This dataset is about books and is filtered where the book is The Cambridge dictionary of statistics, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

The tables presents indices (2005=100) and changes on twelve months previously (%) of production, turnover and orders in industry (excl. construction), by sector of industry.

Data available : January 2000 till December 2012

Table has been discontinued as from 22 March 2013 due to change of the base year from 2005 to 2010. Statistics Netherlands has started a new table, Industry; production, sales and orders, changes and index (2010 = 100). For more information see sections 3 and 4.

Status of the figures: Production: three most recent months: provisional. The figures within a reporting year are revised provisional figures until publication in December of the year concerned. Turnover: three most recent months: provisional. Orders: three most recent months: provisional.

Changes as of 8 July 2011. Due to new regulations (European System for National Accounts, 2010, Balance of Payments Manual 6) for National Accounts and Balance of Payment, the turnover definition has been adapted. This results in adjustments in production index and other short term statistics. The adaptation of the turnover definition is related to a change in registration of enterprises that (partially) contract out their production abroad. The adjustment means that goods dealt with by foreign subsidiaries of Dutch parent companies do count for Dutch production. Goods dealt with in the Netherlands by Dutch subsidiaries of foreign parent companies that remain property of these parent companies do no longer count as Dutch production. However, they count as export of services for the sum that has been added to value in the Netherlands. Until December 2009, index figures for manufacturing turnover are based on the previous turnover definition. From January 2010 onwards, the turnover figures are based on the new turnover definition. Therefore, turnover changes 2010 on 2009 are not accurate.

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

This dataset is the definitive version of the annually released statistical area 1 (SA1) boundaries as at 1 January 2025, as defined by Stats NZ. This version contains 33,164 SA1s (33,148 digitised and 16 with empty or null geometries (non-digitised)).

SA1 is an output geography that allows the release of more low-level data than is available at the meshblock level. Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables.

The SA1 should:

form a contiguous cluster of one or more meshblocks,

be either urban, rural, or water in character,

be small enough to:

• allow flexibility for aggregation to other statistical geographies,

• allow users to aggregate areas into their own defined communities of interest,

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

• be built from meshblocks,

• either define or aggregate to define SA2s, urban rural areas, territorial authorities, and regional councils.

SA1s generally have a population of 100–200 residents, with some exceptions:

• SA1s with nil or nominal resident populations are created to represent remote mainland areas, unpopulated islands, inland water, inlets, or oceanic areas.

• Some SA1s in remote rural areas and urban industrial or business areas have fewer than 100 residents.

• Some SA1s that contain apartment blocks, retirement villages, and large non-residential facilities (prisons, boarding schools, etc.) have more than 500 residents.

SA1 numbering

SA1s are not named. SA1 codes have seven digits starting with a 7 and are numbered approximately north to south. Non-digitised codes start with 79.

As new SA1s are created, they are given the next available numeric code. If the composition of an SA1 changes through splitting or amalgamating different meshblocks, the SA1 is given a new code. The previous code no longer exists within that version and future versions of the SA1 classification.

Digitised and non-digitised SA1s

The digital geographic boundaries are defined and maintained by Stats NZ.

Aggregated from meshblocks, SA1s cover the land area of New Zealand, the water area to the 12-mile limit, the Chatham Islands, Kermadec Islands, sub-Antarctic islands, off-shore oil rigs, and Ross Dependency. The following 16 SA1s are held in non-digitised form.

7999901; New Zealand Economic Zone, 7999902; Oceanic Kermadec Islands,7999903; Kermadec Islands, 7999904; Oceanic Oil Rig Taranaki,7999905; Oceanic Campbell Island, 7999906; Campbell Island, 7999907; Oceanic Oil Rig Southland, 7999908; Oceanic Auckland Islands, 7999909; Auckland Islands, 7999910; Oceanic Bounty Islands, 7999911; Bounty Islands, 7999912; Oceanic Snares Islands, 7999913; Snares Islands, 7999914; Oceanic Antipodes Islands, 7999915; Antipodes Islands, 7999916; Ross Dependency.

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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

The Environment Statistics Database contains selected water and waste statistics by country. Statistics on water and waste are based on official statistics supplied by national statistical offices and/or ministries of environment (or equivalent institutions) in countries in response to the biennial UNSD/UNEP Questionnaire on Environment Statistics. They were complemented by data on EU and OECD member and partner countries from OECD and Eurostat. Environment statistics is still in an early stage of development in many countries, and data are often sparse. The statistics selected here are those of relatively good quality and geographic coverage. The online database currently covers the years 1990, 1995 to 2009. For information on definitions, data quality and other important metadata, please check UNSD Environmental Indicator tables.Last update in UNdata: 19 Sep 2011Next update in UNdata: Jul 2013

This dataset is the definitive set of annually released statistical area 1 (SA1) boundaries for 2022 as defined by Stats NZ (the custodian). This version contains 29,913 SA1 features.

SA1s were introduced as part of the Statistical Standard for Geographic Areas 2018 (SSGA18) which replaced the New Zealand Standard Areas Classification (NZSAC92). SA1 is an output geography that allows the release of more detailed information about population characteristics than is available at the meshblock level.

Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables. SA1s either define or aggregate to define SA2s, urban rural areas, territorial authorities, and regional councils. Some SA1s that contain apartment blocks, retirement villages, and large non-residential facilities have more than 500 residents.

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

Digital boundary data became freely available on 1 July 2007.

The SA1 classification can also be downloaded from the Stats NZ classification and concordance tool Ariā.

LScDC Word-Category RIG MatrixApril 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk / suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny MirkesGetting StartedThis file describes the Word-Category RIG Matrix for theLeicester Scientific Corpus (LSC) [1], the procedure to build the matrix and introduces the Leicester Scientific Thesaurus (LScT) with the construction process. The Word-Category RIG Matrix is a 103,998 by 252 matrix, where rows correspond to words of Leicester Scientific Dictionary-Core (LScDC) [2] and columns correspond to 252 Web of Science (WoS) categories [3, 4, 5]. Each entry in the matrix corresponds to a pair (category,word). Its value for the pair shows the Relative Information Gain (RIG) on the belonging of a text from the LSC to the category from observing the word in this text. The CSV file of Word-Category RIG Matrix in the published archive is presented with two additional columns of the sum of RIGs in categories and the maximum of RIGs over categories (last two columns of the matrix). So, the file ‘Word-Category RIG Matrix.csv’ contains a total of 254 columns.This matrix is created to be used in future research on quantifying of meaning in scientific texts under the assumption that words have scientifically specific meanings in subject categories and the meaning can be estimated by information gains from word to categories. LScT (Leicester Scientific Thesaurus) is a scientific thesaurus of English. The thesaurus includes a list of 5,000 words from the LScDC. We consider ordering the words of LScDC by the sum of their RIGs in categories. That is, words are arranged in their informativeness in the scientific corpus LSC. Therefore, meaningfulness of words evaluated by words’ average informativeness in the categories. We have decided to include the most informative 5,000 words in the scientific thesaurus. Words as a Vector of Frequencies in WoS CategoriesEach word of the LScDC is represented as a vector of frequencies in WoS categories. Given the collection of the LSC texts, each entry of the vector consists of the number of texts containing the word in the corresponding category.It is noteworthy that texts in a corpus do not necessarily belong to a single category, as they are likely to correspond to multidisciplinary studies, specifically in a corpus of scientific texts. In other words, categories may not be exclusive. There are 252 WoS categories and a text can be assigned to at least 1 and at most 6 categories in the LSC. Using the binary calculation of frequencies, we introduce the presence of a word in a category. We create a vector of frequencies for each word, where dimensions are categories in the corpus.The collection of vectors, with all words and categories in the entire corpus, can be shown in a table, where each entry corresponds to a pair (word,category). This table is build for the LScDC with 252 WoS categories and presented in published archive with this file. The value of each entry in the table shows how many times a word of LScDC appears in a WoS category. The occurrence of a word in a category is determined by counting the number of the LSC texts containing the word in a category. Words as a Vector of Relative Information Gains Extracted for CategoriesIn this section, we introduce our approach to representation of a word as a vector of relative information gains for categories under the assumption that meaning of a word can be quantified by their information gained for categories.For each category, a function is defined on texts that takes the value 1, if the text belongs to the category, and 0 otherwise. For each word, a function is defined on texts that takes the value 1 if the word belongs to the text, and 0 otherwise. Consider LSC as a probabilistic sample space (the space of equally probable elementary outcomes). For the Boolean random variables, the joint probability distribution, the entropy and information gains are defined.The information gain about the category from the word is the amount of information on the belonging of a text from the LSC to the category from observing the word in the text [6]. We used the Relative Information Gain (RIG) providing a normalised measure of the Information Gain. This provides the ability of comparing information gains for different categories. The calculations of entropy, Information Gains and Relative Information Gains can be found in the README file in the archive published. Given a word, we created a vector where each component of the vector corresponds to a category. Therefore, each word is represented as a vector of relative information gains. It is obvious that the dimension of vector for each word is the number of categories. The set of vectors is used to form the Word-Category RIG Matrix, in which each column corresponds to a category, each row corresponds to a word and each component is the relative information gain from the word to the category. In Word-Category RIG Matrix, a row vector represents the corresponding word as a vector of RIGs in categories. We note that in the matrix, a column vector represents RIGs of all words in an individual category. If we choose an arbitrary category, words can be ordered by their RIGs from the most informative to the least informative for the category. As well as ordering words in each category, words can be ordered by two criteria: sum and maximum of RIGs in categories. The top n words in this list can be considered as the most informative words in the scientific texts. For a given word, the sum and maximum of RIGs are calculated from the Word-Category RIG Matrix.RIGs for each word of LScDC in 252 categories are calculated and vectors of words are formed. We then form the Word-Category RIG Matrix for the LSC. For each word, the sum (S) and maximum (M) of RIGs in categories are calculated and added at the end of the matrix (last two columns of the matrix). The Word-Category RIG Matrix for the LScDC with 252 categories, the sum of RIGs in categories and the maximum of RIGs over categories can be found in the database.Leicester Scientific Thesaurus (LScT)Leicester Scientific Thesaurus (LScT) is a list of 5,000 words form the LScDC [2]. Words of LScDC are sorted in descending order by the sum (S) of RIGs in categories and the top 5,000 words are selected to be included in the LScT. We consider these 5,000 words as the most meaningful words in the scientific corpus. In other words, meaningfulness of words evaluated by words’ average informativeness in the categories and the list of these words are considered as a ‘thesaurus’ for science. The LScT with value of sum can be found as CSV file with the published archive. Published archive contains following files:1) Word_Category_RIG_Matrix.csv: A 103,998 by 254 matrix where columns are 252 WoS categories, the sum (S) and the maximum (M) of RIGs in categories (last two columns of the matrix), and rows are words of LScDC. Each entry in the first 252 columns is RIG from the word to the category. Words are ordered as in the LScDC.2) Word_Category_Frequency_Matrix.csv: A 103,998 by 252 matrix where columns are 252 WoS categories and rows are words of LScDC. Each entry of the matrix is the number of texts containing the word in the corresponding category. Words are ordered as in the LScDC.3) LScT.csv: List of words of LScT with sum (S) values. 4) Text_No_in_Cat.csv: The number of texts in categories. 5) Categories_in_Documents.csv: List of WoS categories for each document of the LSC.6) README.txt: Description of Word-Category RIG Matrix, Word-Category Frequency Matrix and LScT and forming procedures.7) README.pdf (same as 6 in PDF format)References[1] Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2[2] Suzen, Neslihan (2019): LScDC (Leicester Scientific Dictionary-Core). figshare. Dataset. https://doi.org/10.25392/leicester.data.9896579.v3[3] Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4] WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [5] Suzen, N., Mirkes, E. M., & Gorban, A. N. (2019). LScDC-new large scientific dictionary. arXiv preprint arXiv:1912.06858. [6] Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3), 379-423.

The Core Based Statistical Areas boundaries were defined by OMB based on the 2010 Census, and the dataset was updated on August 09, 2019 from the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan and Micropolitan Statistical Areas are together termed Core Based Statistical Areas (CBSAs) and are defined by the Office of Management and Budget (OMB) and consist of the county or counties or equivalent entities associated with at least one urban core (urbanized area or urban cluster) of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. Categories of CBSAs are: Metropolitan Statistical Areas, based on urbanized areas of 50,000 or more population; and Micropolitan Statistical Areas, based on urban clusters of at least 10,000 population but less than 50,000 population. The CBSA boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2018.

The High Definition Video Transmitter market is emerging as a pivotal component in the broadcasting, telecommunications, and multimedia industries, driven by the increasing demand for high-quality video content across various platforms. These devices play a critical role in transmitting high-definition video signals

This table contains 11 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.

This service shows the percentage of population, excluding institutional residents, with knowledge of English and French for Canada by 2016 census division. The data is from the Census Profile, Statistics Canada Catalogue no. 98-316-X2016001. Knowledge of official languages refers to whether the person can conduct a conversation in English only, French only, in both languages or in neither language. For a child who has not yet learned to speak, this includes languages that the child is learning to speak at home. For additional information refer to 'Knowledge of official languages' in the 2016 Census Dictionary. For additional information refer to 'Knowledge of official languages' in the 2016 Census Dictionary. To have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census division” web service, accessible in the data resources section below.

Through a cooperative agreement, RTI International worked with the Bureau of Justice Statistics (BJS) to create public-use files of victimization data for the 52 largest metropolitan statistical areas (MSAs) covering the 2000-2015 survey years. The National Crime Victimization Survey (NCVS) is one of two national indicators of crime in the U.S. Historically, NCVS estimates of crime were not available at the state or local level because, prior to 2016, the NCVS sample was designed to exclusively produce national estimates. It is important to be able to understand victimization and victimization risk at the local level to inform and improve crime prevention efforts, investigation and victim response practices, and the location and mix of victim services. To protect respondent confidentiality, with a few exceptions, subnational identifiers are traditionally not included on NCVS public-use files. Instead, information required to conduct analyses of crime at subnational levels must be accessed through a Federal Statistical Research Data Center (FSRDC) by obtaining Special Sworn Status from the U.S. Census Bureau. To provide a greater number of analysts with access to NCVS subnational data, in 2007 the Bureau of Justice Statistics (BJS) released a public-use file containing person- and incident-level data from 1979-2004 for the "core" counties (i.e., self-representing PSUs) within the 40 largest metropolitan statistical areas (MSAs). To accommodate interest from analysts and other interested parties in updating the file with more recent data, BJS has created public-use files for the 52 largest MSAs covering the 2000-2015 survey years. The 52 MSAs included on these files are those with a 2015 population of 1 million or more persons and an average annual NCVS sample size of at least 250 persons during the period of 2006-2015. While some of the MSAs from these files were also included on the 1979-2004 version, the definitions used to define MSAs are not the same. The 1979-2004 files were based on the "core" counties that were common to the MSA definitions determined by the Office of Management and Budget (OMB) for the 1970-, 1980-, and 1990-based NCVS sample designs. For the current files (i.e., 2000-2015), MSA definitions are based on the most recent delineation files available from OMB at the time of data collection for each survey year included on the files.

Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 1.

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The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated).

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The variables for part 1 of the dataset are:

• Census usually resident population count
• Census night population count
• Age (5-year groups)
• Age (life cycle groups)
• Median age
• Birthplace (NZ born/overseas born)
• Birthplace (broad geographic areas)
• Ethnicity (total responses) for level 1 and ‘Other Ethnicity’ grouped by ‘New Zealander’ and ‘Other Ethnicity nec’
• Māori descent indicator
• Languages spoken (total responses)
• Official language indicator
• Gender
• Sex at birth
• Rainbow/LGBTIQ+ indicator for the census usually resident population count aged 15 years and over
• Sexual identity for the census usually resident population count aged 15 years and over
• Legally registered relationship status for the census usually resident population count aged 15 years and over
• Partnership status in current relationship for the census usually resident population count aged 15 years and over
• Number of children born for the sex at birth female census usually resident population count aged 15 years and over
• Average number of children born for the sex at birth female census usually resident population count aged 15 years and over
• Religious affiliation (total responses)
• Cigarette smoking behaviour for the census usually resident population count aged 15 years and over
• Disability indicator for the census usually resident population count aged 5 years and over
• Difficulty communicating for the census usually resident population count aged 5 years and over
• Difficulty hearing for the census usually resident population count aged 5 years and over
• Difficulty remembering or concentrating for the census usually resident population count aged 5 years and over
• Difficulty seeing for the census usually resident population count aged 5 years and over
• Difficulty walking for the census usually resident population count aged 5 years and over
• Difficulty washing for the census usually resident population count aged 5 years and over.

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Download lookup file for part 1 from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Te Whata

Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Study participation time series

In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Disability indicator

This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.

Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.

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Symbol

-997 Not available

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Hand transcribed content from the United States Bureau of Labour Statistics Dictionary of Titles (DoT). The DoT is a record of occupations and a description of the tasks performed. Five editions exist from 1939, 1949, 1965, 1977 and 1991. The DoT was replaced by O*NET structured data on jobs, workers and their characteristics. However, apart from the 1991 data, the data in the DoT is not easily ingestible, existing only in scalar PDF documents. Attempts at Optical Character Recognition led to low accuracy. For that reason we present here hand transcribed textual data from these documents. Various data are available for each occupation e.g. numerical codes, references to other occupations as well as the free text description. For that reason the data for each edition is presented in 'long' format with a variable number of lines, with a blank line between occupations. Consult the transcription instructions for more details. Structured meta-data (see here) on occupations is also available for the 1965, 1977 and 1991 editions. For the 1965, 1977 and 1991 editions, this data can be extracted from the numerical codes with the occupational entries, the key for these codes is found in the 1965 edition in separate tables exist which were transcribed. The instructions provided to transcribers for this edition are also added to the repository. The original documents are freely available in PDF format (e.g. here) This data accompanies the paper 'Longitudinal Complex Dynamics of Labour Markets Reveal Increasing Polarisation' by Althobaiti et al

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..The "Employed" and "Unemployment rate" columns refer to the civilian population. For more information, see the ACS Subject Definitions..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police.

The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public.

The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose.

Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.

The number of unique victims recorded in Victoria, and demographic characteristics of victims.

Data Classification - http://www.crimestatistics.vic.gov.au/home/about+the+data/classifications/

Glossary and Data Dictionary - http://www.crimestatistics.vic.gov.au/home/about+the+data/data+dictionary/

List of forms of state statistical observations that are collected and subject to publication in accordance with the requirements of the legislation

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police. The CSA aims to provide an efficient and …Show full descriptionThe Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police. The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public. The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose. Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria. The number and rate of recorded offences in Victoria. Data Classification - http://www.crimestatistics.vic.gov.au/home/about+the+data/classifications/ Glossary and Data Dictionary - http://www.crimestatistics.vic.gov.au/home/about+the+data/data+dictionary/

Refer to the current geographies boundaries table for a list of all current geographies and recent updates. This dataset is the definitive set of statistical area 1 (SA1) boundaries concorded to higher geographies as at 1 January 2025. This version contains 33,164 SA1s, including 16 with empty or null geometries (non-digitised SA1s). SA1 is an output geography that allows the release of more detailed information about population characteristics than is available at the meshblock level. Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables. This SA1 higher geographies 2025 file is a correspondence, or concordance, which relates SA1s to larger geographic areas or 'higher geographies'. The higher geographies contained in this concordance are: statistical area 2 (SA22025), statistical area 3 (SA32025), urban rural (UR2025), and urban rural indicator (IUR2025), urban accessibility indicator (IUA), functional urban area (FUA), indicator functional urban area (IFUA) and functional urban area type (TFUA), territorial authority (TA2025), and regional council (REGC2025). The geography urban accessibility indicator (IUA) was first published in 2020 and added to this concordance in 2022. ​ 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