1911 Ireland Census contains records from Scalp, Kilthomas, Galway, Ireland by Ancestry.com. Web: Ireland, Census, 1911 [database on-line]. Provo, UT, USA: Ancestry.com Operations, Inc., 2013. Original data: Census of Ireland 1901/1911. The National Archives of Ireland. http://www.census.nationalarchives.ie/search/ - .

The data shows the year-wise gender ratio of population for states and union territories of India over the time period of 110 years starting from 1901 to 2011 according to the census reports.

Note: 1. For working out the gender ratio of India and Assam for 1981, interpolated figures for Assam have been used. 2. For working out the gender ratio of India and Jammu & Kashmir for 1991, interpolated figures for Jammu & Kashmir have been used. 3. The gender ratio for Arunachal Pradesh is not available for the years 1901-1951 and for Pondicherry it is not available for the years 1901, 1931 and 1941.

The 1901 Census of Canada was the fourth Census conducted . The Canadian Families Project has a broad mandate. Their re-investigation of family in Canada includes the study of discourses of family; class, ethnicity and region as they relate to family; the history of single parenthood and fragmented families; fertility decline; language, education and family; religion and family; family and community in rural Canada; the social geography of urban families; family income and standards of living. Basic to the work of the Project is the study of families in the past. The Project begins by creating a large database of information from the 1901 census of Canada. The database will include all information from Schedules 1 and 2 of the census for five percent of individuals and families in the whole of Canada (as it existed in 1901). Schedule 1 contains the nominal returns - the enumeration of the population by name. Schedule 2 is a continuation of Schedule 1 and it gives information of buildings and lands held by persons enumerated in Schedule 1. The 5 percent sample will include information on approximately 268,500 persons. (Summary derived from User Guide)

Abstract copyright UK Data Service and data collection copyright owner.

In order to facilitate the study of local history the Pinner Local History Society have transcribed the enumerators' books for Pinner for the census year 1901 in addition to their previous transcriptions of 1841, 1851, 1861, 1871, 1881 and 1891.

Main Topics:

The data comprise one record for each individual with information as follows: year and schedule; address; surname; forename/s; relationship to head code; marital status; sex; age; code for occupation (Booth-Armstrong); occupation name; birthplace; birth county.

Contained within the 4th Edition (1974) of the Atlas of Canada is a collection of four maps showing the distribution of population for 1851 (Newfoundland 1857), 1871 (Newfoundland 1869), 1901 and 1921 by historical region. A supplementary chart for each map shows the percentage of population by province and territory. As well, for each map, a text listing urban centres with historical populations of 10 000 or more is provided.

Age and Sex Highlight Tables, Age group ratios, historical trend, 1901 to 2011 - Provides information highlights by topic via key indicators for various levels of geography.

This collection contains statistical data extracted from the published W.A. colonial census reports from 1848 to 1901. The data are presented both as raw Excel spreadsheets and as processed files for use with the SPSS and Nesstar statistical software. These files were compiled and processed by Leanne Den Hartog in 2012 as part of her work for the Western Australian node of the Australian Data Archive.

Data extracted from the published reports on the Western Australian colonial censuses held between 1848 and 1901, presented as Excel files.

About this dataset

Context

The dataset tabulates the Highland Park 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 Highland Park. The dataset can be utilized to understand the population distribution of Highland Park by age. For example, using this dataset, we can identify the largest age group in Highland Park.

Key observations

The largest age group in Highland Park, NJ was for the group of age 30 to 34 years years with a population of 1,901 (12.69%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Highland Park, NJ was the 80 to 84 years years with a population of 191 (1.27%). 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 Highland Park is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Highland Park 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 Highland Park Population by Age. You can refer the same here

Census: Population: Chhattisgarh data was reported at 25,545,198.000 Person in 03-01-2011. This records an increase from the previous number of 20,833,803.000 Person for 03-01-2001. Census: Population: Chhattisgarh data is updated decadal, averaging 8,305,602.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 25,545,198.000 Person in 03-01-2011 and a record low of 4,181,554.000 Person in 03-01-1901. Census: Population: Chhattisgarh data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.

Abstract copyright UK Data Service and data collection copyright owner.

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.

The Great Britain Historical GIS Project has also produced digitised boundary data, which can be obtained from the UK Data Service Census Support service. Further information is available at census.ukdataservice.ac.uk

Main Topics:

The Great Britain Historical Database is a large database of British nineteenth and twentieth-century statistics. Where practical the referencing of spatial units has been integrated, data for different dates have been assembled into single tables.

The Great Britain Historical Database currently contains :

• Statistics from the 1861 Census and the Registrar General's reports, 1851-1861
  
• Employment statistics from the census, 1841-1931
  
• Demographic statistics from the census, 1841-1931
  
• Mortality statistics from the Registrar General's reports, 1861-1920
  
• Marriage statistics from the Registrar General's reports, 1841-1870
  
• Trade union statistics for the Amalgamated Society of Engineers (ASE), 1851-1918
  
• Trade union statistics for the Amalgamated Society of Carpenters and Joiners (ASCJ), 1863-1912
  
• Official poor law statistics, 1859-1915 and 1919-1939
  
• Wage statistics, 1845-1906
  
• Hours of work statistics, 1900-1913
  
• Small debt statistics from county courts, 1847-1913 and 1938
  

There are five tables in this part of the Great Britain Historical Database :

Rd_pop holds population totals for all registration districts in England and Wales for each census from 1841 to 1911.

Pop_chan holds details of population changes between censuses for all registration districts in England and Wales for each inter-censal period from 1851-1861 to 1901-1911.

Age_sex holds the number of males and females in 5 year age groups for all registration districts in England and Wales for each census from 1851 to 1911, and for all local government districts for each census from 1921 to 1931.

Age_1901 holds a full transcript of the number of males and females in 5 year age groups for all registration districts in England and Wales for the 1901 census with greater detail for ages 13 to 20.

Rd_gaz converts the names of registration districts which appear in the database into the forms used in the GIS.

Rd_gis holds the names and counties of registration districts as they appear in the GIS, and is used for checking names and constructing rd_gaz.

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

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 of Population first gathered data on housing "density", i.e. the number of persons in each household relative to the number of rooms, in 1891, although the first year included here is 1901. In 1891, over-crowding was defined as over 2 persons per room; by 1931 this threshold had dropped to 1.5 persons; and by 1961 to 1 person per room. Up to 1931, the data for each locality and date form a table of numbers of persons against numbers of rooms, and these transcriptions sometimes exclude the rows/columns for the very largest households (see the documentation for individual tables). From 1951 onwards, simpler tables simply list numbers of households in each density category (e.g. over 1 person per room and not more than 1.5 persons).

This is a new edition. Data have been added for 1911 and 1951. Wherever possible, ID numbers have been added for counties and districts which match those used in the digital boundary data created by the GBH GIS, greatly simplifying mapping.

Census: Population: Manipur data was reported at 2,855,794.000 Person in 03-01-2011. This records an increase from the previous number of 2,293,896.000 Person for 03-01-2001. Census: Population: Manipur data is updated decadal, averaging 678,836.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 2,855,794.000 Person in 03-01-2011 and a record low of 284,465.000 Person in 03-01-1901. Census: Population: Manipur data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.

The 1901 Census was taken on 31 March 1901. This is a published report of the 1901 census, and gives statistical tabulations for the information collected. The information is given in the following categories: ages; education; religions; birthplaces; conjugal condition and families; population and habitations; sickness and infirmity; and occupations.

This volume was published in 1904.

(6/5606). 1 vol.

Note:

This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

Analysis of ‘Population by origin and sex, since 1901’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/b90c0f3b-b400-4563-8ae1-5d06e4e98880-stadt-zurich on 16 January 2022.

--- Dataset description provided by original source is as follows ---

Economic resident population of the city of Zurich by origin, sex and year, since 1901.

--- Original source retains full ownership of the source dataset ---

About this dataset

Context

The dataset tabulates the Oxford charter township 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 Oxford charter township. The dataset can be utilized to understand the population distribution of Oxford charter township by age. For example, using this dataset, we can identify the largest age group in Oxford charter township.

Key observations

The largest age group in Oxford charter Township, Michigan was for the group of age 15 to 19 years years with a population of 1,901 (8.47%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Oxford charter Township, Michigan was the 80 to 84 years years with a population of 272 (1.21%). 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 Oxford charter township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Oxford charter township 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 Oxford charter township Population by Age. You can refer the same here

The dataset contains state and gender wise decadal change in the population from 1901 to 2011

About this dataset

Context

The dataset tabulates the New Oxford population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of New Oxford across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of New Oxford was 1,901, a 0.90% increase year-by-year from 2022. Previously, in 2022, New Oxford population was 1,884, an increase of 0.37% compared to a population of 1,877 in 2021. Over the last 20 plus years, between 2000 and 2023, population of New Oxford increased by 222. In this period, the peak population was 1,901 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the New Oxford is shown in this column.
• Year on Year Change: This column displays the change in New Oxford population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 New Oxford Population by Year. You can refer the same here

The island of Ireland is split into 32 different counties, and from 1800 until 1921 the whole island was a part of the United Kingdome of Great Britain and Ireland (although Britain had been a controlling presence on the island for considerably longer than this). In 1921 the island was split into two separate states, where the six counties with the highest population of Protestants formed part of the United Kingdom of Great Britain and Northern Ireland, and the other 26 counties became the Independent Republic of Ireland. From 1821 until 1841, the population of these 26 counties was growing steadily, until the Great Famine from 1845 to 1849 swept across the island, particularly devastating the west and south.

The famine was caused by a Europe-wide potato blight that contributed to mass starvation and death throughout the continent, although it's impact on Ireland was much harsher than anywhere else. The potato blight affected Ireland so severely as the majority of potatoes in Ireland were of a single variety which allowed the disease to spread much faster than in other countries. People in the west and south of Ireland were particularly dependent on potatoes, and these areas were affected more heavily than the north and west, where flax and cereals were the staple. As the potato blight spread, the population became increasingly reliant on dairy and grain products, however a lot of these resources were relocated by the British military to combat food shortages in Britain. Due to disproportional dependency on potatoes, and mismanagement by the British government, over one million people died and a further one million emigrated. The Great Famine lasted from just 1845 to 1849, but it's legacy caused almost a century of population decline, and to this day, the population of Ireland has never exceeded it's pre-famine levels.

The population decline continued well into the twentieth century, during which time the Republic of Ireland achieved independence from the British Empire. After centuries of fighting and rebellion against British rule, Irish nationalists finally gained some independence from Britain in 1921, establishing an Irish Republic in the 26 counties. There was a lot of conflict in Ireland in the early 1900s, through the War of Independence and Irish Civil War, however the population of the Republic began growing again from the 1960s onwards as the quality of life improved and the emigration rate declined. The population was at it's lowest from 1926 to 1971, where it remained at just under three million, but in the following fifty years the population has grown by over two million people.

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, 2019-2023 American Community Survey 5-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..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..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..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..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.

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, 2018-2022 American Community Survey 5-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..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The 2018-2022 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 delineation lists 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.