Existing studies show how population growth and rising incomes will cause a massive increase in the future global demand for food. We add to the literature by estimating the potential effect of increases in human weight, caused by rising BMI and height, on future calorie requirements. Instead of using a market based approach, the estimations are solely based on human energy requirements for maintenance of weight. We develop four different scenarios to show the effect of increases in human height and BMI. In a world where the weight per age-sex group would stay stable, we project calorie requirements to increases by 61.05 percent between 2010 and 2100. Increases in BMI and height could add another 18.73 percentage points to this. This additional increase amounts to more than the combined calorie requirements of India and Nigeria in 2010. These increases would particularly affect Sub-Saharan African countries, which will already face massively rising calorie requirements due to the high population growth. The stark regional differences call for policies that increase food access in currently economically weak regions. Such policies should shift consumption away from energy dense foods that promote overweight and obesity, to avoid the direct burden associated with these conditions and reduce the increases in required calories. Supplying insufficient calories would not solve the problem but cause malnutrition in populations with weak access to food. As malnutrition is not reducing but promoting rises in BMI levels, this might even aggravate the situation.

This map is part of an interactive Story Map series about global change in the US.With the global human population expected to exceed 8 billion people by 2030, our species is already irreversibly changing the future of our planet. The US itself is expected to grow by 16.5% to over 360 million people, making it the third largest country in the world, behind India and China. This population increase isn’t distributed evenly - 81% of people will live in cities, urban, and suburban areas, which will continue to shape how resources are produced, transported, and consumed. The percent of foreign-born and second-generation immigrants in the US is also expected to rise in the future, contributing to an increasingly diverse population. Across the globe, immigration will likely account for significant population changes in the near future, as climate change fuels drought, crop failures, and political instability, creating climate refugees particularly among countries who do not have the infrastructure to mitigate or adapt to global change. Numbers aren’t the only thing that matter: people of different socioeconomic backgrounds use resources differently, both within and between countries.If the rest of the world used energy as intensely as the United States does, the world population would need more than 4 entire Earths to provide us with the resources to feed this rate consumption. This unfortunately means that even regions of the US that contribute less towards the problems of global change will still feel their impacts. To ensure a high quality of life for all citizens, we must address not only population growth, but also excess consumption of and reliance on resources across different regions. Geographic, population, and economic differences among regions can provide opportunities for success in the face of global change. Renewable energy sources have created entrepreneurial economic ventures, and communities have found environmental solutions through forming sustainable local food systems. Environmental justice movements are working now to ensure that all citizens have access to nature, recreational areas, and a healthy future for all.

About this dataset

Context

The dataset tabulates the Oceanside 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 Oceanside 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 Oceanside was 170,020, a 0.81% decrease year-by-year from 2022. Previously, in 2022, Oceanside population was 171,408, a decline of 0.87% compared to a population of 172,916 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Oceanside increased by 8,789. In this period, the peak population was 176,081 in the year 2018. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 Oceanside is shown in this column.
• Year on Year Change: This column displays the change in Oceanside 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 Oceanside Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Wyoming 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 Wyoming 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 2024, the population of Wyoming was 587,618, a 0.44% increase year-by-year from 2023. Previously, in 2023, Wyoming population was 585,067, an increase of 0.53% compared to a population of 581,978 in 2022. Over the last 20 plus years, between 2000 and 2024, population of Wyoming increased by 93,638. In this period, the peak population was 587,618 in the year 2024. 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 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the Wyoming is shown in this column.
• Year on Year Change: This column displays the change in Wyoming 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 Wyoming Population by Year. You can refer the same here

Abstract

The key objective of every census is to count every person (man, woman, child) resident in the country on census night, and also collect information on assorted demographic (sex, age, marital status, citizenship) and socio-economic (education/qualifications; labour force and economic activity) information, as well as data pertinent to household and housing characteristics. This count provides a complete picture of the population make-up in each village and town, of each island and region, thus allowing for an assessment of demographic change over time.

The need for a national census became obvious to the Census Office (Bureau of Statistics) during 1997 when a memo was submitted to government officials proposing the need for a national census in an attempt to update old socio-economic figures. The then Acting Director of the Bureau of Statistics and his predecessor shared a similar view: that the 'heydays' and 'prosperity' were nearing their end. This may not have been apparent, as it took until almost mid-2001 for the current Acting Government Statistician to receive instructions to prepare planning for a national census targeted for 2002. It has been repeatedly said that for adequate planning at the national level, information about the characteristics of the society is required. With such information, potential impacts can be forecast and policies can be designed for the improvement and benefit of society. Without it, the people, national planners and leaders will inevitably face uncertainties.

Geographic coverage

National coverage as the Population Census covers the whole of Nauru.

Analysis unit

• Household
• Individual (in a private household dwelling, institutions and non-private dwelling).

Universe

The Census covers all individuals living in private and non-private dwellings and institutions.

Kind of data

Census/enumeration data [cen]

Sampling deviation

There is no sampling for the population census, full coverage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was based on the Pacific Islands Model Population and Housing Census Form and the 1992 census, and comprised two parts: a set of household questions, asked only of the head of household, and an individual questionnaire, administered to each household member. Unlike the previous census, which consisted of a separate household form plus two separate individual forms for Nauruans and non-Nauruans, the 2 002 questionnaire consisted of only one form separated into different parts and sections. Instructions (and skips) were desi

The questionnaire cover recorded various identifiers: district name, enumeration area, house number, number of households (family units) residing, total number of residents, gender, and whether siblings of the head of the house were also recorded. The second page, representing a summary page, listed every individual residing within the house. This list was taken by the enumerator on the first visit, on the eve of census night. The first part of the census questionnaire focused on housing-related questions. It was administered only once in each household, with questions usually asked of the household head. The household form asked the same range of questions as those covered in the 1992 census, relating to type of housing, structure of outer walls, water supply sources and storage, toilet and cooking facilities, lighting, construction materials and subsistence-type activities. The second part of the census questionnaire focused on individual questions covering all household members. This section was based on the 1992 questions, with notable differences being the exclusion of income-level questions and the expansion of fertility and mortality questions. As in 1992, a problem emerged during questionnaire design regarding the question of who or what should determine a ‘Nauruan’. Unlike the 1992 census, where the emphasis was on blood ties, the issue of naturalisation and citizenship through the sale of passports seriously complicated matters in 2 002. To resolve this issue, it was decided to apply two filtering processes: Stage 1 identified persons with tribal heritage through manual editing, and Stage 2 identified persons of Nauruan nationality and citizenship through designed skips in the questionnaire that were incorporated in the data-processing programming.

The topics of questions for each of the parts include: - Person Particulars: - name - relationship - sex - ethnicity - religion - educational attainment - Economic Activity (to all persons 15 years and above): - economic activity - economic inactive - employment status - Fertility: - Fertility - Mortality - Labour Force Activity: - production of cash crops - fishing - own account businesses - handicrafts. - Disability: - type of disability - nature of disability - Household and housing: - electricity - water - tenure - lighting - cooking - sanitation - wealth ownerships

Cleaning operations

Coding, data entry and editing Coding took longer than expected when the Census Office found that more quality-control checks were required before coding could take place and that a large number of forms still required attention. While these quality-control checks were supposed to have been done by the supervisors in the field, the Census Office decided to review all census forms before commencing the coding. This process took approximately three months, before actual data processing could begin. The amount of additional time required to recheck the quality of every census form meant that data processing fell behind schedule. The Census Office had to improvise, with a little pressure from external stakeholders, and coding, in conjunction with data entry, began after recruiting two additional data entry personnel. All four Census Office staff became actively involved with coding, with one staff member alternating between coding and data entry, depending on which process was dropping behind schedule. In the end, the whole process took almost two months to complete. Prior to commencing data entry, the Census Office had to familiarise itself with the data entry processing system. For this purpose, SPC’s Demography/Population Programme was invited to lend assistance. Two office staff were appointed to work with Mr Arthur Jorari, SPC Population Specialist, who began by revising their skills for the data processing software that had been introduced by Dr McMurray. This training attachment took two weeks to complete. Data entry was undertaken using the 2 .3 version of the US Census Bureau’s census and surveying processing software, or CSPro2.3. This version was later updated to CSPro2.4, and all data were transferred accordingly. Technical assistance for data editing was provided by Mr Jorari over a two-week period. While most edits were completed during this period, it was discovered that some batches of questionnaires had not been entered during the initial data capturing. Therefore, batch-edit application had to be regenerated. This process was frequently interrupted by power outages prevailing at the time, which delayed data processing considerably and also required much longer periods of technical support to the two Nauru data processing staff via phone or email (when available).

Data appraisal

Data was compared with Administrative records after the Census to review the quality and reliability of the data.

This layer shows Population. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

This table contains 24 series, with data for years 2001 - 2001 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Time of day, needed immediate care for a minor health problem (3 items: Needed immediate care for a minor health problem during regular hours; Needed immediate care for a minor health problem during the middle of the night; Needed immediate care for a minor health problem during evenings and weekends ...), Characteristics (8 items: Number of persons; Coefficient of variation; number of persons; Low 95% confidence interval; number of persons; High 95% confidence interval; number of persons ...).

Population by type of longstanding health problem, sex and age

Table of INEBase Population suffering some perceived chronic disease or health problem by sex and social class based on the reference person's occupation. Population aged 15 years old and over. National. European Health Survey

About this dataset

Context

The dataset tabulates the Fort Myers 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 Fort Myers 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 Fort Myers was 97,372, a 1.50% increase year-by-year from 2022. Previously, in 2022, Fort Myers population was 95,930, an increase of 2.82% compared to a population of 93,302 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Fort Myers increased by 45,513. In this period, the peak population was 97,372 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 Fort Myers is shown in this column.
• Year on Year Change: This column displays the change in Fort Myers 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 Fort Myers Population by Year. You can refer the same here

Problems with purchase or order of goods or services over the Internet in the last 3 months by demographic characteristics. National.

This layer shows Population and Poverty Status. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of people whose income in the past 12 months is below poverty level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right.Current Vintage: 2018-2022ACS Table(s): B17017, C17002, DP02, DP03Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

포털 유럽연합 데이터 Population aged 15−64 by county and occurrence of health problem which restricts the ability to work (2002−2011)

Model errors and p-values assessed using population count error as a proportion of actual district population: |Predicted – Actual|/Actual.

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..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..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 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..Armed Forces data are not shown for the population 65 years and over..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 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..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.

Example of FJSP.

About this dataset

Context

The dataset tabulates the population of Russells Point by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Russells Point across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 55.0% of total population being male. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Russells Point is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Russells Point 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 Russells Point Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the New Hampshire 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 Hampshire 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 2024, the population of New Hampshire was 1.41 million, a 0.49% increase year-by-year from 2023. Previously, in 2023, New Hampshire population was 1.4 million, an increase of 0.40% compared to a population of 1.4 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of New Hampshire increased by 168,647. In this period, the peak population was 1.41 million in the year 2024. 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 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the New Hampshire is shown in this column.
• Year on Year Change: This column displays the change in New Hampshire 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 Hampshire Population by Year. You can refer the same here