Nominal Median and Nominal Mean Income Measures by National Income Definition, Year and Statistic

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The nominal unit labour cost (NULC) index is defined as the ratio of labour cost to labour productivity, where labour cost is the ratio of compensation of employees (current prices) to hours worked by employees, and labour productivity is the ratio of gross domestic product (at market prices in millions, chain-linked volumes reference year 2015) to total hours worked.

The input data are obtained through official transmissions of national accounts' country data in the ESA 2010 transmission programme.

The data are expressed as % change on previous year and as index 2015=100.

Cross sectional data, all countries for the statistic Nominal_Exchange_Rate_3_Year_Change_In_Percent. Indicator Definition:Nominal Exchange Rate 1 Year Change In Percent. The Exchange Rate is defined according to the Quantity Notation, that is, foreign currency (here always the USD) per domestic currency (for example the euro for Germany). Hence, a higher value means, that the domestic currency appreciated as more foreign currency units can be purchased for one unit of domestic currency.Indicator Unit:The statistic is measured in Percent.Descriptive Statistics regarding the Indicator "Nominal Exchange Rate 3 Year Change In Percent":The number of countries with data stands at: 153 countries.The average value across those countries stands at: -4.50.The standard deviation across those countries stands at: 25.54.The lowest value stands at: -98.32, and was observed in Lebanon (LBP), which in this case constitutes the country that ranks last.The highest value stands at: 36.51, and was observed in Albania (ALL), which in this case constitutes the country that ranks first.Looking at countries with values, the top 5 countries are:1. Albania, actual value 36.51, actual ranking 1.2. Costa Rica, actual value 36.30, actual ranking 2.3. Afghanistan, actual value 24.75, actual ranking 3.4. Poland, actual value 23.95, actual ranking 4.5. Sri Lanka, actual value 20.48, actual ranking 5.Looking at countries with values, the bottom 5 countries are:1. Lebanon, actual value -98.32, actual ranking 153.2. Venezuela, RB, actual value -94.88, actual ranking 152.3. Iran, Islamic Rep., actual value -93.01, actual ranking 151.4. Argentina, actual value -89.58, actual ranking 150.5. South Sudan, actual value -89.00, actual ranking 149.

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). Integrated Global Radiosonde Archive is a digital data set archived at the former National Climatic Data Center (NCDC), now National Centers for Environmental Information (NCEI). This dataset contains monthly means of geopotential height, temperature, zonal wind, and meridional wind derived from the Integrated Global Radiosonde Archive (IGRA). IGRA consists of radiosonde and pilot balloon observations at over 1500 globally distributed stations, and monthly means are available for the surface and mandatory levels at many of these stations. The period of record varies from station to station, with many extending from 1970 to 2016. Monthly means are computed separately for the nominal times of 0000 and 1200 UTC, considering data within two hours of each nominal time. A mean is provided, along with the number of values used to calculate it, whenever there are at least 10 values for a particular station, month, nominal time, and level.

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...

SAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North. Retrieved mixing ratios of nitrous oxide (N2O) and methane (CH4) are averaged over day and night, along with errors, at 31 pressure levels between 50 and 0.125 mbar. Because the N2O and CH4 channels cannot function simultaneously, only one type of measurement is made for any nominal day. The data were recovered from the original magnetic tapes, and are now stored online as one file in its original proprietary binary format.The data for this product are available from 1 January 1979 through 30 December 1981. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.This product was previously available from the NSSDC with the identifier ESAD-00180 (old ID 78-098A-02C).

Time series data for the statistic Nominal Residential Property Price Index Quarterly and country Finland. Indicator Definition:Nominal Residential Property Price Index QuarterlyThe indicator "Nominal Residential Property Price Index Quarterly" stands at 107.03 as of 06/30/2025. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -1.37 percent compared to the value the year prior.The 1 year change in percent is -1.37.The 3 year change in percent is -11.45.The 5 year change in percent is -4.65.The 10 year change in percent is 0.0996.The Serie's long term average value is 61.26. It's latest available value, on 06/30/2025, is 74.70 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 03/31/1970, to it's latest available value, on 06/30/2025, is +1,486.88%.The Serie's change in percent from it's maximum value, on 06/30/2022, to it's latest available value, on 06/30/2025, is -11.45%.

Time series data for the statistic Nominal Residential Property Price Index Quarterly and country Greece. Indicator Definition:Nominal Residential Property Price Index QuarterlyThe indicator "Nominal Residential Property Price Index Quarterly" stands at 115.20 as of 6/30/2025, the highest value at least since 6/30/1997, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 7.31 percent compared to the value the year prior.The 1 year change in percent is 7.31.The 3 year change in percent is 35.27.The 5 year change in percent is 60.29.The 10 year change in percent is 75.66.The Serie's long term average value is 79.23. It's latest available value, on 6/30/2025, is 45.40 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/1997, to it's latest available value, on 6/30/2025, is +186.88%.The Serie's change in percent from it's maximum value, on 6/30/2025, to it's latest available value, on 6/30/2025, is 0.0%.

The unit labour cost (ULC) is defined as the ratio of labour costs to labour productivity. Nominal ULC (NULC) = (D1/EEM) / (B1GQ/ETO) with D1 = Compensation of employees, all industries, current prices EEM = Employees, all industries, in persons (domestic concept) B1GQ = Gross domestic product at market prices in millions, chain-linked volumes reference year 2015 ETO = Total employment, all industries, in persons (domestic concept) The input data are obtained through official transmissions of national accounts' country data in the ESA 2010 transmission programme. The data are expressed as % change on previous year and as index 2015=100.

We present an exploratory method for discovering likely misconceptions from multiple-choice concept test data, as well as preliminary evidence that this method recovers known misconceptions from real student responses. Our procedure is based on a Bayesian implementation of the Multidimensional Nominal Categories IRT model (MNCM) combined with standard factor-analytic rotation methods; by analyzing student responses at the level of individual distractors rather than at the level of entire questions, this approach is able to highlight multiple likely misconceptions for subsequent investigation without requiring any manual labeling of test content. We explore the performance of the Bayesian MNCM on synthetic data and find that it is able to recover multidimensional item parameters consistently at achievable sample sizes. These studies demonstrate the method's robustness to overfitting and ability to perform automatic dimensionality assessment and selection. The method also compares favorably to existing IRT software implementing marginal maximum likelihood estimation which we use as a validation benchmark. We then apply our method to approximately 10,000 students' responses to a research-designed concept test: the Force Concept Inventory. In addition to a broad first dimension strongly correlated with overall test score, we discover thirteen additional dimensions which load on smaller sets of distractors; we discuss two as examples, showing that these are consistent with already-known misconceptions in Newtonian mechanics. While work remains to validate our findings, our hope is that future applications of this method could aid in the refinement of existing concept inventories or the development of new ones, enable the discovery of previously-unknown student misconceptions across a variety of disciplines, and—by leveraging the method's ability to quantify the prevalence of particular misconceptions—provide opportunities for targeted instruction at both the individual and classroom level.

We present an exploratory method for discovering likely misconceptions from multiple-choice concept test data, as well as preliminary evidence that this method recovers known misconceptions from real student responses. Our procedure is based on a Bayesian implementation of the Multidimensional Nominal Categories IRT model (MNCM) combined with standard factor-analytic rotation methods; by analyzing student responses at the level of individual distractors rather than at the level of entire questions, this approach is able to highlight multiple likely misconceptions for subsequent investigation without requiring any manual labeling of test content. We explore the performance of the Bayesian MNCM on synthetic data and find that it is able to recover multidimensional item parameters consistently at achievable sample sizes. These studies demonstrate the method's robustness to overfitting and ability to perform automatic dimensionality assessment and selection. The method also compares favorably to existing IRT software implementing marginal maximum likelihood estimation which we use as a validation benchmark. We then apply our method to approximately 10,000 students' responses to a research-designed concept test: the Force Concept Inventory. In addition to a broad first dimension strongly correlated with overall test score, we discover thirteen additional dimensions which load on smaller sets of distractors; we discuss two as examples, showing that these are consistent with already-known misconceptions in Newtonian mechanics. While work remains to validate our findings, our hope is that future applications of this method could aid in the refinement of existing concept inventories or the development of new ones, enable the discovery of previously-unknown student misconceptions across a variety of disciplines, and—by leveraging the method's ability to quantify the prevalence of particular misconceptions—provide opportunities for targeted instruction at both the individual and classroom level.

The nominal unit labour cost (NULC) index is defined as the ratio of labour cost to labour productivity, where labour cost is the ratio of compensation of employees (current prices) to hours worked by employees, and labour productivity is the ratio of gross domestic product (at market prices in millions, chain-linked volumes reference year 2015) to total hours worked. Data on employment are presented according to the domestic concept used in national accounts. Input data are obtained from the official national accounts' country data, through ESA 2010 transmission programme.

Real interest rates describe the growth in the real value of the interest on a loan or deposit, adjusted for inflation. Nominal interest rates on the other hand show us the raw interest rate, which is unadjusted for inflation. If the inflation rate in a certain country were zero percent, the real and nominal interest rates would be the same number. As inflation reduces the real value of a loan, however, a positive inflation rate will mean that the nominal interest rate is more likely to be greater than the real interest rate. We can see this in the recent inflationary episode which has taken place in the wake of the Coronavirus pandemic, with nominal interest rates rising over the course of 2022, but still lagging far behind the rate of inflation, meaning these rate rises register as smaller increases in the real interest rate.

Overview This public data was provided by Divvy, a Chicago-based bike share company through the Google Data Analytics Professional Certificate at Coursera. It includes data between August 2022 and July 2023. The Divvy Data License Agreement can be found here. The following CSV files are found in this dataset:

• 202208-divvy-tripdata.csv
• 202209-divvy-oublictripdata.csv
• 20220-divvy-tripdata.csv
• 202211-divvy-tripdata.csv
• 202212-divvy-tripdata.csv
• 202301-divvy-tripdata.csv
• 202302-divvy-tripdata.csv
• 202303-divvy-tripdata.csv
• 202304-divvy-tripdata.csv
• 202305-divvy-tripdata.csv
• 202306-divvy-tripdata.csv
• 202307-divvy-tripdata.csv

Data Dictionary

Context

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Content

Attribute Information:

InvoiceNo:

Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation.

StockCode:

Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product.

Description:

Product (item) name. Nominal.

Quantity:

The quantities of each product (item) per transaction. Numeric.

InvoiceDate:

Invice date and time. Numeric. The day and time when a transaction was generated.

UnitPrice:

Unit price. Numeric. Product price per unit in sterling .

CustomerID:

Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer.

Country:

Country name. Nominal. The name of the country where a customer resides.

Acknowledgements

Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018.

Time series data for the statistic Nominal Residential Property Price Index Quarterly and country Luxembourg. Indicator Definition:Nominal Residential Property Price Index QuarterlyThe indicator "Nominal Residential Property Price Index Quarterly" stands at 211.88 as of 6/30/2025, the highest value since 9/30/2023. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.56 percent compared to the value the year prior.The 1 year change in percent is 4.56.The 3 year change in percent is -9.87.The 5 year change in percent is 14.19.The 10 year change in percent is 70.02.The Serie's long term average value is 145.80. It's latest available value, on 6/30/2025, is 45.32 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/2007, to it's latest available value, on 6/30/2025, is +133.67%.The Serie's change in percent from it's maximum value, on 9/30/2022, to it's latest available value, on 6/30/2025, is -11.70%.

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]

The unit labour cost (ULC) is defined as the ratio of labour costs to labour productivity. Nominal ULC (NULC) = (D1/EEM) / (B1GM/ETO) with D1 = Compensation of employees, all industries, current prices EEM = Employees, all industries, in persons (domestic concept) B1GM = Gross domestic product at market prices in millions, chain-linked volumes reference year 2010 ETO = Total employment, all industries, in persons (domestic concept) The input data are obtained through official transmissions of national accounts' country data in the ESA 2010 transmission programme.

Sources:

German Central Bank (ed.), 1975: Deutsches Geld- und Bankwesen in Zahlen 1876 – 1975. (German monetary system and banking system in numbers 1876 – 1975) German Central Bank (ed.), different years: monthly reports of the German Central Bank, statistical part, interest rates German Central Bank (ed.), different years: Supplementary statistical booklets for the monthly reports of the German Central Bank 1959 – 1992, security statistics Reich Statistical Office (ed.), different years: Statistical yearbook of the German empire Statistical Office (ed.), 1985: Geld und Kredit. Index der Aktienkurse (Money and Credit. Index of share prices) – Lange Reihe; Fachserie 9, Reihe 2. Statistical Office (ed.), 1987: Entwicklung der Nahrungsmittelpreise von 1800 – 1880 in Deutschland. (Development of food prices in Germany 1800 – 1880) Statistical Office (ed.), 1987: Entwicklung der Verbraucherpreise (Development of consumer prices) seit 1881 in Deutschland. (Development of consumer prices since 1881 in Germany) Statistical Office (ed.), different years: Fachserie 17, Reihe 7, Preisindex für die Lebenshaltung (price index for costs of living) Donner, 1934: Kursbildung am Aktienmarkt; Grundlagen zur Konjunkturbeobachtung an den Effektenmärkten. (Prices on the stock market; groundwork for observation of economic cycles on the stock market) Homburger, 1905: Die Entwicklung des Zinsfusses in Deutschland von 1870 – 1903. (Development of the interest flow in Germany, 1870 – 1903) Voye, 1902: Über die Höhe der verschiedenen Zinsarten und ihre wechselseitige Abhängigkeit.(On the values of different types of interests and their interdependence).

Refer to the 'Current Geographic Boundaries Table' layer 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,

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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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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.

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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