56 datasets found
  1. a

    Percentage of Population aged 16-19 in School and/or Employed

    • hub.arcgis.com
    • data.baltimorecity.gov
    • +1more
    Updated Mar 6, 2020
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    Baltimore Neighborhood Indicators Alliance (2020). Percentage of Population aged 16-19 in School and/or Employed [Dataset]. https://hub.arcgis.com/maps/7d814d51552b45e2bfe17e4edf417131
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    Dataset updated
    Mar 6, 2020
    Dataset authored and provided by
    Baltimore Neighborhood Indicators Alliance
    Area covered
    Description

    The percentage of persons aged 16 to 19 who are in school and/or are employed out of all persons in their age cohort. Please note: due to the nature of this indicator, do not compare changes over time. This indicator can only be used as a point in time "snapshot". For more information, please visit the U.S. Census page on Comparing ACS Datahttps://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

  2. S

    2023 Census main means of travel to education by statistical area 2

    • datafinder.stats.govt.nz
    csv, dbf (dbase iii) +4
    Updated Mar 30, 2025
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    Stats NZ (2025). 2023 Census main means of travel to education by statistical area 2 [Dataset]. https://datafinder.stats.govt.nz/table/121971-2023-census-main-means-of-travel-to-education-by-statistical-area-2/
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    mapinfo mif, dbf (dbase iii), csv, geodatabase, mapinfo tab, geopackage / sqliteAvailable download formats
    Dataset updated
    Mar 30, 2025
    Dataset provided by
    Statistics New Zealandhttp://www.stats.govt.nz/
    Authors
    Stats NZ
    License

    https://datafinder.stats.govt.nz/license/attribution-4-0-international/https://datafinder.stats.govt.nz/license/attribution-4-0-international/

    Description

    Dataset shows an individual’s statistical area 2 (SA2) of usual residence and the SA2 of their place of study, for the census usually resident population count who are studying (part time or full time), by main means of travel to education from the 2018 and 2023 Censuses.

    The main means of travel to education categories are:

    • Study at home

    • Drive a car, truck, or van

    • Passenger in a car, truck, or van

    • Bicycle

    • Walk or jog

    • School bus

    • Public bus

    • Train

    • Ferry

    • Other.

    Main means of travel to education is the usual method a person used to travel the longest distance to their place of study.

    Educational institution address is the physical location of the individual’s place of study. Educational institutions include early childhood education, primary school, secondary school, and tertiary education institutions. For individuals who study at home, their educational institution address is the same as their usual residence address.

    Educational institution address is coded to the most detailed geography possible from the available information. This dataset only includes travel to education information for individuals whose educational institution address is available at SA2 level. The sum of the counts for each region in this dataset may not equal the census usually resident population count who are studying (part time or full time) for that region. Educational institution address – 2023 Census: Information by concept has more information.

    This dataset can be used in conjunction with the following spatial files by joining on the SA2 code values:

    Download data table using the instructions in the Koordinates help guide.

    Footnotes

    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.

    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. 

    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. 

    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).

    Educational institution address time series

    Educational institution address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Educational institution address – 2023 Census: Information by concept has more information.

    Rows excluded from dataset

    Rows show SA2 of usual residence by SA2 of educational institution address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA2-SA2 combinations have commuter flows.

    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.

    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.

    Quality rating of a variable

    The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

    Main means of travel to education quality rating

    Main means of travel to education is rated as moderate quality.

    Main means of travel to education – 2023 Census: Information by concept has more information, for example, definitions and data quality.

    Educational institution address quality rating

    Educational institution address is rated as moderate quality.

    Educational institution address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

    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”.

    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.

    Percentages

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

    Symbol

    -999 Confidential

    Inconsistencies in definitions

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

  3. User's Manual for 1970 Census Fourth Count (Population): School District...

    • icpsr.umich.edu
    ascii
    Updated May 28, 2004
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    United States Department of Education. National Center for Education Statistics (2004). User's Manual for 1970 Census Fourth Count (Population): School District Data Tape [Dataset]. http://doi.org/10.3886/ICPSR03525.v1
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    asciiAvailable download formats
    Dataset updated
    May 28, 2004
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States Department of Education. National Center for Education Statistics
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/3525/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/3525/terms

    Time period covered
    1970
    Area covered
    United States
    Description

    The 1970 Census School District Data Tape (SDDT) User's Guide was designed to complement the 1970 Census User's Guide prepared by the United States Census Bureau. The School District Data Tape (SDDT) created by the National Center for Education Statistics is a recompilation of the 1970 Census Fourth Count Population data, providing data tables for each school district in the country with 300 or more students. The preparation of the School District Data Tape required three major steps: (1) overlaying school district boundaries on census maps, (2) creating a geo-reference tape indicating the percent of each census area falling within each school district, and (3) merging the geo-reference tape with the 1970 Census Fourth Count Population Files A (Traced Areas) and B (Minor Civil Divisions). Some of the major uses of the School District Data Tape include: allocation of federal funds, desegregation planning, bilingual and minority special education planning, preschool and child care planning, facility planning, redistricting, urban-suburban-rural analyses, mobility analysis, social and economic inequality among school districts, and school children profiles. In addition to these uses, most state education agencies will find data by school district of value in allocating federal and state aid to school districts and in the evaluation of the inequality of property taxes as a basis for financing elementary and secondary education. The School District Data Tape matches, as closely as possible, the format of the Fourth Count (Population) Summary tapes supplied by the Census Bureau.

  4. 2023 American Community Survey: B14005 | Sex by School Enrollment by...

    • data.census.gov
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    ACS, 2023 American Community Survey: B14005 | Sex by School Enrollment by Educational Attainment by Employment Status for the Population 16 to 19 Years (ACS 1-Year Estimates Detailed Tables) [Dataset]. https://data.census.gov/table/ACSDT1Y2023.B14005
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2023
    Description

    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..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.

  5. Pupil absence in schools in England: autumn 2017 and spring 2018

    • gov.uk
    Updated Oct 18, 2018
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    Department for Education (2018). Pupil absence in schools in England: autumn 2017 and spring 2018 [Dataset]. https://www.gov.uk/government/statistics/pupil-absence-in-schools-in-england-autumn-term-2017-and-spring-term-2018
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    Dataset updated
    Oct 18, 2018
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Education
    Area covered
    England
    Description

    This release provides information on the levels of overall, authorised and unauthorised absence in:

    • state-funded primary schools
    • state-funded secondary schools
    • special schools
    • pupil referral units

    It includes information on:

    • reasons for absence
    • persistent absentees
    • pupil characteristics

    The information is based on pupil level absence data collected via the school census.

    It updates and supplements information published in the May 2018 release - ‘Pupil absence in schools in England, autumn term 2017’.

    A guide on how we produce pupil absence statistics is also available.

    School census statistics team

    Email mailto:schools.statistics@education.gov.uk">schools.statistics@education.gov.uk

    Telephone: Mark Pearson 0370 000 2288

  6. Population Census 2000 - Mauritius

    • dev.ihsn.org
    • datacatalog.ihsn.org
    • +1more
    Updated Apr 25, 2019
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    Statistics Mauritius (2019). Population Census 2000 - Mauritius [Dataset]. https://dev.ihsn.org/nada/catalog/study/MUS_2000_PHC_v01_M
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    Dataset updated
    Apr 25, 2019
    Dataset authored and provided by
    Statistics Mauritiushttp://statsmauritius.govmu.org/
    Time period covered
    2000
    Area covered
    Mauritius
    Description

    Abstract

    A census gives a complete and comprehensive picture of the nation as well as groups of people living in specific areas. In what type of buildings and housing units are we living? What are the amenities and facilities that are available therein? How many rooms are there and what is the extent of overcrowding? How many people live in a given town or locality? How many children are there? How many women are there? How many are old enough to vote? What kind of jobs are we doing? What is our level of education? Do we have the required qualifications or skills to satisfy the needs of the labour market? The census helps to answer these questions and many others.

    It provides up-to-date and disaggregated data on the housing conditions, the spatial distribution, and the demographic and socio-economic characteristics of the population. These data are essential for assessing the country's demographic, social and economic performance and for developing sound policies and programmes aimed at fostering the welfare of the country and its population.

    Census data are also useful to business, industrial and commercial organisations to estimate and forecast demand for their products and services, and to assess the supply of manpower with the relevant skills to run their activities.

    Furthermore, census data are used in the derivation of many important and meaningful social indicators that are needed by local and international organizations. Thus, many social indicators, as defined in the set of indicators recommended by the United Nations Statistics Division, can only be worked out from census data.

    Legal framework Census 2000 was conducted according to provisions of the Statistics Act of 7 April 1951. The underlying procedures are given in Sections 5, 6 and 13 of the Act. In March 1998, the Cabinet agreed to the conduct of a housing and population census in year 2000. In June 1999, it gave its approval to the census dates and to the topics to be investigated. The regulations for the Housing Census, prescribing the particulars and information to be collected, were subsequently prepared and approved by the President in November 1999. The regulations were published as Government Notice 170 of 1999. In December 1999, the President made an order to the effect that a census of the population be taken between 19 June and 16 July 2000 in respect of all persons alive at midnight on 2 July 2000. The Order was gazetted in December 1999. The regulations for the Population Census, prescribing the particulars and information to be collected were approved by the President in April 2000 and published as Government Notice 57 of 2000.

    Geographic coverage

    Housing and population enumerations were conducted on the Islands of Mauritius, Rodrigues and Agalega. As regards St Brandon islands, only a count of persons spending census night on the islands was made, these islands being fishing stations with no resident population.

    Analysis unit

    • Household
    • Individual
    • Housing unit

    Universe

    The Housing Census enumerated all buildings, housing units, households, commercial and industrial establishments, hotels and boarding houses as well as fruit trees of bearing age on residential premises.

    The Population Census enumerated all persons present on census night in all households and communal establishments, as well as usual residents who were away on census night.

    Kind of data

    Census/enumeration data [cen]

    Mode of data collection

    Self administered and face to face

    Research instrument

    Questionnaire Design Consultation with stakeholders from Government Ministries and Departments started in 1998. Heads of Government Ministries and Departments were invited via a circular letter to submit a list of demographic, social and economic data they considered essential for administration, planning and policy-making and which could be collected at the census. The proposals received were discussed at various levels. In the light of these discussions and taking into account recommendations of the United Nations Statistics Division on subject matters that can be investigated at a census, final selection of topics was made at a meeting with subject matter specialists from our parent Ministry.

    The main considerations in the final selection of topics were: - the importance of the topics to the country - the cost for collecting and processing data on a given item - where it was possible by other means to obtain satisfactory information more cheaply, the topic was not selected - the suitability of topics - sensitive and controversial issues as well as questions that were too complicated or difficult for the average respondent to answer were avoided - whether the census was the appropriate method for data collection - topics that required detailed investigation or highly qualified staff were not included since they would be best canvassed by sample surveys.

    Housing Census Questionnaire All topics investigated at the 1990 Census were included in the 2000 Housing Census questionnaire. Three new items were however added. These were: “Availability of domestic water tank/reservoir”, “Principal fuel used in bathroom” and “Fruit trees on premises”.

    The housing census questionnaire was divided into seven parts. A list of topics and items included in the questionnaire is given below: Part I - Location Part II - Type of Building Part III - Characteristics of buildings - Storeys above ground floor
    - Year of completion
    - Principal material of construction used for roof and walls
    Part IV - Characteristics of housing units - Ownership
    - Occupancy
    - Water supply
    - Domestic water tank/reservoir - Availability of electricity
    - Toilet facilities
    - Bathing facilities
    - Availability of kitchen - Refuse disposal Part V - Characteristics of households - Household type - Name and address of head of household - Number of persons by sex - Tenure - Number of rooms for living purposes - Number of rooms for business or profession - Monthly rent - Principal fuel used for cooking - Principal fuel used in bathroom Part VI - Commercial and industrial establishments, hotels and boarding houses - Name and address of establishment or working proprietor/manager - Main activity in which the establishment is engaged - Number of persons engaged at the time of enumeration Part VII - Fruit-trees on premises - Number of fruit trees of bearing age by type

    Population Census Questionnaire The 2000 Population Census questionnaire covered most of the topics investigated at the 1990 Population Census. A question on income was added while the questions on education were reviewed to include qualifications, other than those of the primary and secondary levels, of the respondent. The topic, main activity status of person during the year, which was investigated at the previous census was not included.

    Topics and items included in the population census questionnaire are given below: (i) Location (ii) Names of persons These information were asked only to ensure that all members of the household were enumerated. Also, the listing of names of each person facilitated the checking for accuracy and completeness of each entry at the time of enumeration and later, if errors or missing information still persisted on the form. It should be pointed out that names were not captured at the data entry stage, so that data collected could not be identified with any individual person, in line with the requirements of the Statistics Act. (iii) Demographic and social characteristics - Relationship to head (only one head is allowed for each household) - Sex - Age - Date of birth (This question served as a verification to the age reported earlier) - Citizenship - Marital Status - Religion - Linguistic group - Language usually spoken (iv) Whether disabled or not - Type of disability, if disabled (v) Migration characteristics - Whereabouts on Census night - Usual address - Usual address five years ago (vi) Fertility - For persons not single: - Age at first marriage - Whether married more than once - Number of children ever born (for women only) (vii) Education characteristics - For persons 2 years and above: - Languages read and written - School attendance - Primary and secondary education (viii) Current economic characteristics (ix) Income

    Census Guide and Instructions A census guide and instructions booklet was prepared and distributed to all heads of households. The booklet contained extensive explanations on how to fill in the census form and answered questions that people usually asked about censuses. Thus the objectives of the census, what happened to the census forms once the enumeration was over, the confidential aspect of collected information as well as the usefulness of each item were explained.

    Printing of Census Questionnaires and Guides
    The census questionnaires, and the census guide and instructions booklets were printed by the Government Printer. The numbers printed were as follows: (i) Housing Census questionnaires - 16,000 booklets of 25 questionnaires (ii) Population Census questionnaires - 375,000 (iii) Census guide and instructions booklets - 312,000

    Cleaning operations

    Recruitment and Training of Editors and Coders About 15 clerical officers who were previously engaged in the various units of the Office and 10 newly recruited statistical officers were called on to the editing and coding of the census forms while a request for the services of 50 additional clerical officers was made to the Ministry for Civil Service Affairs and Administrative Reform. Between March 2000 and May 2001, small groups of clerical officers from the ministry joined the

  7. USA 2020 Census Population Characteristics - School Geographies

    • datalibrary-lnr.hub.arcgis.com
    Updated May 26, 2023
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    Esri (2023). USA 2020 Census Population Characteristics - School Geographies [Dataset]. https://datalibrary-lnr.hub.arcgis.com/datasets/esri::usa-2020-census-population-characteristics-school-geographies
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    Dataset updated
    May 26, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This layer shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, School District Unified, School District Elementary, School District Secondary boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, School District Unified, School District Elementary, School District SecondaryNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

  8. p

    Population and Housing Census 2005 - Palau

    • microdata.pacificdata.org
    Updated Aug 18, 2013
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    Office of Planning and Statistics (2013). Population and Housing Census 2005 - Palau [Dataset]. https://microdata.pacificdata.org/index.php/catalog/27
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    Dataset updated
    Aug 18, 2013
    Dataset authored and provided by
    Office of Planning and Statistics
    Time period covered
    2005
    Area covered
    Palau
    Description

    Abstract

    The 2005 Republic of Palau Census of Population and Housing will be used to give a snapshot of Republic of Palau's population and housing at the mid-point of the decade. This Census is also important because it measures the population at the beginning of the implementation of the Compact of Free Association. The information collected in the census is needed to plan for the needs of the population. The government uses the census figures to allocate funds for public services in a wide variety of areas, such as education, housing, and job training. The figures also are used by private businesses, academic institutions, local organizations, and the public in general to understand who we are and what our situation is, in order to prepare better for our future needs.

    The fundamental purpose of a census is to provide information on the size, distribution and characteristics of a country's population. The census data are used for policymaking, planning and administration, as well as in management and evaluation of programmes in education, labour force, family planning, housing, health, transportation and rural development. A basic administrative use is in the demarcation of constituencies and allocation of representation to governing bodies. The census is also an invaluable resource for research, providing data for scientific analysis of the composition and distribution of the population and for statistical models to forecast its future growth. The census provides business and industry with the basic data they need to appraise the demand for housing, schools, furnishings, food, clothing, recreational facilities, medical supplies and other goods and services.

    Geographic coverage

    A hierarchical geographic presentation shows the geographic entities in a superior/subordinate structure in census products. This structure is derived from the legal, administrative, or areal relationships of the entities. The hierarchical structure is depicted in report tables by means of indentation. The following structure is used for the 2005 Census of the Republic of Palau:

    Republic of Palau State Hamlet/Village Enumeration District Block

    Analysis unit

    Individuals Families Households General Population

    Universe

    The Census covered all the households and respective residents in the entire country.

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    Not applicable to a full enumeration census.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The 2005 Palau Census of Population and Housing comprises three parts: 1. Housing - one form for each household 2. Population - one for for each member of the household 3. People who have left home - one form for each household.

    Cleaning operations

    Full scale processing and editing activiities comprised eight separate sessions either with or separately but with remote guidance of the U.S. Census Bureau experts to finalize all datasets for publishing stage.

    Processing operation was handled with care to produce a set of data that describes the population as clearly and accurately as possible. To meet this objective, questionnaires were reviewed and edited during field data collection operations by crew leaders for consistency, completeness, and acceptability. Questionnaires were also reviewed by census clerks in the census office for omissions, certain inconsistencies, and population coverage. For example, write-in entries such as "Don't know" or "NA" were considered unacceptable in certain quantities and/or in conjunction with other data omissions.

    As a result of this review operation, a telephone or personal visit follow-up was made to obtain missing information. Potential coverage errors were included in the follow-up, as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

    Subsequent to field operations, remaining incomplete or inconsistent information on the questionnaires was assigned using imputation procedures during the final automated edit of the collected data. Allocations, or computer assignments of acceptable data in place of unacceptable entries or blanks, were needed most often when an entry for a given item was lacking or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in previous censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in lace of blanks or unacceptable entries enhanced the usefulness of the data.

    Another way to make corrections during the computer editing process is substitution. Substitution is the assignment of a full set of characteristics for a person or housing unit. Because of the detailed field operations, substitution was not needed for the 2005 Census.

    Sampling error estimates

    Sampling Error is not applicable to full enumeration censuses.

    Data appraisal

    In any large-scale statistical operation, such as the 2005 Census of the Republic of Palau, human- and machine-related errors were anticipated. These errors are commonly referred to as nonsampling errors. Such errors include not enumerating every household or every person in the population, not obtaining all required information form the respondents, obtaining incorrect or inconsistent information, and recording information incorrectly. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires.

    To reduce various types of nonsampling errors, a number of techniques were implemented during the planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

  9. a

    2020 Census Designated Places

    • hub.arcgis.com
    • data-ocpw.opendata.arcgis.com
    Updated Nov 5, 2021
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    OC Public Works (2021). 2020 Census Designated Places [Dataset]. https://hub.arcgis.com/datasets/OCPW::redistricting-map-submittals?layer=14
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    Dataset updated
    Nov 5, 2021
    Dataset authored and provided by
    OC Public Works
    Area covered
    Description

    Original census file name: tl_2020_

  10. a

    ABS 2021 Census G16 Highest year of school completed by age by sex by 2021...

    • digital.atlas.gov.au
    Updated Dec 8, 2023
    + more versions
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    Digital Atlas of Australia (2023). ABS 2021 Census G16 Highest year of school completed by age by sex by 2021 LGA [Dataset]. https://digital.atlas.gov.au/datasets/abs-2021-census-g16-highest-year-of-school-completed-by-age-by-sex-by-2021-lga
    Explore at:
    Dataset updated
    Dec 8, 2023
    Dataset authored and provided by
    Digital Atlas of Australia
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This dataset presents information from G16 – Highest year of school completed by age by sex in Australia based on the general community profile from the 2021 Census. It contains characteristics of persons, families, and dwellings by Local Government Areas (LGA), 2021, from the Australian Statistical Geography Standard (ASGS) Edition 3.

    This dataset is part of a set of web services based on the 2021 Census. It can be used as a tool for researching, planning, and analysis. The data is based on place of usual residence (that is, where people usually live, rather than where they were counted on Census night), unless otherwise stated.

    Small random adjustments have been made to all cell values to protect the confidentiality of respondents. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For further information see the 2021 Census Privacy Statement, Confidentiality, and Introduced random error/perturbation.

    Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

    Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

    Data and geography references Source data publication: G16 – Highest year of school completed by age by sex Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: About the Census, 2021 Census product release guide – Community Profiles, Understanding Census geography Source: Australian Bureau of Statistics (ABS)

  11. Pupil absence in schools in England: autumn 2018 and spring 2019

    • gov.uk
    Updated Oct 10, 2019
    + more versions
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    Department for Education (2019). Pupil absence in schools in England: autumn 2018 and spring 2019 [Dataset]. https://www.gov.uk/government/statistics/pupil-absence-in-schools-in-england-autumn-2018-and-spring-2019
    Explore at:
    Dataset updated
    Oct 10, 2019
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Education
    Area covered
    England
    Description

    This release provides information on the levels of overall, authorised and unauthorised absence in:

    • state-funded primary schools
    • state-funded secondary schools
    • special schools
    • pupil referral units

    It includes information on:

    • reasons for absence
    • persistent absentees
    • pupil characteristics

    The information is based on pupil level absence data collected via the school census.

    It updates and supplements information published in the May 2019 release - ‘Pupil absence in schools in England, autumn term 2018’.

    A guide on how we produce pupil absence statistics is also available.

    School census statistics team

    Email mailto:schools.statistics@education.gov.uk">schools.statistics@education.gov.uk

    Telephone: Mark Pearson 0370 000 2288

  12. 2022 American Community Survey: B14005 | Sex by School Enrollment by...

    • data.census.gov
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    ACS, 2022 American Community Survey: B14005 | Sex by School Enrollment by Educational Attainment by Employment Status for the Population 16 to 19 Years (ACS 1-Year Estimates Detailed Tables) [Dataset]. https://data.census.gov/table/ACSDT1Y2022.B14005?q=&t=School%20Enrollment
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    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2022
    Description

    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 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, 2022 American Community Survey 1-Year Estimates.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..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 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..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.

  13. 2019 American Community Survey: B14005 | SEX BY SCHOOL ENROLLMENT BY...

    • data.census.gov
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    ACS, 2019 American Community Survey: B14005 | SEX BY SCHOOL ENROLLMENT BY EDUCATIONAL ATTAINMENT BY EMPLOYMENT STATUS FOR THE POPULATION 16 TO 19 YEARS (ACS 1-Year Estimates Detailed Tables) [Dataset]. https://data.census.gov/table/ACSDT1Y2019.B14005?q=School%20Enrollment&g=010XX00US&y=2019
    Explore at:
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2019
    Description

    Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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, 2019 American Community Survey 1-Year Estimates.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..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 2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 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:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

  14. Great Britain Historical Database : Census Data : Education Statistics,...

    • beta.ukdataservice.ac.uk
    • datacatalogue.cessda.eu
    Updated 2021
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    H. R. Southall; D. Dorling (2021). Great Britain Historical Database : Census Data : Education Statistics, 1951-1961 [Dataset]. http://doi.org/10.5255/ukda-sn-4552-2
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    Dataset updated
    2021
    Dataset provided by
    UK Data Servicehttps://ukdataservice.ac.uk/
    DataCitehttps://www.datacite.org/
    Authors
    H. R. Southall; D. Dorling
    Area covered
    Great Britain, United Kingdom
    Description

    The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

    These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

    The census gathered data on levels of educational attainment only from 1951. In 1951 and 1961, attainment was measured simply by the age at which a person's education was completed, rather than by the level of qualifications achieved. These data cover, broadly, the adult population, including many people who had completed their education decades before the relevant census, so the data are indicative of the general level of education of the workforce at the census date, but are a problematic guide to the performance of the education system at that date. The census reports also include cross-tabulations of age of education completion with current age, but not with the level of geographical detail of the transcribed tables.

    The 1951 data for England and Wales were computerised by Danny Dorling (now of Oxford University), as part of research funded by the Joseph Rowntree Foundation.


    Latest edition information:

    For the 2nd edition (June 2021), data for Scotland for 1951 and data for England & Wales and Scotland for 1961 have been added to the study.

    <!--[if gte mso 9]>

  15. b

    Percent Population (25 Years and over) With Less Than a High School Diploma...

    • data.baltimorecity.gov
    • vital-signs-bniajfi.hub.arcgis.com
    • +1more
    Updated Mar 13, 2020
    + more versions
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    Baltimore Neighborhood Indicators Alliance (2020). Percent Population (25 Years and over) With Less Than a High School Diploma or GED [Dataset]. https://data.baltimorecity.gov/maps/b394fb448d6548ab904f8e174d2ab049
    Explore at:
    Dataset updated
    Mar 13, 2020
    Dataset authored and provided by
    Baltimore Neighborhood Indicators Alliance
    Area covered
    Description

    The percentage of persons that have not completed, graduated, or received a high school diploma or GED. This is a standard indicator used to measure the portion of the population with less than a basic level of skills needed for the workplace. Persons under the age of 25 are not included in this analysis since many of these persons are still attending various levels of schooling.Source: American Community Survey Years Available: 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

  16. S

    2023 Census totals by topic for individuals by statistical area 1 – part 2

    • datafinder.stats.govt.nz
    csv, dwg, geodatabase +6
    Updated Dec 9, 2024
    + more versions
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    Stats NZ (2024). 2023 Census totals by topic for individuals by statistical area 1 – part 2 [Dataset]. https://datafinder.stats.govt.nz/layer/120792-2023-census-totals-by-topic-for-individuals-by-statistical-area-1-part-2/
    Explore at:
    csv, shapefile, pdf, geodatabase, kml, geopackage / sqlite, mapinfo tab, mapinfo mif, dwgAvailable download formats
    Dataset updated
    Dec 9, 2024
    Dataset provided by
    Statistics New Zealandhttp://www.stats.govt.nz/
    Authors
    Stats NZ
    License

    https://datafinder.stats.govt.nz/license/attribution-4-0-international/https://datafinder.stats.govt.nz/license/attribution-4-0-international/

    Area covered
    Description

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

    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.

    The variables for part 2 of the dataset are:

    • Individual home ownership for the census usually resident population count aged 15 years and over
    • Usual residence 1 year ago indicator
    • Usual residence 5 years ago indicator
    • Years at usual residence
    • Average years at usual residence
    • Years since arrival in New Zealand for the overseas-born census usually resident population count
    • Average years since arrival in New Zealand for the overseas-born census usually resident population count
    • Study participation
    • Main means of travel to education, by usual residence address for the census usually resident population who are studying
    • Main means of travel to education, by education address for the census usually resident population who are studying
    • Highest qualification for the census usually resident population count aged 15 years and over
    • Post-school qualification in New Zealand indicator for the census usually resident population count aged 15 years and over
    • Highest secondary school qualification for the census usually resident population count aged 15 years and over
    • Post-school qualification level of attainment for the census usually resident population count aged 15 years and over
    • Sources of personal income (total responses) for the census usually resident population count aged 15 years and over
    • Total personal income for the census usually resident population count aged 15 years and over
    • Median ($) total personal income for the census usually resident population count aged 15 years and over
    • Work and labour force status for the census usually resident population count aged 15 years and over
    • Job search methods (total responses) for the unemployed census usually resident population count aged 15 years and over
    • Status in employment for the employed census usually resident population count aged 15 years and over
    • Unpaid activities (total responses) for the census usually resident population count aged 15 years and over
    • Hours worked in employment per week for the employed census usually resident population count aged 15 years and over
    • Average hours worked in employment per week for the employed census usually resident population count aged 15 years and over
    • Industry, by usual residence address for the employed census usually resident population count aged 15 years and over
    • Industry, by workplace address for the employed census usually resident population count aged 15 years and over
    • Occupation, by usual residence address for the employed census usually resident population count aged 15 years and over
    • Occupation, by workplace address for the employed census usually resident population count aged 15 years and over
    • Main means of travel to work, by usual residence address for the employed census usually resident population count aged 15 years and over
    • Main means of travel to work, by workplace address for the employed census usually resident population count aged 15 years and over
    • Sector of ownership for the employed census usually resident population count aged 15 years and over
    • Individual unit data source.

    Download lookup file for part 2 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.

    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.

    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.

    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.

    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.

    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).

    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.

    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.

    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.

    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.

    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.

    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”.

    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.

    Measures

    Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value

  17. c

    1831 Census Database as Organised by the Registration Districts of 1851

    • datacatalogue.cessda.eu
    • beta.ukdataservice.ac.uk
    Updated Nov 28, 2024
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    Gatley, D. Alan, University of Staffordshire (2024). 1831 Census Database as Organised by the Registration Districts of 1851 [Dataset]. http://doi.org/10.5255/UKDA-SN-4961-1
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    Dataset updated
    Nov 28, 2024
    Dataset provided by
    School of Social Sciences
    Authors
    Gatley, D. Alan, University of Staffordshire
    Area covered
    Great Britain, Isle of Man, Channel Islands
    Variables measured
    Individuals, Families/households, Groups, Administrative units (geographical/political), National
    Measurement technique
    Transcription of existing materials, Compilation or synthesis of existing material
    Description

    Abstract copyright UK Data Service and data collection copyright owner.


    The 1831 Census was the fourth national census to be undertaken in Great Britain. Although the amount of information collected in this census was far less than was to be collected in later years, that of 1831 was the first in which detailed occupational statistics were collected on the employment of males aged 20 and over. The census was also the first in which detailed instructions were given to the enumerators on how they were to count the population.

    Main Topics:

    This dataset is comprise by a complete transcription of the 1831 census abstracts for the whole of Great Britain and the offshore islands of Jersey, Guernsey and the Isle of Man; re-organised according to 1851 registration districts. It forms part of the wider Victorian Census project which aims to digitise nineteenth century census documents and related material, such as vital registration and crime statistics, pertaining to Great Britain and Ireland.

    This dataset will not be available until January 2005, but a simplified version of the database can be downloaded from the Victorian Census Project web site:

    http://www.staffs.ac.uk/schools/humanities_and_soc_sciences/census/vichome.htm


    Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

  18. f

    Data from: Full-time teaching and segmentation of the offer: analysis of the...

    • scielo.figshare.com
    png
    Updated Jun 13, 2023
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    Eduardo Donizeti Girotto; Isabel Furlan Jorge; João Victor Pavesi de Oliveira (2023). Full-time teaching and segmentation of the offer: analysis of the ETI and PEI programs in the São Paulo state public system [Dataset]. http://doi.org/10.6084/m9.figshare.20970281.v1
    Explore at:
    pngAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    SciELO journals
    Authors
    Eduardo Donizeti Girotto; Isabel Furlan Jorge; João Victor Pavesi de Oliveira
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    State of São Paulo, Prince Edward Island
    Description

    ABSTRACT The article analyzes the implementation of two Full-Time Teaching Programs in the state public education system of São Paulo, seeking to understand their effects on the dynamics of the system with a focus on expanding the segmentation of educational offer and educational inequalities. For that, we resorted to the analysis of documents that guide the programs, interviews with former secretaries and members of the State Department of Education, as well as producing, from the microdata of the School Census (2019), graphs, tables and maps to understand the organization of each of the programs. The data show that, although they originally had different proposals, the two programs are currently similar in terms of supply and service conditions, better than those found in other schools in the chain. Thus, it is possible to verify the segmentation of the offer on the network, induced by the Programs, which can contribute to the reproduction of educational inequalities.

  19. Pupil absence in schools in England: 2015 to 2016

    • gov.uk
    Updated Apr 19, 2017
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    Department for Education (2017). Pupil absence in schools in England: 2015 to 2016 [Dataset]. https://www.gov.uk/government/statistics/pupil-absence-in-schools-in-england-2015-to-2016
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    Dataset updated
    Apr 19, 2017
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Education
    Area covered
    England
    Description

    This release provides information on the levels of overall, authorised and unauthorised absence in:

    • state-funded primary schools
    • state-funded secondary schools
    • special schools

    It includes information on:

    • reasons for absence
    • persistent absence
    • pupil characteristics
    • absence information for pupil referral units

    The information is based on pupil level absence data collected via the school census.

    It updates and supplements information published in the October 2016 release ‘Pupil absence in schools in England, autumn 2015 and spring 2016’.

    A guide on how we produce pupil absence statistics is also available.

    School census statistics team

    Email mailto:schools.statistics@education.gov.uk">schools.statistics@education.gov.uk

    Telephone: Mark Pearson 0370 000 2288

  20. Current Population Survey, October 2005: School Enrollment Supplement -...

    • search.gesis.org
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    ICPSR - Interuniversity Consortium for Political and Social Research, Current Population Survey, October 2005: School Enrollment Supplement - Version 2 [Dataset]. http://doi.org/10.3886/ICPSR04567.v2
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    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    GESIS search
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de438686https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de438686

    Description

    Abstract (en): This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a survey on the topic of School Enrollment in the United States, which was administered as a supplement to the October CPS. The Census Bureau and the National Center for Education Statistics jointly sponsored the supplemental questions. The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States, for the week prior to the survey. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. The October supplemental survey queried respondents on school enrollment for all persons in the household aged three and over. Information was collected on current grade at public or private school, whether attending college full- or part-time at a two-or four-year institution, year last attended a regular school, year graduated from high school, grade retention, library use, library accessibility, and resources for people with disabilities. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income. The data contain seven weight variables: Household Weight -- HWHHWGT -- Used in tallying household characteristics.; Family Weight -- PWFMWGT -- Used only in tallying family characteristics.; Longitudinal Weight -- PWLGWGT -- Found only on adult records matched from month to month (used for gross flows analysis).; Outgoing Rotation Weight -- PWORWGT -- Used for tallying information collected only in outgoing rotations.; Final Weight -- PWSSWGT -- Used for most tabulations, controlled to independent estimates for (1) States; (2) Origin, Sex, and Age; and (3) Age, Race, and Sex.; Veteran's Weight -- PWVETWGT -- Used for tallying veteran's data only.; Composited Final Weight -- PWCMPWGT -- Used to create BLS's published labor force statistics.; There is no supplement weight associated with the October 2005 School Enrollment supplement data. Use the basic CPS weight (PWSSWGT) for tallying individuals on the file. Users are strongly encouraged to refer to the User Guide for additional detailed information on how to use these weights, and how they were derived. The CPS universe consists of all persons in the civilian noninstitutional population of the United States living in households. The October 2005 supplement universe includes the full CPS sample comprised of all people 3 years old or over. A multistage probability sample was selected to represent the universe of approximately 55,000-56,000 households. 2011-12-21 The ASCII data for this collection have been completely replaced. The data collection has been updated to include SAS, SPSS, and Stata setup files for use with the new data. Also included in the update are a corresponding SAS transport (CPORT) file, SPSS system file, Stata system file, and a tab-delimited version of the new ASCII data. computer-assisted personal interview (CAPI), computer-assisted telephone interview (CATI)Users are strongly encouraged to refer to the User Guide (produced by the Principal Investigators), which contains not only information about the basic CPS survey, but also detailed technical documentation specific to the School Enrollment Supplement. In particular, Attachment 8 of the User Guide contains the supplement questionnaire.The universe statements for each variable are defined in either the basic or supplement record layout, which are located in Attachments 6 and 7, respectively, of the User Guide.ICPSR removed all FILLER and PADDING variables from the data. As a result, the column locations in any ICPSR-released data product (e.g., codebook and setup files) will have column locations that are not consistent with locations described in the User Guide.

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Baltimore Neighborhood Indicators Alliance (2020). Percentage of Population aged 16-19 in School and/or Employed [Dataset]. https://hub.arcgis.com/maps/7d814d51552b45e2bfe17e4edf417131

Percentage of Population aged 16-19 in School and/or Employed

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Dataset updated
Mar 6, 2020
Dataset authored and provided by
Baltimore Neighborhood Indicators Alliance
Area covered
Description

The percentage of persons aged 16 to 19 who are in school and/or are employed out of all persons in their age cohort. Please note: due to the nature of this indicator, do not compare changes over time. This indicator can only be used as a point in time "snapshot". For more information, please visit the U.S. Census page on Comparing ACS Datahttps://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

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