Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 2.

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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
• Cisgender and transgender status – census usually resident population count aged 15 years and over
• 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.

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

This statistic displays the result of a survey conducted on individuals in Norway in 2018 on access to tablets, divided by household types. The group which had highest rate of access to tablets (** percent) were people of age of ** or higher who lived with children from 0-12 years.

This dataset reflects is for the Individual Shelter & Rescue Statistics that were reported in 2018 for the 2017 Calendar year. Although PACFA requires this data to be submitted and takes all care possible to ensure the validity of this data, we do not control, and therefore guarantee, the complete accuracy, completeness and availability of data. PACFA believes this information to be within ± 4% margin of error. The CDA-PACFA is not responsible for any issues that may arise from the use of this data.

From 2018 to 2019, around 2.03 million people in the U.S. had binge eating disorder, while around another 2.42 million had other specified feeding and eating disorders. This statistic illustrates the number of individuals in the United States with eating disorders from 2018 to 2019, by condition.

Persons with a Disability as a Percentage of All Population by Age Group, Sex, CensusYear and Statistic

View data using web pages

Download .px file (Software required)

This lookup table relates to the web service 2018 Census individual part 2 by SA1. The web service contains data from the 2018 Census only, no data from previous censuses has been included.

The individual (part 2) dataset is displayed by statistical area 1 geography and contains information on: • Religious affiliation (total responses) • Cigarette smoking behaviour • Difficulty seeing even if wearing glasses • Difficulty hearing even if using a hearing aid • Difficulty walking or climbing steps • Difficulty remembering or concentrating • Difficulty washing all over or dressing • Difficulty communicating using your usual language for example being understood by others • Legally registered relationship status • Partnership status in current relationship • Individual home ownership • Number of children born • Highest qualification • Study participation • Total personal income (grouped), including median total personal income • Sources of personal income (total responses) • Main means of travel to education, by usual residence address (2018 only) • Main means of travel to education, by educational institution address (2018 only)

The data uses fixed random rounding to protect confidentiality. Some counts of less than 6 are suppressed according to 2018 confidentiality rules. Values of ‘-999’ indicate suppressed data, and values of ‘Null’ indicate data not collected.

For further information on this dataset please refer to the Statistical area 1 dataset for 2018 Census webpage - footnotes for individual part 2, Excel workbooks, and CSV files are available to download. Data quality ratings for 2018 Census variables, summarising the quality rating and priority levels for 2018 Census variables, are available.

For information on the statistical area 1 geography please refer to the Statistical standard for geographic areas 2018.

EQA75 - Individuals who experienced discrimination in accessing/using transport services. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Individuals who experienced discrimination in accessing/using transport services...

The Individual Statistics by Tax Filing Method (ISTFM) tables present statistics on the filing method of taxfilers grouped by certain demographic and economic characteristics. The data in the tables are extracted from personal income tax returns that were processed for tax year 2016.

The number of individuals involved in indigenous religions and neo-paganism in Finland increased from 2014 onwards. During the period under survey, no individuals involved in indigenous religions and neo-paganism were reported until 2014. In 2023, 63 individuals identified themselves as belonging to an indigenous religion and neo-paganism.

This dataset contains replication files for "A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples" by Raj Chetty and John Friedman. For more information, see https://opportunityinsights.org/paper/differential-privacy/. A summary of the related publication follows. Releasing statistics based on small samples – such as estimates of social mobility by Census tract, as in the Opportunity Atlas – is very valuable for policy but can potentially create privacy risks by unintentionally disclosing information about specific individuals. To mitigate such risks, we worked with researchers at the Harvard Privacy Tools Project and Census Bureau staff to develop practical methods of reducing the risks of privacy loss when releasing such data. This paper describes the methods that we developed, which can be applied to disclose any statistic of interest that is estimated using a sample with a small number of observations. We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic’s maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the Opportunity Atlas. We also provide a step-by-step guide and illustrative Stata code to implement our approach.

WBB47 - Percentage of individuals receiving help from others. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Percentage of individuals receiving help from others...

How often do Swedes paint or draw? In 2019, 31 percent of individuals interviewed stated to have drawn or painted in the past twelve months. The share of respondents was highest in 2015 and 2017, with 32 percent of Swedes.

This statistic displays the projected number of individuals with sight loss in the United Kingdom (UK) from 2010 to 2050. In 2010 approximately 1.9 million individuals had problems with sight loss, this is expected to more than double by 2050.

Italy Vital Statistics: Immigration: Male: Oth Countries data was reported at 199,940.000 Person in 2017. This records an increase from the previous number of 169,091.000 Person for 2016. Italy Vital Statistics: Immigration: Male: Oth Countries data is updated yearly, averaging 178,632.000 Person from Dec 2002 (Median) to 2017, with 16 observations. The data reached an all-time high of 256,939.000 Person in 2007 and a record low of 109,722.000 Person in 2002. Italy Vital Statistics: Immigration: Male: Oth Countries data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Italy – Table IT.G005: Vital Statistics: By Region and Sex: Annual.

Register in the Eastern District of Chiayi City in 2017, and calculate the average distribution of total income among families in each neighborhood; the monthly income of each individual does not exceed the standard minimum living expenses for the year, meeting the criteria of low-income households, those in the lower tier of low-income households, and individuals over 65 living alone.

While sample sizes in elite sports are necessarily small, so are the effects that may be relevant. This conundrum is complicated by an understandable reluctance of athletes to comply with extensive study requirements. In Bayesian analyses, pre-existing knowledge (e.g., from sub-elite trials) can be formally included to supplement scarce data. Moreover, some design specifics for small sample research extend to the extreme case of a single subject. This provides the basis for actionable feedback (e.g., about individual responses) thereby incentivising participation. As a proof-of-concept, we conducted a replicated cross-over trial on the effect of cold-water immersion (CWI) on sprint performance recovery in soccer players. Times for 30 m linear sprint and the initial 5 m section, respectively, were measured by light gates before and 24 h after induction of fatigue. Data were analysed by Bayesian and by standard frequentist methods. Informative priors are based on a published metaanalysis. Seven players completed the trial. Sprint performance was 4.156 ± 0.193 s for 30 m linear sprint and 0.978 ± 0.064 s for the initial 5 m section. CWI improved recovery of sprint time for the initial 5 m section (difference to control: −0.060 ± 0.060 s, p = 0.004) but not for the full 30 m sprint (0.002 ± 0.115 s, p = 0.959), with general agreement between Bayesian and frequentist interval estimates. On the individual level, relevant differences between analytical approaches were present for most players. Changes in the two performance measures are correlated (p = 0.009) with a fairly good reproducibility of individual response patterns. Bayesian analyses with informative priors may be a practicable and meaningful option particularly for very small samples and when the analytical aim is decision making (use / don't use in the specific setting) rather than generalizable inference.

Age_GroupsAge - 4 CategoriesThis table provides Census 2021 estimates that classify People by Age - 4 Categories at Census 2021 Data Zone level. The table contains 15,120 counts.National Statistics ThemeCensus 2021CoverageCensus 2021 Data ZoneContact nameCensus Customer ServicesContact number028 9025 5156Contact emailcensus@nisra.gov.ukNational Statistics DataYesResponsible StatisticianDr David MarshallYear of data2021VariablesAge - 4 CategoriesDescription of dataThis table provides Census 2021 estimates that classify People by Age - 4 Categories at Census 2021 Data Zone level. The table contains 15,120 counts.AbstractThe census collected information on the usually resident population of Northern Ireland on Census Day (21 March 2021).Initial contact letters or questionnaire packs were delivered to every household and communal establishment, and residents were asked to complete online or return the questionnaire with information as correct on Census Day.Special arrangements were made to enumerate special groups such as students, members of the Travellers Community, HM Forces personnel etc.The Census Coverage Survey (an independent doorstep survey) followed between 12 May and 29 June 2021 and was used to adjust the census counts for under-enumeration.Disclosure control methodsStatistical disclosure control (SDC) refers to a range of methods that aim to protect individuals, households, businesses, and their attributes from being identified in published information.NISRA has taken steps to ensure that the confidentiality of respondents is fully protected.All published results from the census have been subject to statistical processes to ensure that individuals cannot be identified. These processes may result in very marginal differences between tables for the same statistic.For Census 2021, NISRA is applying two strategies - targeted record swapping (TRS) and cell key perturbation (CKP), to ensure individuals are protected from identification while minimising the impact on the quality of results.Disclosure control methodologyFor more information, please refer to:Statistical disclosure control methodologyMethodologyThe census questionnaire including the questions asked and the administrative procedures involved in collecting the census data underwent substantial testing. Coding of the data was subject to quality checks.The quality of the results was improved by the use of edit and imputation procedures for missing or incorrect data, and the data were adjusted for over and under-enumeration.The outputs reflect the complete usually-resident population of Northern Ireland.Methodology overviewFurther information on the methodology used in Census 2021 is available in the:Census 2021 methodology overviewQuality issuesThe census results underwent an extensive quality assurance process, which included checks against administrative data sources and information on particular groups such as students and HM Forces personnel.Edit procedures were applied to obviously incorrect responses (such as someone aged 180) and were designed to correct the mistake by making the least possible change to the data.Imputation procedures were applied to missing data on a returned questionnaire, and drew on responses to the question from people with similar characteristics.Quality assurance reportFurther information on the quality assurance processes used in Census 2021 is available in the:Census 2021 quality assurance reportStatement about data qualityFor more information on data quality, including response rate and item response rate, please refer to the:Census 2021 statement about data qualityGeographic referencingIrish National GridNational Statistics publicationCensus statistics are produced by the Northern Ireland Statistics and Research Agency free from political influence and have been assessed as National Statistics by the Office for Statistics Regulation.Office for Statistics RegulationMore information is available on the following web site:Office for Statistics RegulationProducing census statisticsCensus 2021 statistics meet the highest standards of trust, quality and value and are produced using standards set out in the statutory Code of Practice for Statistics.Code of Practice for StatisticsMore information is available in the:Code of Practice for StatisticsDate of publicationJune 2023Further informationCensus 2021 results webpage

The Bath and North East Somerset Council has one of the largest databases in the world on the production and trade of minerals. The dataset contains annual production statistics by mass for more than 70 mineral commodities covering the majority of economically important and internationally-traded minerals, metals and mineral-based materials. For each commodity the annual production statistics are recorded for individual countries, grouped by continent. Import and export statistics are also available for years up to 2002. Maintenance of the database is funded by the Science Budget and output is used by government, private industry and others in support of policy, economic analysis and commercial strategy. As far as possible the production data are compiled from primary, official sources. Quality assurance is maintained by participation in such groups as the International Consultative Group on Non-ferrous Metal Statistics. Individual commodity and country tables are available for sale on request.