivalised nominal disposable income...

Dataset from Ministry of Manpower. For more information, visit https://data.gov.sg/datasets/d_16dfef0280cd2c09f95dcb52c6a7a006/view

Goals: Measure SOA yield ... D5 Summary: This is the set of experiments performed with D5 where the RH was adjusted so that O3 made variable OH concentrations. This is half of the set (see 210301 for more). These are F9 through F15. Organization: Caltech Atmospheric Chamber Lab Affiliation: California Institute of Technology, Pasadena, CA Chamber: CPOT Experiment Category: Aerosol formation Oxidant: Hydroxyl radical Reactants: DECAMETHYLCYCLOPENTASILOXANE Reaction Type: Photooxidation Relative Humidity: 00 Temperature: 23.0

Data Set Overview The Mars Express (MEX) Planetary Fourier Spectrometer (PFS) Data Archive is a collection of raw data collected during the MEX Mission to Mars. For more information on the investigations proposed see the PFS documentations in the DOCUMENT/ folder. This data set was collected during the MEX Mission phases: First Extension Mission Phase Mission Phase Definition It should be noted that the Mars Express (MEX) Planetary Fourier Spectrometer (PFS) group uses mission phases which deviate from the ones defined in the MISSION.CAT files given by ESA in order to keep the keywords and abbreviations consistent for Mars Express, Venus Express and Rosetta. Those mission phase abbreviations are also used in the data description field of the dataset_id. MaRS mission name | abbreviation | time span Near Earth Verification | NEV | 20030602 20030731 Interplanetary Cruise | IC | 20030801 20031225 Nominal Mission | Nominal | 20031226 20051130 First Extension Mission | EXT1 | 20060101 20070930 Second Extension Mission| EXT2 | 20071001 20091231 Data files Data files are: The tracking files from Deep Space Network (DSN) and from the Intermediate Frequency Modulation System (IFMS) used by the ESA ground station New Norcia. Level 1b data are archived. The Geometry files All Level binary data files will have the file name extension eee .DAT Data levels It should be noted that these data levels which are also used in the file names and data directories are PSA dat truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

TSIS-1 SIM Solar Spectral Irradiance V09 ; ; SELECTION CRITERIA ; date range: 20180314 to 20230129 ; cadence: 24 hours ; spectral range: 200.0 to 2400.0 nm ; number of data: 3307488 ; identifier_product_doi: 10.5067/TSIS/SIM/DATA318 ; identifier_product_doi_authority: http://dx.doi.org/ ; END SELECTION CRITERIA ;
; DATA DEFINITIONS, number = 11 (name, type, format) ; nominal_date_yyyymmdd, R8, f11.2 ; nominal_date_jdn, R8, f11.2 ; wavelength, R4, f9.3 (nm) ; instrument_mode_id, I2, i3 ; data_version, I2, i3 ; irradiance_1AU, R8, e15.8 (W/m^2/nm) ; instrument_uncertainty, R8, e15.8 (W/m^2/nm, 1 sigma) ; measurement_precision, R8, e15.8 (W/m^2/nm, 1 sigma) ; measurement_stability, R8, e15.8 (W/m^2/nm, 1 sigma) ; additional_uncertainty, R8, e15.8 (W/m^2/nm, 1 sigma) ; quality, UI2, i6 ; END DATA DEFINITIONS ; ; Background on the Total and Spectral Solar Irradiance Sensor (TSIS-1) ; ; The Total and Spectral Solar Irradiance Sensor (TSIS-1) level 3 (L3) data product is constructed ; using measurements from the Total Irradiance Monitor (TIM) and Spectral Irradiance Monitor (SIM) ; instruments. The TIM instrument measures the total solar irradiance (TSI) that is incident at the ; outer boundaries of the atmosphere and the SIM instrument measures the solar spectral irradiance ; (SSI) from 200 nm to 2400 nm, which are combined into 12-hr and 24-hr solar spectra. The TSIS-1 data ; products are provided on a fixed wavelength scale, which has a variable resolution over the ; spectral range. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of- ; sight velocity with respect to the Sun. ; ; Table: Solar Spectral Irradiance (SSI) Measurement Summary. ; ; Measuring Instrument SIM ; Temporal Cadence Daily ; Detector Diodes (200 nm to 1620 nm), ESR (1620 nm to 2400 nm) ; Instrument Modes 86 (UV), 85 (VIS), 84 (IR), 83 (ESR) ; Spectral Range 200 nm to 2400 nm ; ; The spectral irradiances are tabulated below ("DATA RECORDS"), with each row giving the nominal date ; (YYYYMMDD.D), nominal date (Julian Day), wavelength center (nm), instrument mode, data version, ; spectral irradiance @ 1au (irradiance_1AU, Watts/m^2/nm), instrument_uncertainty (Watts/m^2/nm), ; measurement_precision (Watts/m^2/nm), measurement_stability (Watts/m^2/nm), additional_uncertainty ; (Watts/m^2/nm), and a "quality" (data quality flag) value. Measurement_stability is given as ; 0.00000000e+00 (0.0) at wavelengths > 1050 nm, where we do not currently calculate a degradation ; correction, and for all data that arrives after the bi-annual Channel C calibration scans. The ; bi-annual Channel C scans trigger a new data release version, so there could be up to six months of ; measurement stability values that are 0.0 until determined during the creation of the next data release. ; Data quality flags are assigned to each spectral measurement in the 'quality' column. The value in this ; column is the addition of all the bit-wise data quality flags (DQF) associated with a given measurement. ; Nominal data has a DQF of '0'. The L3 TSIS-1 SIM data quality flags are: ; ; VALUE CONDITION ; ----- --------- ; 1 Missing data ; 2 Backfilled data (from previous day) ; 512 Data taken with offset pointing; a spectral correction has been applied ; ; Data with the '512' bit set was taken from March 19, 2022 through May 19, 2022. During this period, ; the TSIS-1 SIM pointing was off by ~1 arcmin due to external contamination of the pointing system ; quad-diode (HFSSB). A wavelength-dependent correction has been applied to data during this period, ; and the corresponding additional irradiance uncertainties associated with this correction are given ; in the 'additional_uncertainty' column. Note that it is possible that multiple flags can be set on ; the same measurement. For example, a quality of '514' is backfilled data, and the data used was taken ; during the offset pointing. ; ; Instrument_uncertainty, measurement_precision, measurement_stability, and additional_uncertainty are ; all in units of (Watts/m^2/nm). ; ; Each field (column) is defined and further described in the "DATA DEFINITIONS" section. ; ; An IDL file reader (http://lasp.colorado.edu/data/tsis/file_readers/read_lasp_ascii_file.pro) is ; available which will read this file and return an array of structures whose field names and types ; are taken from the "DATA DEFINITIONS" section. ; ; Erik Richard (2023), Level 3 (L3) Solar Spectral Irradiance Daily Means V009, ; Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), ; Accessed [Data Access Date] at http://dx.doi.org/10.5067/TSIS/SIM/DATA318 ; ; For more information on the TSIS-1 instruments and data products, see: ; http://lasp.colorado...

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 3 (SA3) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 929 SA3s, including 4 non-digitised SA3s.

The SA3 geography aims to meet three purposes:

1. approximate suburbs in major, large, and medium urban areas,

2. in predominantly rural areas, provide geographical areas that are larger in area and population size than SA2s but smaller than territorial authorities,

3. minimise data suppression.

SA3s in major, large, and medium urban areas were created by combining SA2s to approximate suburbs as delineated in the Fire and Emergency NZ (FENZ) Localities dataset. Some of the resulting SA3s have very large populations.

Outside of major, large, and medium urban areas, SA3s generally have populations of 5,000–10,000. These SA3s may represent either a single small urban area, a combination of small urban areas and their surrounding rural SA2s, or a combination of rural SA2s.

Zero or nominal population SA3s

To minimise the amount of unsuppressed data that can be provided in multivariate statistical tables, SA2s with fewer than 1,000 residents are combined with other SA2s wherever possible to reach the 1,000 SA3 population target. However, there are still a number of SA3s with zero or nominal populations.

Small population SA2s designed to maintain alignment between territorial authority and regional council geographies are merged with other SA2s to reach the 5,000–10,000 SA3 population target. These merges mean that some SA3s do not align with regional council boundaries but are aligned to territorial authority.

Small population island SA2s are included in their adjacent land-based SA3.

Island SA2s outside territorial authority or region are the same in the SA3 geography.

Inland water SA2s are aggregated and named by territorial authority, as in the urban rural classification.

Inlet SA2s are aggregated and named by territorial authority or regional council where the water area is outside the territorial authority.

Oceanic SA2s translate directly to SA3s as they are already aggregated to regional council.

The 16 non-digitised SA2s are aggregated to the following 4 non-digitised SA3s (SA3 code; SA3 name):

70001; Oceanic outside region, 70002; Oceanic oil rigs, 70003; Islands outside region, 70004; Ross Dependency outside region.

SA3 numbering and naming

Each SA3 is a single geographic entity with a name and a numeric code. The name refers to a suburb, recognised place name, or portion of a territorial authority. In some instances where place names are the same or very similar, the SA3s are differentiated by their territorial authority, for example, Hillcrest (Hamilton City) and Hillcrest (Rotorua District).

SA3 codes have five digits. North Island SA3 codes start with a 5, South Island SA3 codes start with a 6 and non-digitised SA3 codes start with a 7. They are numbered approximately north to south within their respective territorial authorities. When first created in 2025, the last digit of each code was 0. When SA3 boundaries change in future, only the last digit of the code will change to ensure the north-south pattern is maintained.

​

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

1 Introduction

The Peatland Decomposition Database (PDD) stores data from published litterbag experiments related to peatlands. Currently, the database focuses on northern peatlands and Sphagnum litter and peat, but it also contains data from some vascular plant litterbag experiments. Currently, the database contains entries from 34 studies, 2,160 litterbag experiments, and 7,297 individual samples with 117,841 measurements for various attributes (e.g. relative mass remaining, N content, holocellulose content, mesh size). The aim is to provide a harmonized data source that can be useful to re-analyse existing data and to plan future litterbag experiments.

The Peatland Productivity and Decomposition Parameter Database (PPDPD) (Bona et al. 2018) is similar to the Peatland Decomposition Database (PDD) in that both contain data from peatland litterbag experiments. The differences are that both databases partly contain different data, that PPDPD additionally contains information on vegetation productivity, which PDD does not, and that PDD provides more information and metadata on litterbag experiments, and also measurement errors.

2 Updates

Compared to version 1.0.0, this version has a new structure for table experimental_design_format, contains additional metadata on the experimental design (these were omitted in version 1.0.0), and contains the scripts that were used to import the data into the database.

3 Methods

3.1 Data collection

Data for the database was collected from published litterbag studies, by extracting published data from figures, tables, or other data sources, and by contacting the authors of the studies to obtain raw data. All data processing was done with R (R version 4.2.0 (2022-04-22)) (R Core Team 2022).

Studies were identified via a Scopus search with search string (TITLE-ABS-KEY ( peat* AND ( "litter bag" OR "decomposition rate" OR "decay rate" OR "mass loss")) AND NOT ("tropic*")) (2022-12-17). These studies were further screened to exclude those which do not contain litterbag data or which recycle data from other studies that have already been considered. Additional studies with litterbag experiments in northern peatlands we were aware of, but which were not identified in the literature search were added to the list of publications. For studies not older than 10 years, authors were contacted to obtain raw data, however this was successful only in few cases. To date, the database focuses on Sphagnum litterbag experiments and not from all studies that were identified by the literature search data have been included yet in the database.

Data from figures were extracted using the package ‘metaDigitise’ (1.0.1) (Pick, Nakagawa, and Noble 2018). Data from tables were extracted manually.

Data from the following studies are currently included: Farrish and Grigal (1985), Bartsch and Moore (1985), Farrish and Grigal (1988), Vitt (1990), Hogg, Lieffers, and Wein (1992), Sanger, Billett, and Cresser (1994), Hiroki and Watanabe (1996), Szumigalski and Bayley (1996), Prevost, Belleau, and Plamondon (1997), Arp, Cooper, and Stednick (1999), Robbert A. Scheffer and Aerts (2000), R. A. Scheffer, Van Logtestijn, and Verhoeven (2001), Limpens and Berendse (2003), Waddington, Rochefort, and Campeau (2003), Asada, Warner, and Banner (2004), Thormann, Bayley, and Currah (2001), Trinder, Johnson, and Artz (2008), Breeuwer et al. (2008), Trinder, Johnson, and Artz (2009), Bragazza and Iacumin (2009), Hoorens, Stroetenga, and Aerts (2010), Straková et al. (2010), Straková et al. (2012), Orwin and Ostle (2012), Lieffers (1988), Manninen et al. (2016), Johnson and Damman (1991), Bengtsson, Rydin, and Hájek (2018a), Bengtsson, Rydin, and Hájek (2018b), Asada and Warner (2005), Bengtsson, Granath, and Rydin (2017), Bengtsson, Granath, and Rydin (2016), Hagemann and Moroni (2015), Hagemann and Moroni (2016), B. Piatkowski et al. (2021), B. T. Piatkowski et al. (2021), Mäkilä et al. (2018), Golovatskaya and Nikonova (2017), Golovatskaya and Nikonova (2017).

4 Database records

The database is a ‘MariaDB’ database and the database schema was designed to store data and metadata following the Ecological Metadata Language (EML) (Jones et al. 2019). Descriptions of the tables are shown in Tab. 1.

The database contains general metadata relevant for litterbag experiments (e.g., geographical, temporal, and taxonomic coverage, mesh sizes, experimental design). However, it does not contain a detailed description of sample handling, sample preprocessing methods, site descriptions, because there currently are no discipline-specific metadata and reporting standards. Table 1: Description of the individual tables in the database.

Name Description

attributes Defines the attributes of the database and the values in column attribute_name in table data.

citations Stores bibtex entries for references and data sources.

citations_to_datasets Links entries in table citations with entries in table datasets.

custom_units Stores custom units.

data Stores measured values for samples, for example remaining masses.

datasets Lists the individual datasets.

experimental_design_format Stores information on the experimental design of litterbag experiments.

measurement_scales, measurement_scales_date_time, measurement_scales_interval, measurement_scales_nominal, measurement_scales_ordinal, measurement_scales_ratio Defines data value types.

missing_value_codes Defines how missing values are encoded.

samples Stores information on individual samples.

samples_to_samples Links samples to other samples, for example litter samples collected in the field to litter samples collected during the incubation of the litterbags.

units, unit_types Stores information on measurement units.

5 Attributes Table 2: Definition of attributes in the Peatland Decomposition Database and entries in the column attribute_name in table data.

Name Definition Example value Unit Measurement scale Number type Minimum value Maximum value String format

4_hydroxyacetophenone_mass_absolute A numeric value representing the content of 4-hydroxyacetophenone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

4_hydroxyacetophenone_mass_relative_mass A numeric value representing the content of 4-hydroxyacetophenone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

4_hydroxybenzaldehyde_mass_absolute A numeric value representing the content of 4-hydroxybenzaldehyde, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

4_hydroxybenzaldehyde_mass_relative_mass A numeric value representing the content of 4-hydroxybenzaldehyde, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

4_hydroxybenzoic_acid_mass_absolute A numeric value representing the content of 4-hydroxybenzoic acid, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

4_hydroxybenzoic_acid_mass_relative_mass A numeric value representing the content of 4-hydroxybenzoic acid, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

abbreviation In table custom_units: A string representing an abbreviation for the custom unit. gC NA nominal NA NA NA NA

acetone_extractives_mass_absolute A numeric value representing the content of acetone extractives, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

acetone_extractives_mass_relative_mass A numeric value representing the content of acetone extractives, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

acetosyringone_mass_absolute A numeric value representing the content of acetosyringone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

acetosyringone_mass_relative_mass A numeric value representing the content of acetosyringone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

acetovanillone_mass_absolute A numeric value representing the content of acetovanillone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

acetovanillone_mass_relative_mass A numeric value representing the content of acetovanillone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

arabinose_mass_absolute A numeric value representing the content of arabinose, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

arabinose_mass_relative_mass A numeric value representing the content of arabinose, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

ash_mass_absolute A numeric value representing the content of ash (after burning at 550°C). 4 g ratio real 0 Inf NA

ash_mass_relative_mass A numeric value representing the content of ash (after burning at 550°C). 0.05 g/g ratio real 0 Inf NA

attribute_definition A free text field with a textual description of the meaning of attributes in the dpeatdecomposition database. NA NA nominal NA NA NA NA

attribute_name A string describing the names of the attributes in all tables of the dpeatdecomposition database. attribute_name NA nominal NA NA NA NA

bibtex A string representing the bibtex code used for a literature reference throughout the dpeatdecomposition database. Galka.2021 NA nominal NA NA NA NA

bounds_maximum A numeric value representing the minimum possible value for a numeric attribute. 0 NA interval real Inf Inf NA

bounds_minimum A numeric value representing the maximum possible value for a numeric attribute. INF NA interval real Inf Inf NA

bulk_density A numeric value representing the bulk density of the sample [g cm-3]. 0,2 g/cm^3 ratio real 0 Inf NA

C_absolute The absolute mass of C in the sample. 1 g ratio real 0 Inf NA

C_relative_mass The absolute mass of C in the sample. 1 g/g ratio real 0 Inf NA

C_to_N A numeric value representing the C to N ratio of the sample. 35 g/g ratio real 0 Inf NA

C_to_P A numeric value representing the C to P ratio of the sample. 35 g/g ratio real 0 Inf NA

Ca_absolute The

This table contains data on government debt. The debt is divided into different debt securities. Of each debt instrument (type of financial instrument) the share of the various holders (money-givers) is indicated. The debt is presented in nominal value (the original amount of debt) and in market value (the value at which the debt can be traded in the relevant period). When determining the sovereign debt according to EMU definitions, the nominal value is used, in the National Accounts, the market value.

The data in this table have been consolidated, i.e. the elimination of flows between them. As a result, the debts of the subsectors do not add up to the total government debt. Debts of, for example, the empire to the social insurers are part of the government’s debts. For the debt of the total government, they do not count, after all, they are debts that the government has to the government.

The terms used are in line with the National Accounts. The National Accounts are based on the international definitions of the European System of Accounts (ESA 1995). To increase the accessibility of the table, in some cases more common descriptions are used instead of the terms from the National Accounts. The relevant National Accounts term is then mentioned in the notes. The data presented corresponds to the publications on the National Accounts and the EMU publications.

Data available from: Annual figures from 1990 to 2013, quarterly figures from 2005 to 2013.

Status of the figures: The figures in this table since 1990 are final. The most recent years have a (further) provisional character. As this table has been discontinued, the data will no longer be definitive.

Changes as of 25 June 2014: None, this table has been discontinued.

When are new figures coming? No longer applicable. This table is followed by Government; debt to debt securities and lender, nominal and market value. See paragraph 3.

This dataset contains multiple variables with spatio-temporal information relating to sea-ice and the southern ocean. This collection of data is utilised by the nilas.org platform for dynamically visualising these variables in the web browser. Together they provide a valuable resource for understanding the interactions between physical, climate and biogeochemical parameters. These include variables to understand sea-ice in three dimensions, chlorophyll and sea surface temperature. The time range of these data covers from 1980 until the present and the spatial coverage is Antarctic circumpolar. Name: Daily Sea Ice Concentration Desc: Sea ice concentration is a measure of the amount of size ice over an area. It is calculated from satellite observations of sea ice for all areas adjacent the Antarctic coastline. The minimum area of sea ice naturally occurs in February and the maximum in September. Product: ARTIST (ASI 5) (Spreen et al. 2008) Source: Universität Bremen Resolution: 6.125 km nominal Timeframe: 2012 to present Notes: Concentrations of less than 15% have been removed. Name: Monthly Sea Ice Concentration Desc: Sea ice concentration is a measure of the amount of size ice over an area. It is calculated from satellite observations of sea ice for all areas adjacent the Antarctic coastline. The minimum area of sea ice naturally occurs in February and the maximum in September. Product: Sea Ice Index (Windnagel et al. 2017) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25 km nominal Timeframe: 1980 to present Notes: Concentrations of less than 15% have been removed. Name: Monthly Anomalies in Sea Ice Concentration Desc: Anomalies in sea ice concentration show the monthly variation from the long term mean. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to present Notes: Anomalies are calculated as the difference between the sea ice concentration and the 1981-2010 mean sea ice concentration for that month. Anomalies less than 7.5% are not shown. Name: Long term monthly mean sea ice extent Desc: Sea ice extent is calculated as contour lines at 15% and 80% sea ice concentration. Product: Sea Ice Index (Windnagel et al. 2017) Source: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Resolution: - Timeframe: 1980 to present Notes: Contours with less than 15 vertices are discarded. Name: Long Term Monthly Mean Sea Ice Extent Desc: Mean monthly sea ice extent over the 1981-2010 time interval. This is calculated as contour lines at 15% and 80% long term mean (1981-2010) sea ice concentration. Product: Sea Ice Index (Windnagel et al. 2017) Source: NSIDC (National Snow and Ice Data Center) Resolution: - Timeframe: Long term monthly mean (1981-2010) Notes: Contours with less than 15 vertices are discarded. Name: Gridded Freeboard (ATL20) IceSat2 Desc: Sea ice freeboard is the distance between the waterline and the surface height of sea ice in open leads. This dataset contains monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10,V3). Product: ATL20 (Petty et al. 2020) Source: NSIDC Resolution: 25 km nominal Timeframe: Oct 2018 to July 2022 Notes: Data greater than 1 metre is shown as 1 metre height. Name: Annual Sea Ice Duration Desc: Sea ice duration (contour lines) is the number of days sea ice concentrations above 15% occur between consecutive sea ice minima (assumed to occur on Feb 16 each year). Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2021 Notes:
Name: Sea Ice Duration Anomalies Desc: Anomalies in sea ice duration show difference in duration of sea ice from the long term mean, where sea ice duration is the number of days sea ice concentrations above 15% occur between consecutive sea ice minima. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2021 Notes: Anomalies in sea ice duration are calculated relative to the 1981 to 2010 mean. Name: Annual Sea Ice Advance Desc: Sea ice advance is the date when sea ice concentrations persist above 15% after the sea ice minimum. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2022 Notes:
Name: Sea Ice Advance Anomalies Desc: Anomalies in sea ice advance show number of days (early/late) from the long term mean, where sea ice advance is the date when sea ice concentrations persist above 15% after the sea ice minimum. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2022 Notes: Anomalies in sea ice advance are calculated relative to the 1981 to 2010 mean. Name: Annual Sea Ice Retreat Desc: Sea ice retreat is the date when sea ice concentrations persist below 15% occur after the sea ice maximum. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2021 Notes:
Name: Annual Sea Ice Retreat Desc: Anomalies in sea ice retreat show number of days (early/late) from the long term mean, where sea ice retreat is the date when sea ice concentrations persist below 15% occur after the sea ice maximum. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2021 Notes: Anomalies in sea ice retreat are calculated relative to the 1981 to 2010 mean. Name: Monthly Chlorophyll Concentration Desc: Chlorophyll-a is a proxy for phytoplankton activity in the ocean and is estimated through ocean colour remote sensing. Product: Ocean Colour Climate Change Initiative (Sathyendranath et al. 2019) Source: Ocean Colour Resolution: 4km nominal, displayed at 10km Timeframe: 1998-Jun 2022 Notes: This product relies on reflectance in the visible spectrum and does not perform well at Southern Hemisphere high latitudes during periods of low light (April to August) or where there is dense sea ice cover. Name: Monthly Chlorophyll Concentration Anomalies Desc: Anomalies in the monthly chlorophyll concentration are calculated relative to the 1998-2020 average for that month. Product: Ocean Colour Climate Change Initiative (Sathyendranath et al. 2019) Source: Ocean Colour Resolution: 4km nominal, displayed at 10km Timeframe: 1998-Jun 2022 Notes: This product relies on reflectance in the visible spectrum and does not perform well at Southern Hemisphere high latitudes during periods of low light (April to August) or where there is dense sea ice cover. Name: Monthly Sea Surface Temperature Desc: Sea Surface Temperature (SST) is an estimate of the water temperature at the ocean surface calculated from satellite and surface observations. Product: Met Office OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis) Near Real Time (Good et al. 2020) Source: Copernicus Resolution: 0.05° × 0.05° Nominal Timeframe: Oct 1981 to present Notes: There is limited validation of SST remote sensing datasets in the marginal ice zone and close to Antarctica. SST within polynyas and within areas of low concentration sea ice are included but not well validated. Where SST under high concentration sea ice cannot be calculated from observations, it defaults to -1.8°C. Name: Monthly Sea Surface Temperature Anomalies Desc: Anomalies in the monthly Sea Surface Temperature (SST) are calculated relative to the 1981 to 2010 average for that month. Product: Met Office OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis) Near Real Time (Good et al. 2020) Source: Copernicus Resolution: 0.05° × 0.05° Nominal Timeframe: Oct 1981 to present Notes: There is limited validation of SST remote sensing datasets in the marginal ice zone and close to Antarctica. SST within polynyas and within areas of low concentration sea ice are included but not well validated. Where SST under high concentration sea ice cannot be calculated from observations, it defaults to -1.8°C. Name: International Bathymetric Chart of the Southern Ocean Version 2 (IBCSO v2) (Dorschel et al. 2022) Desc: Bathymetry is a measure of the depth of the ocean, provided for the areas south of 50° S. The product chosen is a digital bathymetric model for the area south of 50° S with special emphasis on the bathymetry of the Southern Ocean. The total data coverage of the seafloor is 23.79% with a multibeam-only data coverage of 22.32%. The remaining 1.47% include singlebeam and other data. Product: International Bathymetric Chart of the Southern Ocean Version 2 (IBCSO v2) (Dorschel et al. 2022) Source: Pangaea Resolution: 5km nominal Timeframe: Released 2022 Notes: The source product is provided at 500m nominal resolution, which has been regridded to the 5km nominal resolution displayed. See the metadata record "AAS_4506_NILAS_SOFTWARE" for information about the Nilas software package.

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, 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.

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Clipped Version

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

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High-definition version

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

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Macrons

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

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Digital data

Digital boundary data became freely available on 1 July 2007.

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 3 (SA3) is a new output geography, introduced in 2023, that allows aggregations of population data between the SA2 geography and territorial authority geography.

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This dataset is the definitive version of the annually released statistical area 3 (SA3) boundaries as at 1 January 2023 as defined by Stats NZ. This version contains 929 SA3s, including 4 non-digitised SA3s.

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The SA3 geography aims to meet three purposes:

1. approximate suburbs in major, large, and medium urban areas,

2. in predominantly rural areas, provide geographical areas that are larger in area and population size than SA2s but smaller than territorial authorities,

3. minimise data suppression.

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SA3s in major, large, and medium urban areas were created by combining SA2s to approximate suburbs as delineated in the Fire and Emergency NZ (FENZ) Localities dataset. Some of the resulting SA3s have very large populations.

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Outside of major, large, and medium urban areas, SA3s generally have populations of 5,000–10,000. These SA3s may represent either a single small urban area, a combination of small urban areas and their surrounding rural SA2s, or a combination of rural SA2s.

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Zero or nominal population SA3s

To minimise the amount of unsuppressed data that can be provided in multivariate statistical tables, SA2s with fewer than 1,000 residents are combined with other SA2s wherever possible to reach the 1,000 SA3 population target. However, there are still a number of SA3s with zero or nominal populations.

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Small population SA2s designed to maintain alignment between territorial authority and regional council geographies are merged with other SA2s to reach the 5,000–10,000 SA3 population target. These merges mean that some SA3s do not align with regional council boundaries but are aligned to territorial authority.

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Small population island SA2s are included in their adjacent land-based SA3.

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Island SA2s outside territorial authority or region are the same in the SA3 geography.

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Inland water SA2s are aggregated and named by territorial authority, as in the urban rural classification.

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Inlet SA2s are aggregated and named by territorial authority or regional council where the water area is outside the territorial authority.

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Oceanic SA2s translate directly to SA3s as they are already aggregated to regional council.

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The 16 non-digitised SA2s are aggregated to the following 4 non-digitised SA3s (SA3 code; SA3 name):

70001; Oceanic outside region, 70002; Oceanic oil rigs, 70003; Islands outside region, 70004; Ross Dependency outside region.

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SA3 numbering and naming

Each SA3 is a single geographic entity with a name and a numeric code. The name refers to a suburb,recognised place name, or portion of a territorial authority. In some instances where place names are the same or very similar, the SA3s are differentiated by their territorial authority, for example, Hillcrest (Hamilton City) and Hillcrest (Rotorua District).

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SA3 codes have five digits. North Island SA3 codes start with a 5, South Island SA3 codes start with a 6 and non-digitised SA3 codes start with a 7. They are numbered approximately north to south within their respective territorial authorities. When first created in 2023, the last digit of each code was 0. When SA3 boundaries change in future, only the last digit of the code will change to ensure the north-south pattern is maintained.

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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.

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