The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

In 2024, the population of Africa was projected to grow by 2.27 percent compared to the previous year. The population growth rate on the continent has been constantly over 2.5 percent from 2000 onwards, and it peaked at 2.63 percent in 2013. Despite a slowdown in the growth rate after that, the continent's population will continue to increase significantly in the coming years. The second-largest population worldwide In 2023, the total population of Africa amounted to almost 1.5 billion. The number of inhabitants had grown steadily in the previous decades, rising from approximately 831 million in 2000. Driven by a decreasing mortality rate and a higher life expectancy at birth, the African population was forecast to increase to about 2.5 billion individuals by 2050. Africa is currently the second most populous continent worldwide after Asia. However, forecasts showed that Africa could gradually close the gap and almost reach the size of the Asian population in 2100. By that year, Africa might count 3.8 billion people, compared to 4.6 billion in Asia. The world's youngest continent The median age in Africa corresponded to 19.2 years in 2024. Although the median age has increased in recent years, the continent remains the youngest worldwide. In 2023, roughly 40 percent of the African population was aged 15 years and younger, compared to a global average of 25 percent. Africa recorded not only the highest share of youth but also the smallest elderly population worldwide. As of the same year, only three percent of Africa's population was aged 65 years and older. Africa and Latin America were the only regions below the global average of ten percent. On the continent, Niger, Uganda, and Angola were the countries with the youngest population in 2023.

About this dataset

Context

The dataset tabulates the Belle Plaine 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 Belle Plaine 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 Belle Plaine was 7,416, a 0.40% decrease year-by-year from 2022. Previously, in 2022, Belle Plaine population was 7,446, an increase of 0.31% compared to a population of 7,423 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Belle Plaine increased by 3,572. In this period, the peak population was 7,446 in the year 2022. 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 Belle Plaine is shown in this column.
• Year on Year Change: This column displays the change in Belle Plaine 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 Belle Plaine Population by Year. You can refer the same here

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.

About this dataset

Context

The dataset tabulates the Florida 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 Florida 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 2022, the population of Florida was 22,244,823, a 1.91% increase year-by-year from 2021. Previously, in 2021, Florida population was 21,828,069, an increase of 1.10% compared to a population of 21,589,602 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Florida increased by 6,198,675. In this period, the peak population was 22,244,823 in the year 2022. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Florida is shown in this column.
• Year on Year Change: This column displays the change in Florida 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 Florida Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Yellowstone County 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 Yellowstone County 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 Yellowstone County was 170,843, a 0.57% increase year-by-year from 2022. Previously, in 2022, Yellowstone County population was 169,870, an increase of 1.45% compared to a population of 167,438 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Yellowstone County increased by 41,299. In this period, the peak population was 170,843 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 Yellowstone County is shown in this column.
• Year on Year Change: This column displays the change in Yellowstone County 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 Yellowstone County Population by Year. You can refer the same here

Chart and table of population level and growth rate for the state of Florida from 1900 to 2024.

Canada's population doubled between 1956 and 2006. Human population growth is an important factor in determining society's influence on the environment. One example of our growing population's influence on the environment is illustrated vis-à-vis the increasing number of motor vehicles registered over time.

In 2023, the annual population growth in the United States stood at 0.49 percent. Between 1961 and 2023, the figure dropped by 1.17 percentage points, though the decline followed an uneven course rather than a steady trajectory.

This indicator gives an insight of the past population development of a city. It shows hot spots, where annual growth is predominant, as well as cold spots, where population declined in the last 8 years. The values are given in percentage and the visualization shows the difference compared to a neutral population balance (0% growth). Population growth is used as an indicator in urban planning to determine urban areas that are developing, growing and attracting people to move there. Population decline on the other hand is an indicator for urban areas that are losing residential population which can have many different causes, e.g. increased rents, number of crimes or segregation. A decline can also happen in areas where residential lots have been transformed to office or commercial housing units.Data Source:Popular Demographics Points: This feature layer provides Esri 2018 demographic estimates for popular variables including: 2018 Total Population, 2018 Household Population, 2018 Median Age, 2018 Median Household Income, 2018 Per Capita Income, 2018 Diversity Index and many more. Data is available for block group centroids.

About this dataset

Context

The dataset tabulates the Kirkland 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 Kirkland 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 Kirkland was 91,194, a 0.37% increase year-by-year from 2022. Previously, in 2022, Kirkland population was 90,856, a decline of 0.60% compared to a population of 91,404 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Kirkland increased by 46,090. In this period, the peak population was 92,975 in the year 2019. 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 Kirkland is shown in this column.
• Year on Year Change: This column displays the change in Kirkland 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 Kirkland Population by Year. You can refer the same here

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

Market Overview: The Global Population-Based Health Services Market is projected to exhibit a robust growth over the forecast period of 2023-2030, expanding from a valuation of USD 24.98 Billion in 2022 to reach USD 62.17 Billion by 2030, exhibiting a CAGR of 12.4%. Market growth is attributed to the increasing focus on preventive healthcare, the growing prevalence of chronic diseases, and the adoption of value-based care models. Population-based health services encompass a wide range of healthcare interventions aimed at improving the health of specific population groups, such as targeted screenings, disease management programs, and community health outreach initiatives. Market Trends and Drivers: Key trends in the population-based health services market include the increasing use of artificial intelligence (AI) and machine learning (ML) to analyze patient data, the rise of telemedicine and remote monitoring technologies, and the growing emphasis on health equity. Factors driving market growth include the increasing awareness of the importance of preventative healthcare, the aging population, and the growing burden of non-communicable diseases. Moreover, the expansion of government initiatives and the rising adoption of value-based reimbursement models are expected to further boost market growth in the coming years.

Theory suggests that the drivers of demographic variation and local adaptation are shared and may feedback on one other. Despite some evidence for these links in controlled settings, the relationship between local adaptation and demography remains largely unexplored in natural conditions. Using 10 years of demographic data and two reciprocal transplant experiments, we tested predictions about the relationship between the magnitude of local adaptation and demographic variation (population growth rates and their elasticities to vital rates) across 10 populations of a well-studied annual plant. In both years, we found a strong unimodal relationship between mean home-away local adaptation and stochastic population growth rates. Other predicted links were either weakly or not supported by our data. Our results suggest that declining and rapidly growing populations exhibit reduced local adaptation, potentially due to maladaptation and relaxed selection, respectively., This dataset includes long-term data collected using observations and environmetnal sensors, data on population dynamics derived from field census data, and data from 2 years of reciprocal transplants in field conditions. Data describing population dynamics have been processed from raw census data using matrix population models. All other data processing is performed using code that is archived along with the data., Annotated code necessary to reproduce the analyses and figures presented in the associated manuscript are included in this archive., # Data from: Local adaptation is highest in populations with stable long-term growth

Lauren N. Carley et al.

lauren.n.carley@gmail.com

STRUCTURE OF THIS ARCHIVE:

Details on the purpose of each file in these folders, and their subdirectories, is provided below, following the general outline:

• Clarkia-LTREB-transplant-archive/
  • README.txt
  • 1-data/
  • 2-analyses/
  • 2a-seed-prediction/
  • 2b-aster/
  • 2c-dist-calcs/
    • out/
  • 2d-dist-analyses/
  • 2e-permutation-tests/
    • Fdists/
  • 3-figures/
  • supplemental/

NOTE: Throughout the whole directory, variables in datasets are unitless unless otherwise defined, and "NA" values represent missing data unless otherwise defined.

Clarkia-LTREB-transplant-archive-R2/

This directory contains all of the other subdirectories, which take you through data processing, modeling, and analysis step by step.

It also contains one file:

README.txt

You are curren...

Population growth will result in a significant anthropogenic environmental change worldwide through increases in developed land (DL) consumption. DL consumption is an important environmental and socioeconomic process affecting humans and ecosystems. Attention has been given to DL modeling inside highly populated cities. However, modeling DL consumption should expand to non-metropolitan areas where arguably the environmental consequences are more significant. Here, we study all counties within the conterminous U.S. and based on satellite-derived product (National Land Cover Dataset 2001) we calculate the associated DL for each county. By using county population data from the 2000 census we present a comparative study on DL consumption and we propose a model linking population with expected DL consumption. Results indicate distinct geographic patterns of comparatively low and high consuming counties moving from east to west. We also demonstrate that the relationship of DL consumption with population is mostly linear, altering the notion that expected population growth will have lower DL consumption if added in counties with larger population. Added DL consumption is independent of a county’s starting population and only dependent on whether the county belongs to a Metropolitan Statistical Area (MSA). In the overlapping MSA and non-MSA population range there is also a constant DL efficiency gain of approximately 20km2 for a given population for MSA counties which suggests that transitioning from rural to urban counties has significantly higher benefits in lower populations. In addition, we analyze the socioeconomic composition of counties with extremely high or low DL consumption. High DL consumption counties have statistically lower Black/African American population, higher poverty rate and lower income per capita than average in both NMSA and MSA counties. Our analysis offers a baseline to investigate further land consumption strategies in anticipation of growing population pressures.

Environmental variables at the UPR Agricultural Experimental Station in Adjuntas, Puerto Rico.

This study on Prussia by Gerd Hohorst presents a number of estimations of income on a regional basis since 1816 and examines the meaning of the term ‘leading regions’ (as equivalent for: ‘leading sectors’), as well as the competing explanations for the differentiation of regional incomes in Prussia. In fact, this is a first attempt to verify the hypothesis of an agricultural cycle sui generis by means of an interregional comparison of the Prussian provinces East Prussia and Rhineland (implying regional differences as to the process of industrialisation within Prussia).As a conclusion, it can be said that the income and employment divide, as it could be assessed for the development of the Prussian regions already in 1816, was still increasing in the course of the 19th century. These findings support the Sector-Export-Basis Thesis (Borchardt) as well as the Myrdal Thesis.Furthermore, it seems that the population pressure, which was counteracted by the expansion of the inner regional agriculture, led to an increase in the per capita income at first, whereas an intensification of the protoindustrial capacities only held the per capita income on a constant level. Already in the pre-industrial age, this phenomenon had caused a growing divergence in the regional (per capita) incomes because of the complex interrelation of basic agricultural conditions and population growth. Later, particularly the technical progress and the discovery of new raw materials gave rise to a universal restructuring of the named locations. Hereby, regions with protoindustrial experience and an appropriate potential of population were especially favoured, as these factors constitute the elements of a sales-oriented infrastructure and a greater potential of demand; now agricultural monostructures, which could stand their ground against the demo-economic impulses of change, were disadvantaged although they created agricultural bases for export. Since the development in the former led to an increase in their per capita income due to rising labour productivity, the income differences increased as well. This trend was weakened by the fact that the growing population compelled progresses in productivity also in rural regions. On the other hand, the income gap was widened by the declining demand for protoindustrial products which where squeezed out of the market by industrial competition.
Factual classification of corresponding data tables in HISTAT:A.1 Income development trends in the Prussian provinces in marks (1820-1883)A.2 Per capita income in the Prussian provinces (constant weights)in marks (1821-1884)A.2 Per capita income in the Prussian provinces (variable weights) in marks (1820-1883)A.3 Estimated results of the per capita income in the Prussian administrative districts (unrevised, 1816-1883)A.4 Regional development of the per capita income in the Prussian provinces as compared to the whole of Prussia (1816-1913) B. Development of the total price index in the Prussian provinces (1820-1883) C. Estimated results for the agricultural labour force in the Prussian administrative districts, unrevised (1816-1883)

The global Population Health Management Solutions market is projected to reach a valuation of USD 93.2 billion by 2033, exhibiting a CAGR of 12.3% during the forecast period (2023-2033). The increasing prevalence of chronic diseases, rising healthcare costs, and growing emphasis on preventive healthcare are key factors driving market growth. Population health management solutions enable healthcare providers to identify, stratify, and target populations based on their health risks and needs, allowing for more efficient and cost-effective care delivery. Rising healthcare expenditure around the world has led to the adoption of value-based payment models, such as bundled payments and pay-for-performance, which incentivize healthcare providers to deliver high-quality care while reducing costs. Population health management solutions play a crucial role in supporting these models by providing data and analytics that help providers identify and address the needs of their populations. Moreover, the growing adoption of electronic health records (EHRs) and other healthcare information technology (HIT) systems has facilitated the collection and analysis of large amounts of data, which can be leveraged to improve population health outcomes.

Capture and Mortality Metrics of Mule Deer in SE WyomingThis data was collected from 2010-2014 of helicopter captured mule deer near Douglas, Wyoming, USA. The information is coded and look-up tables are provided on additional worksheets contained in the Excel file that explain what each code represents. Mule deer were GPS radio-collared and followed for the duration of the study. Marked deer were recaptured annually to test for CWD and pregnancy.CWD_MuleDeer_WY.xlsx

About this dataset

Context

The dataset tabulates the Princeton 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 Princeton 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 2022, the population of Princeton was 22,900, a 13.87% increase year-by-year from 2021. Previously, in 2021, Princeton population was 20,110, an increase of 14.69% compared to a population of 17,534 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Princeton increased by 19,315. In this period, the peak population was 22,900 in the year 2022. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Princeton is shown in this column.
• Year on Year Change: This column displays the change in Princeton 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 Princeton Population by Year. You can refer the same here