The 119th Congressional Districts dataset reflects boundaries from January 03, 2025 from the United States Census Bureau (USCB), and the attributes are updated every Sunday from the United States House of Representatives and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Information for each member of Congress is appended to the Census Congressional District shapefile using information from the Office of the Clerk, U.S. House of Representatives' website https://clerk.house.gov/xml/lists/MemberData.xml and its corresponding XML file. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. This dataset also includes 9 geographies for non-voting at large delegate districts, resident commissioner districts, and congressional districts that are not defined. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 119th Congress is seated from January 3, 2025 through January 3, 2027. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the CDs to cover all of the state or state equivalent area. In these areas with no CDs defined, the code "ZZ" has been assigned, which is treated as a single CD for purposes of data presentation. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by May 31, 2024. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529006

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Description: The data presents Uganda’s population distribution by region, district, and gender as of 2024. Buganda region, comprising 26 districts, has the highest population at 11,171,924 people. It is followed by Busoga region, which has 12 districts and a population of 4,363,295. Sebei region, with only three districts, has the smallest population at 377,294 people. Most recent changes: The dataset was last updated on 2024. Languages: EN Source: This data was obtained from the National Housing and Population Census report published by the Uganda Bureau of Statistics. For more information visit https://www.ubos.org/

Final approved map by the 2020 California Citizens Redistricting Commission for the California State Senate. Final approved map by the 2020 California Citizens Redistricting Commission for the California State Senate; the authoritative and official delineations of the California State Senate drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State. Line drawing criteria included population equality as required by the U.S. Constitution, the Federal Voting Rights Act, geographic contiguity, geographic integrity, geographic compactness, and nesting. Geography was defined by U.S. Census Block geometry. Each of the 40 Senate districts has an ideal population of nearly one million people and represents the largest state legislative districts in the nation. In consideration of population equality, the Commission chose to limit the population deviation as close to zero percent as practicable. Per the California Constitution, the Commission strived to nest two Assembly districts where practicable. However, higher ranking criteria made this difficult in practice. While the size of the Senate districts allowed the Commission to recognize broadly shared interests, these interests did not always overlap exactly with the interests of smaller communities recognized in the related Assembly districts. Based on the large number of people in each district, there were a variety of different interests that were balanced and included.

About this dataset

Context

The dataset tabulates the District Heights population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of District Heights. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 3,464 (58.83% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the District Heights population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in District Heights is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the District Heights is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for District Heights Population by Age. You can refer the same here

DVRPC manually adjusted TIGER MCD dataset by adding Philly Planning District boundaries. To get the Planning District boundaries, census tracts were dissolved into most appropriate CPA geography. (Planning Districts are sometime referred to as County Planning Areas at DVRPC) Updated in Jan 2023 to reflect Pine Valley/Pine Hill merger (Pine Valley was incorporated into Pine Hill) The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area. See https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc_Ch3.pdf for more information Downloaded from: https://www2.census.gov/geo/tiger/TGRGDB20/ on August 13, 2020

The census is Canada's largest and most comprehensive data source conducted by Statistics Canada every five years. The Census of Population collects demographic and linguistic information on every man, woman and child living in Canada.The data shown here is provided by Statistics Canada from the 2011 Census as a custom profile data order for the City of Vancouver, using the City's 22 local planning areas. The data may be reproduced provided they are credited to Statistics Canada, Census 2011, custom order for City of Vancouver Local Areas.Data accessThis dataset has not yet been converted to a format compatible with our new platform. The following links provide access to the files from our legacy site: Census local area profiles 2011 (CSV) Census local area profiles 2011 (XLS) Dataset schema (Attributes)Please see the Census local area profiles 2011 attributes page. NoteThe 22 Local Areas is defined by the Census blocks and is equal to the City'​s 22 local planning areas and includes the Musqueam 2 reserve.Vancouver CSD (Census Subdivision) is defined by the City of Vancouver municipal boundary which excludes the Musqueam 2 reserve but includes Stanley Park. Vancouver CMA (Census Metropolitan Area) is defined by the Metro Vancouver boundary which includes the following Census Subdivisions: Vancouver, Surrey, Burnaby, Richmond, Coquitlam, District of Langley, Delta, District of North Vancouver, Maple Ridge, New Westminster, Port Coquitlam, City of North Vancouver, West Vancouver, Port Moody, City of Langley, White Rock, Pitt Meadows, Greater Vancouver A, Bowen Island, Capilano 5, Anmore, Musqueam 2, Burrard Inlet 3, Lions Bay, Tsawwassen, Belcarra, Mission 1, Matsqui 4, Katzie 1, Semiahmoo, Seymour Creek 2, McMillian Island 6, Coquitlam 1, Musqueam 4, Coquitlam 2, Katzie 2, Whonnock 1, Barnston Island 3, and Langley 5. In 2011 Statistics Canada replaced the "long form" census with a voluntary National Household Survey. The result of the survey will not be directly comparable with previous census data. In 2006 there were changes made to the definition of households. A number of Single Room Occupancy and Seniors facilities were considered to be dwellings in 2001, and collective dwellings in 2006. The City believes a similar change occurred on some properties between 2006 and 2011. This would explain why the numbers of "Apartments under 5 stories" has fallen in some locations.Note that for the first time in 2011, three language questions (knowledge of official languages, home language and mother tongue) were included on the census questionnaire that was administered to 100% of the population.Language data and analysis published for all censuses since 1996 have been based almost exclusively on responses from the long-form census questionnaire administered to 20% of the population. However, Statistics Canada has observed changes in patterns of response to both the mother tongue and home language questions that appear to have arisen from changes in the placement and context of the language questions on the 2011 Census questionnaire relative to previous censuses. As a result, Canadians appear to have been less inclined than in previous censuses to report languages other than English or French as their only mother tongue, and also more inclined to report multiple languages as their mother tongue and as the language used most often at home. Data currencyThe data for Census 2011 was collected in May 2011. Data accuracyStatistics Canada is committed to protect the privacy of all Canadians and the confidentiality of the data they provide to us. As part of this commitment, some population counts of geographic areas are adjusted in order to ensure confidentiality. Counts of the total population are rounded to a base of 5 for any dissemination block having a population of less than 15. Population counts for all standard geographic areas above the dissemination block level are derived by summing the adjusted dissemination block counts. The adjustment of dissemination block counts is controlled to ensure that the population counts for dissemination areas will always be within 5 of the actual values. The adjustment has no impact on the population counts of census divisions and large census subdivisions. Websites for further information Statistics Canada 2011 Census Dictionary Local area boundary dataset

Final approved map by the 2020 California Citizens Redistricting Commission for the California State Senate; the authoritative and official delineations of the California State Senate drawn during the 2020 redistricting cycle. The Citizens Redistricting Commission for the State of California has created statewide district maps for the State Assembly, State Senate, State Board of Equalization, and United States Congress in accordance, with the provisions of Article XXI of the California Constitution. The Commission has approved the final maps and certified them to the Secretary of State.

Description: This dataset shows the population changes in mining and oil rich districts from the 1969 census to the 2014 census conducted by the Uganda Bureau of Statistics. The mining rich districts included in this dataset are those with large artisanal mining operations and with companies holding mining leases. Most recent changes: The dataset was last updated in December 2020 Languages: EN Source: This data was obtained from the Uganda Bureau of Statistics by the African Centre for Media Excellence (ACME). For more information visit https://www.ubos.org/

In the isodemographic map of the population development Graz 2006 - 2012, the individual districts are shown in proportion to the population in 2012. The size and shape of the district boundaries are adjusted in such a way that a uniform population density prevails in the areas shown; However, the spatial relationship and distribution of the city districts to each other remain largely the same. Specific spatial trends and developments can be derived from this: The most populous municipalities are Jakomini (35,800), Lend (32,000), Gries (29,300) and Geidorf (28,000); The focus of the population of Graz is clearly in the districts around the old town of Graz. The highest growth rates and dynamics in the period May 2006 to May 2012 are recorded in percentage terms in the districts of Lend (+9.2%), Strassgang (+8.5%) and Mariatrost (+8.1). Compared to the absolute growth figures, Lend (+2,700 p.e.), Jakomini (+1,300 p.e.) and Strassgang (+1,200 p.e.) are clearly ahead. This contrasts with the districts with the lowest growth rates over the same period: Ries (+0.3% / 19 p.e.), Innere Stadt (+0.5% / 21 p.e.), Waltendorf (+1.1% / 149 p.e.) and Gries (+1.1% / 332 p.e.).

A dataset listing Washington cities by population for 2024.

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

More MetadataThis data was produced in compliance with the constitution of the Commonwealth of Virginia and the Code of Virginia, which mandate that counties redraw their Board of Supervisor election districts based of the results of the most recent decennial census, in this case 2010. Redrawing the boundaries ensures equal representation of the population. Each election district is required to have a similar population count, generally within +/- 5% of the equalized decennial census population of the County, which is calculated using the Loudoun County decennial Census population divided by the number of districts. Loudoun County has eight districts, and also elects the Board Chairman at-large. Election districts: Algonkian, Ashburn, Blue Ridge, Broad Run, Catoctin, Dulles, Leesburg, Sterling

Abstract

The ‘South African Population Research Infrastructure Network’ (SAPRIN) is a national research infrastructure funded through the Department of Science and Innovation and hosted by the South African Medical Research Council. One of SAPRIN’s initial goals has been to harmonise and share the longitudinal data from the three current Health and Demographic Surveillance System (HDSS) Nodes. These long-standing nodes are the MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, established in 1993, with a population of 113 113 people; the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, established in 1996, with a current population of 38 479; and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, established in 2000, with a current population of 139 250.

This dataset represents a snapshot of the continually evolving data in the underlying longitudinal databases maintained by the SAPRIN nodes. In these databases the rightmost extend of the individual's surveillance episode is indicated by the data collection date of the last time the individual's membership of a household under surveillance has been confirmed. Each dataset has a right censor date (31 December 2017 for the current version of the dataset) and individual surveillance episodes are terminated at that point if the individual is still under surveillance beyond the cut-off date.

Each individual surveillance episode is associated with a physical location, for internal residency episodes it is the actual place of residence of the individual, for external residence episodes (periods of temporary migration) it is the place of residence of the individual's household. If an individual change their place of residency from one location within the surveillance area to another location still within the surveillance area, the episode at the original location is terminated with a location exit event, and a new episode starts with a location entry event at the destination location. It is also possible for the household the individual is a member of, to change their place of residency in the surveillance area, whilst the individual is externally resident (is a temporary migrant), in which case the individual's external resident episode will also be split with a location exit-entry pair of events.

At every household visit written consent is obtained from the household respondent for continued participation in the surveillance and such consent can be withdrawn. When this happens all household members' surveillance episodes are terminated with a refusal event. It is possible for households to again provide consent to participate in the surveillance after some time, in such cases surveillance events are restarted with a permission event.

As mentioned previously, surveillance episodes are continually extended by the last data collection event if the individual remains under surveillance. In certain cases, individuals may be lost to follow-up and surveillance episodes where the date of last data collection is more than one year prior to the right censor data are terminated as lost to follow up at that last data collection date. Individuals with data collection dates within a year of the right censor date is considered still to be under surveillance up to this last data collection date.

Each surveillance episode contains the identifier of the household the individual is a member of during that episode. Under relatively rare circumstances it is possible for an individual to change household membership whilst still resident at the same location, or to change membership whilst externally resident, in these cases the surveillance episode will be split with a pair of membership end and membership start events. More commonly membership start and end events coincide with location exit and entry events or in- and out-migration events. Memberships also obviously start at birth or enumeration and end at death, refusal to participate or lost to follow-up.

In about half of the cases, individuals have a single episode from first enumeration, birth or in-migration, to their eventual death, out-migration or currently still under surveillance. In the remaining cases, individuals transition from internal residency to external residency via out-migration, or from one location to another via internal migration with a location exit and entry event, or some other rarer form of transition involving membership change, refusal or lost to follow-up. Usually these series of surveillance episodes are continuous in time, with no gaps between episodes, but gaps can form, e.g. when an individual out-migrates and end membership with the household and so is no longer under surveillance, only to return via in-migration at some future date and take up membership with same or different household.

The SAPRIN Individual Surveillance Episodes 2020 Datasets consists of three types of the Demographic surveillance datasets: 1.SAPRIN Individual Surveillance Episodes 2020: Basic Dataset. This dataset contains only the internal and external residency episodes for an individual. 2.SAPRIN Individual Surveillance Episodes 2020: Age-Year-Delivery Dataset. This dataset splits the basic surveillance episodes at calendar year end and at the date when the age in years (birth-day) of an individual changes. In the case of women who have given births, episodes are split at the time of delivery as well. 3.SAPRIN Individual Surveillance Episodes 2020: Detailed Dataset. This dataset adds to the dataset 2 time-varying attributes such as education, employment, marital status and socio-economic status.

Geographic coverage

The South African Population Research Infrastructure Network (SAPRIN) currently represents a network of three Health and Demographic Surveillance System (HDSS) nodes located in rural South Africa, namely: 1) MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, which has collected data since 1993. The nodal website is: http://www.agincourt.co.za; 2) the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, which has collected data since 1996.The nodal website is: N/A; 3) and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, which has collected data since 2000.The nodal website is: http://www.ahri.org.

The Agincourt HDSS covers a surveillance area of approximately 420 square kilometres and is located in the Bushbuckridge District, Mpumalanga in the rural northeast of South Africa close to the Mozambique border. At baseline in 1992, 57 600 people were recorded in 8900 households in 20 villages; by 2006, the population had increased to about 70 000 people in 11 700 households. As of December 2017, there were 113 113 people under surveillance of whom 28% were not resident within the surveillance area, with a total of about 2m person years of observation. 33% of the population is under 15 years old. The population is almost exclusively Shangaan-speaking.The Agincourt HDSS has population density of over 200 persons per square kilometre. The Agincourt HDSS extends between latitudes 24° 50´ and 24° 56´S and longitudes 31°08´ and 31°´ 25´ E. The altitude is about 400-600m above sea level.

DIMAMO is located in the Capricorn district, Limpopo Province approximately 40 kilometres from Polokwane, the capital city of Limpopo Province and 15-50 kilometres from the University of Limpopo. The site covers an area of approximately 400 square kilometres . The initial total population observed was about 8 000 but the field site was expanded in 2010. As of December 2017, there were 38 479 people under surveillance, of whom 22% were not resident within the surveillance area, with about 400,000 person years of observation. 30% of the population is under 15 years old. The population is predominantly Sotho speaking. Most households have electricity. Some households have piped water either inside the house or in their yards, but most fetch water from taps situated at strategic points in the villages. Most households have a pit latrine in their yards. The area lies between latitudes and 23°65´ and 23°90´S and longitudes 29°65´ and 29°85´E. The HDSS is located on a high plateau area (approximately 1250 m above sea level) where communities typically consist of households clustered in villages, with access to local land for small-scale food production.

Africa Health Research Institute (AHRI) is situated in the south-east portion of the Umkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the south by the Umfolozi river, on the east by the N2 highway (except form portions where the Kwamsane township stradles the highway) and in the north by the Inyalazi river for portions of the boundary. The surveillance area is approximately 850 square kilometres. As of December 2017, there were 139 250 people under surveillance of whom 28% were not resident within the surveillance area, with about 1.7m person years of observation. 32% of the population is under 15 years old. The population is almost exclusively Zulu-speaking. The surveillance area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20-3000 people per square kilometre). The area lies between latitudes -28°24' and 28°20'N and longitudes 32°10' and 31°58'E.

Analysis unit

Households and individuals

Universe

Households resident in dwellings within the study area will be eligible for inclusion in the household component of SAPRIN. All individuals identified by the household proxy informant as a member of

Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together separately from the 2003 datasets.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was, as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households

The latest population figures produced by the Office for National Statistics (ONS) on 28 June 2018 show that an estimated 534,800 people live in Bradford District – an increase of 2,300 people (0.4%) since the previous year.

Bradford District is the fifth largest metropolitan district (in terms of population) in England, after Birmingham, Leeds, Sheffield and Manchester although the District’s population growth is lower than other major cities.

The increase in the District’s population is largely due to “natural change”- there have been around 3,300 more births than deaths, although this has been balanced by a larger number of people leaving Bradford to live in other parts of the UK than coming to live here and a lower number of international migrants. In 2016/17 the net internal migration was -2,700 and the net international migration was 1,700.

A large proportion of Bradford’s population is dominated by the younger age groups. More than one-quarter (29%) of the District’s population is aged less than 20 and nearly seven in ten people are aged less than 50. Bradford has the highest percentage of the under 16 population in England after the London Borough of Barking and Dagenham, Slough Borough Council and Luton Borough Council.

The population of Bradford is ethnically diverse. The largest proportion of the district’s population (63.9%) identifies themselves as White British. The district has the largest proportion of people of Pakistani ethnic origin (20.3%) in England.

The largest religious group in Bradford is Christian (45.9% of the population). Nearly one quarter of the population (24.7%) are Muslim. Just over one fifth of the district’s population (20.7%) stated that they had no religion.

There are 216,813 households in the Bradford district. Most households own their own home (29.3% outright and 35.7% with a mortgage). The percentage of privately rented households is 18.1%. 29.6% of households were single person households.

Information from the Annual Population Survey in December 2017 found that Bradford has 228,100 people aged 16-64 in employment. At 68% this is significantly lower than the national rate (74.9%). 91,100 (around 1 in 3 people) aged 16-64, are not in work. The claimant count rate is 2.9% which is higher than the regional and national averages.

Skill levels are improving with 26.5% of 16 to 74 year olds educated to degree level. 18% of the district’s employed residents work in retail/wholesale. The percentage of people working in manufacturing has continued to decrease from 13.4% in 2009 to 11.9% in 2016. This is still higher than the average for Great Britain (8.1%).

About this dataset

Context

The dataset tabulates the District of Columbia population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for District of Columbia. The dataset can be utilized to understand the population distribution of District of Columbia by age. For example, using this dataset, we can identify the largest age group in District of Columbia.

Key observations

The largest age group in District of Columbia was for the group of age 30 to 34 years years with a population of 76,902 (11.44%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in District of Columbia was the 85 years and over years with a population of 9,859 (1.47%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the District of Columbia is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of District of Columbia total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for District of Columbia Population by Age. You can refer the same here

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The data on this page was collected in September 2021. The CEV can generate reliable estimates at the national level, within states and the District of Columbia, and in the largest twelve Metropolitan Statistical Areas to support evidence-based decision making and efforts to understand how people make a difference in communities across the country. Click on "Export" to download and review an excerpt from the 2021 CEV Analytic Codebook that shows the variables available in the analytic CEV datasets produced by AmeriCorps. Click on "Show More" to download and review the following 2021 CEV data and resources provided as attachments: 1) 2021 CEV Dataset Fact Sheet – brief summary of technical aspects of the 2021 CEV dataset. 2) CEV FAQs – answers to frequently asked technical questions about the CEV 3) Constructs and measures in the CEV 4) 2021 CEV Analytic Data and Setup Files – analytic dataset in Stata (.dta), R (.rdata), SPSS (.sav), and Excel (.csv) formats, codebook for analytic dataset, and Stata code (.do) to convert raw dataset to analytic formatting produced by AmeriCorps. These files were updated on January 16, 2025 to correct erroneous missing values for the ssupwgt variable. 5) 2021 CEV Technical Documentation – codebook for raw dataset and full supplement documentation produced by U.S. Census Bureau 6) Nonresponse Bias Analysis produced by U.S. Census Bureau 7) 2021 CEV Raw Data and Read In Files – raw dataset in Stata (.dta) format, Stata code (.do) and dictionary file (.dct) to read ASCII dataset (.dat) into Stata using layout files (.lis)

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The data on this page was collected in September 2017. The CEV can generate reliable estimates at the national level, within states and the District of Columbia, and in the largest twelve Metropolitan Statistical Areas to support evidence-based decision making and efforts to understand how people make a difference in communities across the country. This page was updated on January 16, 2025 to ensure consistency across all waves of CEV data. Click on "Export" to download and review an excerpt from the 2017 CEV Analytic Codebook that shows the variables available in the analytic CEV datasets produced by AmeriCorps. Click on "Show More" to download and review the following 2017 CEV data and resources provided as attachments: 1) CEV FAQs – answers to frequently asked technical questions about the CEV 2) Constructs and measures in the CEV 3) 2017 CEV Analytic Data and Setup Files – analytic dataset in Stata (.dta), R (.rdata), SPSS (.sav), and Excel (.csv) formats, codebook for analytic dataset, and Stata code (.do) to convert raw dataset to analytic formatting produced by AmeriCorps. 4) 2017 CEV Technical Documentation – codebook for raw dataset and full supplement documentation produced by U.S. Census Bureau 5) 2017 CEV Raw Data and Read In Files – raw dataset in Stata (.dta) format, Stata code (.do) and dictionary file (.dct) to read ASCII dataset (.dat) into Stata using layout files (.lis)

About this dataset

Context

The dataset tabulates the population of District township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for District township. The dataset can be utilized to understand the population distribution of District township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in District township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for District township.

Key observations

Largest age group (population): Male # 65-69 years (86) | Female # 55-59 years (93). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the District township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the District township is shown in the following column.
• Population (Female): The female population in the District township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in District township for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for District township Population by Gender. You can refer the same here