In 2023, about 19.5 million people populated the New York-Newark-Jersey City metropolitan area, the largest metropolitan area in the United States. This is a slight increase from the 18.92 million people that lived there in 2010.

Graph and download economic data for Resident Population in New York-Newark-Jersey City, NY-NJ-PA (MSA) (NYTPOP) from 2000 to 2022 about NJ, New York, PA, NY, residents, population, and USA.

Graph and download economic data for Resident Population in New York (NYPOP) from 1900 to 2024 about NY, residents, population, and USA.

In 2023, the metropolitan area of New York-Newark-Jersey City had the biggest population in the United States. Based on annual estimates from the census, the metropolitan area had around 19.5 million inhabitants, which was a slight decrease from the previous year. The Los Angeles and Chicago metro areas rounded out the top three. What is a metropolitan statistical area? In general, a metropolitan statistical area (MSA) is a core urbanized area with a population of at least 50,000 inhabitants – the smallest MSA is Carson City, with an estimated population of nearly 56,000. The urban area is made bigger by adjacent communities that are socially and economically linked to the center. MSAs are particularly helpful in tracking demographic change over time in large communities and allow officials to see where the largest pockets of inhabitants are in the country. How many MSAs are in the United States? There were 421 metropolitan statistical areas across the U.S. as of July 2021. The largest city in each MSA is designated the principal city and will be the first name in the title. An additional two cities can be added to the title, and these will be listed in population order based on the most recent census. So, in the example of New York-Newark-Jersey City, New York has the highest population, while Jersey City has the lowest. The U.S. Census Bureau conducts an official population count every ten years, and the new count is expected to be announced by the end of 2030.

About this dataset

Context

The dataset tabulates the Great Valley town 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 Great Valley town 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 Great Valley town was 1,951, a 0.36% decrease year-by-year from 2022. Previously, in 2022, Great Valley town population was 1,958, a decline of 0.81% compared to a population of 1,974 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Great Valley town decreased by 183. In this period, the peak population was 2,137 in the year 2003. 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 Great Valley town is shown in this column.
• Year on Year Change: This column displays the change in Great Valley town 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 Great Valley town Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Oyster Bay, New York population pyramid, which represents the Oyster Bay town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Oyster Bay, New York, is 26.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Oyster Bay, New York, is 31.5.
• Total dependency ratio for Oyster Bay, New York is 58.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Oyster Bay, New York is 3.2.

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 for the Oyster Bay town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Oyster Bay town for the selected age group is shown in the following column.
• Population (Female): The female population in the Oyster Bay town for the selected age group is shown in the following column.
• Total Population: The total population of the Oyster Bay town for the selected age group is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Oyster Bay town Population by Age. You can refer the same here

Context

There are a number of Kaggle datasets that provide spatial data around New York City. For many of these, it may be quite interesting to relate the data to the demographic and economic characteristics of nearby neighborhoods. I hope this data set will allow for making these comparisons without too much difficulty.

Exploring the data and making maps could be quite interesting as well.

Content

This dataset contains two CSV files:

1. nyc_census_tracts.csv

   This file contains a selection of census data taken from the ACS DP03 and DP05 tables. Things like total population, racial/ethnic demographic information, employment and commuting characteristics, and more are contained here. There is a great deal of additional data in the raw tables retrieved from the US Census Bureau website, so I could easily add more fields if there is enough interest.

   I obtained data for individual census tracts, which typically contain several thousand residents.

2. census_block_loc.csv

   For this file, I used an online FCC census block lookup tool to retrieve the census block code for a 200 x 200 grid containing New York City and a bit of the surrounding area. This file contains the coordinates and associated census block codes along
   with the state and county names to make things a bit more readable to users.

   Each census tract is split into a number of blocks, so one must extract the census tract code from the block code.

Acknowledgements

The data here was taken from the American Community Survey 2015 5-year estimates (https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml).

The census block coordinate data was taken from the FCC Census Block Conversions API (https://www.fcc.gov/general/census-block-conversions-api)

As public data from the US government, this is not subject to copyright within the US and should be considered public domain.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races (5-year estimate) in New York County, NY (B03002009E036061) from 2009 to 2023 about New York County, NY; non-hispanic; New York; estimate; NY; 5-year; persons; population; and USA.

Many residents of New York City speak more than one language; a number of them speak and understand non-English languages more fluently than English. This dataset, derived from the Census Bureau's American Community Survey (ACS), includes information on over 1.7 million limited English proficient (LEP) residents and a subset of that population called limited English proficient citizens of voting age (CVALEP) at the Community District level. There are 59 community districts throughout NYC, with each district being represented by a Community Board.

In 2023, the GDP of the New York metro area amounted to *** trillion chained 2017 U.S. dollars. This is an increase from 2021, when the GDP of the New York metro area was **** trillion dollars. New York CityThe New York metro area’s GDP has steadily risen in the last two decades from *** trillion U.S. dollars in 2001 to **** trillion U.S. dollars in 2023. In September 2023, the New York- Newark-Jersey City area had an unemployment rate of *** percent. It also had the highest population in the country in 2022 at ***** million people. New York City’s economy is one of the greatest in the country and is home to many Fortune 500 companies, including Big Pharma’s Bristol-Myers Squibb. Industries such as media, real estate, fashion and entertainment are some of the most prominent in the area. The finance industry in New York City, also known as Wall Street, is one of the leading financial centers of the world and houses the New York Stock Exchange and NASDAQ. The region is also home to one of the largest trading industries in the country at the Port of New York and New Jersey. This port includes a large estuary, regional airports, and a plethora of rail and road networks. Silicon Alley is one of the country’s largest technology industry hubs, including internet, telecommunications, and biotechnology. In 2022, there were some ****** business establishments in the region that focused on professional, scientific, and technical services.

About this dataset

Context

The dataset tabulates the data for the Stony Point, New York population pyramid, which represents the Stony Point town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Stony Point, New York, is 24.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Stony Point, New York, is 29.5.
• Total dependency ratio for Stony Point, New York is 53.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Stony Point, New York is 3.4.

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 for the Stony Point town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Stony Point town for the selected age group is shown in the following column.
• Population (Female): The female population in the Stony Point town for the selected age group is shown in the following column.
• Total Population: The total population of the Stony Point town for the selected age group is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Stony Point town Population by Age. You can refer the same here

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Richmond County, NY (B03002011E036085) from 2009 to 2023 about Richmond County, NY; non-hispanic; New York; estimate; NY; 5-year; persons; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Queens County, NY (B03002021E036081) from 2009 to 2023 about Queens County, NY; latino; hispanic; New York; estimate; NY; 5-year; persons; population; and USA.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under the following licenses: Public Domain

The study analyzes the forces leading to or impeding the assimilation of 18- to 32-year-olds from immigrant backgrounds that vary in terms of race, language, and the mix of skills and liabilities their parents brought to the United States. To make sure that what we find derives specifically from growing up in an immigrant family, rather than simply being a young person in New York, a comparison group of people from native born White, Black, and Puerto Rican backgrounds was also studied. The sample was drawn from New York City (except for Staten Island) and the surrounding counties in the inner part of the New York-New Jersey metropolitan region where the vast majority of immigrants and native born minority group members live and grow up. The study groups make possible a number of interesting comparisons. Unlike many other immigrant groups, the West Indian first generation speaks English, but the dominant society racially classifies them as Black. The study explored how their experiences resemble or differ from native born African Americans. Dominicans and the Colombian-Peruvian-Ecuadoran population both speak Spanish, but live in different parts of New York, have different class backgrounds prior to immigration, and, quite often, different skin tones. The study compared them to Puerto Rican young people, who, along with their parents, have the benefit of citizenship. Chinese immigrants from the mainland tend to have little education, while young people with overseas Chinese parents come from families with higher incomes, more education, and more English fluency. Respondents were divided into eight groups depending on their parents' origin. Those of immigrant ancestry include: Jewish immigrants from the former Soviet Union; Chinese immigrants from the mainland, Taiwan, Hong Kong, and the Chinese Diaspora; immigrants from the Dominican Republic; immigrants from the English-speaking countries of the West Indies (including Guyana but excluding Haiti and those of Indian origin); and immigrants from Colombia, Ecuador, and Peru. These groups composed 44 percent of the 2000 second-generation population in the defined sample area. For comparative purposes, Whites, Blacks, and Puerto Ricans who were born in the United States and whose parents were born in the United States or Puerto Rico were also interviewed. To be eligible, a respondent had to have a parent from one of these groups. If the respondent was eligible for two groups, he or she was asked which designation he or she preferred. The ability to compare these groups with native born Whites, Blacks, and Puerto Ricans permits researchers to investigate the effects of nativity while controlling for race and language background. About two-thirds of second-generation respondents were born in the United States, mostly in New York City, while one-third were born abroad but arrived in the United States by age 12 and had lived in the country for at least 10 years, except for those from the former Soviet Union, some of whom arrived past the age of 12. The project began with a pilot study in July 1996. Survey data collection took place between November 1999 and December 1999. The study includes demographic variables such as race, ethnicity, language, age, education, income, family size, country of origin, and citizenship status.

About this dataset

Context

The dataset tabulates the Great Neck 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 Great Neck 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 Great Neck was 11,007, a 0.38% decrease year-by-year from 2022. Previously, in 2022, Great Neck population was 11,049, a decline of 0.51% compared to a population of 11,106 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Great Neck increased by 1,384. In this period, the peak population was 11,111 in the year 2020. 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 Great Neck is shown in this column.
• Year on Year Change: This column displays the change in Great Neck 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 Great Neck Population by Year. You can refer the same here

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Nassau County, NY (B03002011E036059) from 2009 to 2023 about Nassau County, NY; non-hispanic; New York; estimate; NY; 5-year; persons; population; and USA.

About this dataset

Context

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

Key observations

The largest age group in Great Valley, New York was for the group of age 60-64 years with a population of 199 (10.76%), according to the 2021 American Community Survey. At the same time, the smallest age group in Great Valley, New York was the 75-79 years with a population of 50 (2.70%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Great Valley town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Great Valley town 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 Great Valley town Population by Age. You can refer the same here

Overview and Inspiration

I wanted to make some geospatial visualizations to convey the current severity of COVID19 in different parts of the U.S..

I liked the NYTimes COVID dataset, but it was lacking information on county boundary shape data, population per county, new cases / deaths per day, and per capita calculations, and county demographics.

After a lot of work tracking down the different data sources I wanted and doing all of the data wrangling and joins in python, I wanted to open-source the final enriched data set in order to give others a head start in their COVID-19 related analytic, modeling, and visualization efforts.

This dataset is enriched with county shapes, county center point coordinates, 2019 census population estimates, county population densities, cases and deaths per capita, and calculated per day cases / deaths metrics. It contains daily data per county back to January, allowing for analyizng changes over time.

UPDATE: I have also included demographic information per county, including ages, races, and gender breakdown. This could help determine which counties are most susceptible to an outbreak.

How this data can be used

Geospatial analysis and visualization - Which counties are currently getting hit the hardest (per capita and totals)? - What patterns are there in the spread of the virus across counties? (network based spread simulations using county center lat / lons) -county population densities play a role in how quickly the virus spreads? -how does a specific county/state cases and deaths compare to other counties/states? Join with other county level datasets easily (with fips code column)

Content Details

See the column descriptions for more details on the dataset

Visualizations and Analysis Examples

COVID-19 U.S. Time-lapse: Confirmed Cases per County (per capita)

https://github.com/ringhilterra/enriched-covid19-data/blob/master/example_viz/covid-cases-final-04-06.gif?raw=true" alt="">-

Other Data Notes

• Please review nytimes README for detailed notes on Covid-19 data - https://github.com/nytimes/covid-19-data/
• The only update I made in regards to 'Geographic Exceptions', is that I took 'New York City' county provided in the Covid-19 data, which has all cases for 'for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) and replaced the missing FIPS for those rows with the 'New York County' fips code 36061. That way I could join to a geometry, and then I used the sum of those five boroughs population estimates for the 'New York City' estimate, which allowed me calculate 'per capita' metrics for 'New York City' entries in the Covid-19 dataset

Acknowledgements

• Special thanks to NYTimes for all of their hard work gathering and consolidating all of the U.S. COVID19 related data on daily basis. Their git repo https://github.com/nytimes/covid-19-data/
• Also, thanks to ykzeng for the county population density estimates: https://github.com/ykzeng/covid-19/tree/master/data-

In 2020, about 82.66 percent of the total population in the United States lived in cities and urban areas. As the United States was one of the earliest nations to industrialize, it has had a comparatively high rate of urbanization over the past two centuries. The urban population became larger than the rural population during the 1910s, and by the middle of the century it is expected that almost 90 percent of the population will live in an urban setting. Regional development of urbanization in the U.S. The United States began to urbanize on a larger scale in the 1830s, as technological advancements reduced the labor demand in agriculture, and as European migration began to rise. One major difference between early urbanization in the U.S. and other industrializing economies, such as the UK or Germany, was population distribution. Throughout the 1800s, the Northeastern U.S. became the most industrious and urban region of the country, as this was the main point of arrival for migrants. Disparities in industrialization and urbanization was a key contributor to the Union's victory in the Civil War, not only due to population sizes, but also through production capabilities and transport infrastructure. The Northeast's population reached an urban majority in the 1870s, whereas this did not occur in the South until the 1950s. As more people moved westward in the late 1800s, not only did their population growth increase, but the share of the urban population also rose, with an urban majority established in both the West and Midwest regions in the 1910s. The West would eventually become the most urbanized region in the 1960s, and over 90 percent of the West's population is urbanized today. Urbanization today New York City is the most populous city in the United States, with a population of 8.3 million, while California has the largest urban population of any state. California also has the highest urbanization rate, although the District of Columbia is considered 100 percent urban. Only four U.S. states still have a rural majority, these are Maine, Mississippi, Montana, and West Virginia.