Context

I'm creating a new website in which I need this type of data. I didn't found it easily available as I had to scrape it from an interactive graph, so now I upload it here for everyone

Content

In this dataset you can find real and nominal gold prices since 1791 to 2020. The explanation of the differences between real and nominal prices are:

· Nominal values are the current monetary values. · Real values are adjusted for inflation and show prices/wages at constant prices. · Real values give a better guide to what you can actually buy and the opportunity costs you face.

Example of real vs nominal:

· If you receive an 8% increase in your wages from £100 to £108, this is the nominal increase. · However, if inflation is 2%, then the real increase in wages is (8-2%) 6%. · The real wage is a better guide to how your living standards changes. It shows what you are actually able to buy with the extra increase in wages. · If wages increased 80%, but inflation was also 80%, the real increase in wages would be 0% – in effect, despite the monetary increase in wages of 80%, the amount of goods and services you could buy would be the same.

Hope this dataset is useful for you! Any questions or answers do not hesitate in contact me.

Data and probability for an incomplete 2×2 table.

The coverage probability (CP) and the expected length (EL) of various 95% CIs.

This archive contains a unique collection of naturalistic child language data collected between 2017 and 2020 in Southern Senegal. The deposit contains ELAN files of annotated data based on recordings of children's production and child directed speech in naturalistic settings. The language under investigation is Eegimaa, a Jóola language of sourthern Senegal. This is part of the Atlantic branch of the Niger‑Congo Phylum. The data was collected as part of a research project which investigates the acquisition of an Atlantic noun class system. Our research looks at the factors underlying children’s learning of nominal class prefixes and syntactic and semantic agreement at the level of the NP.

We focus on questions including the following.

• Which elements of noun class morphology do children begin to use productively?

• What is the role of input frequency, morphological salience, and transparency in children acquisition of noun class and agreement in Eegimaa?

• Are errors in the production of nominal class prefixes also reflected in children’s use of the corresponding agreement markers?

Theoretical accounts of the strategies used by children to learn the structures of words and grammatical features of languages differ considerably, but our knowledge of what is possible is limited by the existing focus on a relatively small number of languages associated with industrialised nations. Here, we will investigate grammatical features and structures that may be expressed in a variety of different ways. Examples of grammatical features include number, e.g. the distinction between singular and plural, or gender, e.g. distinguishing masculine and feminine in languages like French, features expressed within the shape of the word and associated items. Grammatical structure may be manifested in agreement across the separate words of a noun phrase (e.g. The cat purrs, where the -s on 'purrs' shows agreement with cat, indicating that there is only one cat.) This project investigates the acquisition of inflectional morphology, i.e., grammatical features and structures as reflected in the word forms and associated agreement, in Gújjolaay Eegimaa, a language of the Atlantic family of the Niger Congo phylum spoken in Southern Senegal. This language has a gender system of the type traditionally known as a noun class system. Noun class systems with complex gender agreement are characteristic of the Niger-Congo languages. In Eegimaa nouns use prefixes to form singular and plural. For example ba- is the singular marker for ba-ginh 'chest', but its plural marker is u- as in u-ginh 'chests'. Nouns which have the same singular prefix, e.g. ba-, can form their plural with a different marker (e.g., bá-jur 'young woman', plural sú-jur 'young women'). Eegimaa has a complex morphological system of gender and number marking which is also reflected in its agreement system. Current knowledge as to how children acquire gender/noun class marking and agreement is based entirely on the Bantu languages of the Niger Congo family. There are no studies available of Atlantic languages, which, though similar to Bantu in some ways, also have important differences. Here we will investigate the influence of the three factors found to affect children's acquisition of noun class morphology and agreement, namely: i) Input frequency, according to which the forms that children hear the most will tend to be acquired first ii) Perceptual salience, according to which more salient forms such as stressed syllables will tend to be acquired first, and iii) Morphological transparency, according to which forms whose meanings are easily determined will tend to be acquired more easily than those whose meanings are more obscure. Our study will build on findings on the acquisition of Bantu noun class systems, and will aim to answer questions such as the following. What strategies do children rely on to learn complex language structure? What is the role of adult input language in the acquisition of morphology and agreement in Eegimaa? How do children cope with variation in language input from their caregivers? In what order do they learn the different noun class markers? We will carry out a longitudinal study in which we will observe over three years the interactions of five children aged from about 2 to 4 years with their caregivers. Among Eegimaa speakers, caregivers include children's parents, older siblings and other members of the community. Children's daytime activities mostly take place outside their homes. We will record children's output speech on audio and video and compare the data with child-directed speech from adults and with adult-directed speech (interactions between adults), collected as part of a previous project. We will also carry out a cross-sectional study by twice observing the speech of ten additional children at two points, at ages 3 and 4 years. These studies together will provide both an in-depth look and a broader overview of the...

The average nominal salary in Russia was measured at ****** Russian rubles per month in 2024, marking an increase of roughly ****** Russian rubles compared to the previous year. After the currency redenomination and the financial default in 1998, the average wage levels in the country have grown exponentially. Who gets paid more in Russia? The Russian oil and gas industry paid the highest average wage to their employees, at ******* Russian rubles between January and September 2021. Salaries in management and management consulting were the second-highest, followed by air transportation and software development. On average, men earned more than women across all industries in the country. For example, in the information and communications sector, the average wage of a male worker amounted to nearly ******* Russian rubles, compared to under ****** Russian rubles for a female worker. Economic inequality in Russia The national income distribution of Russian households shows a high concentration of income and wealth in the hands of few individuals. In 2021, the mean income of the top one percent exceeded ******* euros before income tax, compared to ***** euros earned by the bottom 50 percent of the population. Furthermore, the richest one percent in Russia held an average wealth of over *** billion euros, whereas the personal wealth of the bottom 50 percent was measured at ***** euros in the same year. However, the income gap was forecast to decrease in Russia, with the Gini index expected to decline to **** by 2029.

Key information about Philippines Nominal GDP

• Philippines Nominal GDP reached 112.5 USD bn in Dec 2022, compared with 93.0 USD bn in the previous quarter.
• Nominal GDP in Philippines is updated quarterly, available from Mar 1981 to Dec 2022, with an average number of 22.7 USD bn.
• The data reached an all-time high of 112.5 USD bn in Dec 2022 and a record low of 7.9 USD bn in Sep 1984.

• In the latest reports, Philippines GDP expanded 7.1 % YoY in Dec 2022.
• Its GDP deflator (implicit price deflator) increased 6.3 % in Dec 2022.
• Philippines GDP Per Capita reached 3,623.3 USD in Dec 2022.
• Its Gross Savings Rate was measured at 10.8 % in Dec 2022.
• For Nominal GDP contributions, Investment accounted for 21.5 % in Dec 2022.
• Public Consumption accounted for 13.8 % in Dec 2022.
• Private Consumption accounted for 75.4 % in Dec 2022.

Dataset Description This dataset consists of academic and demographic information about 300 students from a university, which can be used for predicting academic outcomes, such as probation status. The dataset was simulated to represent a variety of student attributes across multiple categories like personal data, academic history, and other related information. The primary goal of this dataset is to analyze factors contributing to academic performance and identify students at risk of probation.

Column Descriptions Student No.: (Numeric) A unique identifier for each student. In this dataset, each student has a different ID number, making it a 100% unique column. Cohort: (Numeric) The year a student enrolled in the university. No missing values and consistent across the dataset. College: (Nominal) The name of the college the student belongs to. Examples include "Engineering," "Science," etc. No missing values. College Code: (Nominal) A numerical or alphanumerical code representing the college. This is an alternative representation of the "College" column. Major: (Nominal) The major field of study of the student. Some missing values (23%) represent students who haven’t declared a major or are in an undeclared status. Major Code: (Nominal) A code representing the major subject. Similar to the "Major" column, this has 23% missing values due to undeclared majors. Minor: (Nominal) The minor subject, if any, chosen by the student. This column has a high percentage of missing data (91%) since most students do not have minors. Spec: (Nominal) Specialization within the major field of study. Like the "Minor" column, this has 93% missing data as most students do not declare a specialization. Degree: (Numeric) The type of degree the student is pursuing (e.g., Bachelor's). In this dataset, all students are pursuing the same degree, so there are no missing values. Status: (Nominal) The current academic standing of the student (e.g., "Active," "Inactive"). No missing values. Load Status: (Nominal) The academic load status (e.g., "Full-time," "Part-time"). This column has very few missing values (1%). Gender: (Nominal) The gender of the student (e.g., "Male," "Female"). No missing values. Country: (Nominal) The country of origin of the student. Only 2 missing values, making it nearly complete. Governorate: (Nominal) The administrative region (governorate) the student comes from. This column has a small percentage of missing values (1%). Wellayah: (Nominal) The district or locality within the governorate. Around 1% of the data is missing. CGPA: (Numeric) The cumulative grade point average (CGPA) of the student. This field has 145 missing values, representing students without available CGPA records. Estimated Graduation Year: (Numeric) The expected year in which the student will graduate. No missing values. From HEAC: (Nominal) Indicates whether the student was admitted through the Higher Education Admission Center (HEAC). This column has 4% missing values. Admission Category: (Nominal) The category of admission (e.g., scholarship, self-funded). This column has a significant amount of missing data (98%), indicating that admission category data is either unavailable or irrelevant for most students. Birth Date: (Nominal) The birth date of the student. The dataset includes very few missing values (0%) and has been replaced by the derived feature "Age." Actual Graduation Date: (Nominal) The actual date on which a student graduates. More than half of the values are missing (54%), representing students who haven’t graduated yet. Withdrawal: (Nominal) Indicates whether the student has withdrawn from the university. This column has 89% missing data since the majority of students haven’t withdrawn. Marital Status: (Nominal) The marital status of the student (e.g., "Single," "Married"). No missing values. SQU Hostel: (Nominal) Indicates whether the student lives in the university hostel. No missing values. Percentage (Secondary School Score): (Nominal) The student’s percentage score from secondary school. No missing values. Probation Student: (Nominal) Indicates whether the student is under academic probation. This is the target variable for classification, with no missing values.

Record Details Total Records: 300 Total Attributes: 26 Missing Values: Some columns have a significant proportion of missing data (e.g., Minor, Spec, Major Code), while others have very few or no missing values (e.g., Gender, Cohort, College). Missing values were handled using a placeholder for clarity in certain columns.

This example describes the calibration of a conventional mass of a weight W against a reference weight R with a nominal mass of 100 g. The example builds on that given in JCGM 101:2008. This time a Bayesian evaluation of the measurement is performed. A Bayesian approach differs from the Monte Carlo method (MCM) of JCGM 101:2008 and the law of propagation of uncertainty (LPU) in JCGM 100:2008 in that it combines prior knowledge about the measurand with the data obtained during calibration. From the joint posterior probability density function which is obtained from this combination, a value and a coverage interval for the measurand are obtained.

Files contained in the dataset are:

- EMUEActivity113_MassCalibration.pdf: report “Bayesian approach applied to the mass calibration example in JCGM 101:2008”;

- EMUEActivity113_MassCalibration.tex: LaTeX source file to be compiled in order to produce EMUEActivity113_MassCalibration.pdf;

- Compendium.bib: bibliography file;

- conjugateBayesKnownV.pdf: image contained in the report;

- MCMvsBayesNI.pdf: image contained in the report;

- JCGM101_Mass_calibration_code.R : R code to run the example from the report.

It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

### On-/Off-Axis Data Release
#### (Version 1.0.1, dated 2024/08/12)

This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below:

+ `onoffaxis_xsec_data.root`
This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README.

+ `flux_analysis.root`
This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix).

+ `xsec_data_mc.csv`
The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row.

+ `cov_matrix.csv` and `inv_matrix.csv`
The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file.

+ `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv`
The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins.

+ `calc_chisq.cxx`
This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`.

+ `calc_chisq.py`
This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv`

+ ND280 angle bin numbering
- 0: `-1.0 < cos(#theta) < 0.20`
- 1: `0.20 < cos(#theta) < 0.60`
- 2: `0.60 < cos(#theta) < 0.70`
- 3: `0.70 < cos(#theta) < 0.80`
- 4: `0.80 < cos(#theta) < 0.85`
- 5: `0.85 < cos(#theta) < 0.90`
- 6: `0.90 < cos(#theta) < 0.94`
- 7: `0.94 < cos(#theta) < 0.98`
- 8: `0.98 < cos(#theta) < 1.00`

+ INGRID angle bin numbering
- 0: `0.50 < cos(#theta) < 0.82`
- 1: `0.82 < cos(#theta) < 0.94`
- 2: `0.94 < cos(#theta) < 1.00`

### Changelog

#### v1.0.1
Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.

We include the course syllabus used to teach quantitative research design and analysis methods to graduate Linguistics students using a blended teaching and learning approach. The blended course took place over two weeks and builds on a face to face course presented over two days in 2019. Students worked through the topics in preparation for a live interactive video session each Friday to go through the activities. Additional communication took place on Slack for two hours each week. A survey was conducted at the start and end of the course to ascertain participants' perceptions of the usefulness of the course. The links to online elements and the evaluations have been removed from the uploaded course guide.Participants who complete this workshop will be able to:- outline the steps and decisions involved in quantitative data analysis of linguistic data- explain common statistical terminology (sample, mean, standard deviation, correlation, nominal, ordinal and scale data)- perform common statistical tests using jamovi (e.g. t-test, correlation, anova, regression)- interpret and report common statistical tests- describe and choose from the various graphing options used to display data- use jamovi to perform common statistical tests and graph resultsEvaluationParticipants who complete the course will use these skills and knowledge to complete the following activities for evaluation:- analyse the data for a project and/or assignment (in part or in whole)- plan the results section of an Honours research project (where applicable)Feedback and suggestions can be directed to M Schaefer schaemn@unisa.ac.za

This series was created to provide access to inward correspondence located in VPRS 8892 for the period 1919 to 1973. Although VPRS 8892 formally ended in 1971, the agency continued to create contract files under this system. Hence cards were created to index the contract files during the period 1971 to early 1973.

The index cards are arranged alphabetically by name of correspondent or organisation within each year. Divider cards indicating the alphabetical divisions are posted throughout the index.

The information recorded on the Index includes ;

Name of the correspondent
Date of letter
Registration number of the letter
Subject heading of the group under which the letter was filed
Remarks, for example, if the file was destroyed or top numbered into the subsequent multiple file numbering system.

United Kingdom Nominal Average Weekly Earnings: sa: Total Pay (TP): Whole Economy data was reported at 716.000 GBP in Feb 2025. This records an increase from the previous number of 711.000 GBP for Jan 2025. United Kingdom Nominal Average Weekly Earnings: sa: Total Pay (TP): Whole Economy data is updated monthly, averaging 461.000 GBP from Jan 2000 (Median) to Feb 2025, with 302 observations. The data reached an all-time high of 716.000 GBP in Feb 2025 and a record low of 299.809 GBP in Feb 2000. United Kingdom Nominal Average Weekly Earnings: sa: Total Pay (TP): Whole Economy data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.G083: Average Weekly Earnings: Seasonally Adjusted: SIC 2007 . Labour Force Estimates are shown for the mid-month of the three-month average time periods. For example, estimates for January to March 2012 are shown as 'February 2012', estimates for February to April 2012 are shown as 'March 2012', etc. [COVID-19-IMPACT]

These data represent 6 hourly and 12 hourly instantaneous values of selected variables for ENSEMBLES (http://www.ensembles-eu.org). The list of output variables can be found in: http://ensembles.wdc-climate.de/output-variables

The 1PCTTO4X simulation (included year 2150) was initiated from nominal year 1970 of preindustriel run,when equilibrium was reached (corresponds to nominal year 1860 of CO2-quadrupling experiment). Forcing agents included: CO2, CH4, N2O, O3, CFC11 (including other CFCs and HFCs), CFC12; sulfate(Boucher), BC, sea salt, desert dust aerosols.

These datasets are available in netCDF format. The dataset names are composed of - centre/model acronym (e.g. CNCM3: CNRM/CM3) - scenario acronym (e.g. SRA1B: SRES A1B) - run number (e.g. 1: run 1) - time interval (MM:monthly mean, DM:daily mean, DC:diurnal cycle, 6H:6 hourly, 12h:12hourly) - variable acronym with level value --> example: CNCM3_SRA1B_1_MM_hur850

Technical data to this experiment: CNRM-CM3 (2004): atmosphere: Arpege-Climat v3 (T42L45, cy 22b+); ocean: OPA8.1; sea ice: Gelato 3.10; river routing: TRIP

These data represent daily values (daily mean, instantaneous daily output, diurnal cycle) of selected variables for ENSEMBLES (http://www.ensembles-eu.org). The list of output variables can be found in: http://ensembles.wdc-climate.de/output-variables

The SRES-B1 simulation(included year 2100) was initiated from nominal year 2000 of 20C3M run1. It corresponds to nominal year 2000 of SRES-B1 experiment. Forcing agents included: CO2,CH4,N2O,O3,CFC11(including other CFCs and HFCs),CFC12; sulfate(Boucher),BC,sea salt,desert dust aerosols. This 550 ppm stabilization experiment continued until 2300 with all concentrations fixed at their levels of year 2100.

These datasets are available in netCDF format. The dataset names are composed of - centre/model acronym (e.g. CNCM3: CNRM/CM3) - scenario acronym (e.g. SRB1: SRES B1) - run number (e.g. 1: run 1) - time interval (MM:monthly mean, DM:daily mean, DC:diurnal cycle, 6H:6 hourly, 12h:12hourly) - variable acronym with level value --> example: CNCM3_SRB1_1_MM_hur850

Technical data to this experiment: CNRM-CM3 (2004): atmosphere: Arpege-Climat v3 (T42L45, cy 22b+); ocean: OPA8.1; sea ice: Gelato 3.10; river routing: TRIP

United Kingdom Nominal Average Weekly Earnings: sa: Bonus Pay (BP): Whole Economy data was reported at 31.581 GBP in Sep 2018. This records a decrease from the previous number of 31.594 GBP for Aug 2018. United Kingdom Nominal Average Weekly Earnings: sa: Bonus Pay (BP): Whole Economy data is updated monthly, averaging 26.253 GBP from Jan 2000 (Median) to Sep 2018, with 225 observations. The data reached an all-time high of 40.836 GBP in Apr 2013 and a record low of 12.800 GBP in Feb 2000. United Kingdom Nominal Average Weekly Earnings: sa: Bonus Pay (BP): Whole Economy data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.G049: Average Weekly Earnings: Seasonally Adjusted: SIC 2007 . Labour Force Estimates are shown for the mid-month of the three-month average time periods. For example, estimates for January to March 2012 are shown as 'February 2012', estimates for February to April 2012 are shown as 'March 2012', etc.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

Nominal technical terms (NTTs), as crucial builders for disciplinary knowledge, can cause difficulties for students. However, previous studies have rarely associated NTTs with disciplinary knowledge construction. Apart from that, scholars of English for Specific Purposes (ESP) have mainly focused on making wordlists for one specific discipline based on corpora rather than on meanings. Moreover, the current categorization of technical terms cannot reveal their role in constructing disciplinary knowledge. Against this backdrop, we carried out a corpus-based study to classify NTTs in secondary school biology textbooks and to unveil the knowledge constructed by different types of those NTTs. As a result, we found that NTTs in those textbooks could fall into five major categories: Thing, Activity, Semiotic, Place, and Time. We also found intra-disciplinary differences in NTT distributions. The lexicogrammatical analysis indicates that the five types of NTTs can construct different knowledge. With the findings, we put forward implications for teaching biology in secondary schools.

It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

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.

​

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’.

​

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