This graph shows the population density in the federal state of New York from 1960 to 2018. In 2018, the population density of New York stood at 414.7 residents per square mile of land area.

Population Numbers By New York City Neighborhood Tabulation Areas The data was collected from Census Bureaus' Decennial data dissemination (SF1). Neighborhood Tabulation Areas (NTAs), are aggregations of census tracts that are subsets of New York City's 55 Public Use Microdata Areas (PUMAs). Primarily due to these constraints, NTA boundaries and their associated names may not definitively represent neighborhoods. This report shows change in population from 2000 to 2010 for each NTA. Compiled by the Population Division – New York City Department of City Planning.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The map shows population density in Tioga County NY using a quantile classification with 5 data breaks each rounded to the nearest 10 people. The population data is census block level data from the 2010 U.S. Census.

The codes attached are used to support our study. Each of these codes is exported from ArcMap where they were constructed using ModelBuilder.Our study area focuses on New York City, which provides a data-rich urban environment with extreme variations in local population density and diverse types of input data in which to construct multiple methods. In this study area we can then compare the efficacy of multiple methodologies, which employ a strong binary mask paired with a density variable directly derived from the binary mask. We test the following methodologies:

1. Land areas binary mask

2. Building footprint binary mask

3. Building footprint binary mask and area density variable

4. Building footprints binary mask and volume density variable

5. Residential building footprint binary mask

6. Residential building footprint binary mask and area density variable

7. Residential building footprint binary mask and volume density variable

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

A comparison of three city types.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

Includes the error tables, ESRI ArcMap document, accompanying ESRI Geodatabase, ESRI Toolkit and the Python scripts/codes used in the analysis. The error tables are by Census Block for each tested method as well as the calculated grouped error statistics.Our study area focuses on New York City, which provides a data-rich urban environment with extreme variations in local population density and diverse types of input data in which to construct multiple methods. In this study area we can then compare the efficacy of multiple methodologies, which employ a strong binary mask paired with a density variable directly derived from the binary mask. We test the following methodologies:1. Land areas binary mask2. Building footprint binary mask3. Building footprint binary mask and area density variable4. Building footprints binary mask and volume density variable5. Residential building footprint binary mask6. Residential building footprint binary mask and area density variable7. Residential building footprint binary mask and volume density variable

The population density maps presented here for the UNDESERT study areas in Burkina Faso, Benin, Niger and Senegal for 1990, 2000 and 2010 were produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Centro Internacional de Agricultura Tropical (CIAT). CIESIN/CIAT population density grids are available for the entire globe at a 2.5 arc-minutes resolution (http://sedac.ciesin.columbia.edu/data/collection/gpw-v3/sets/browse). The UNDESERT project (EU FP7 243906), financed by the European Commission, Directorate General for Research and Innovation, Environment Program, aims to improve the Understanding and Combating of Desertification to Mitigate its Impact on Ecosystem Services in West Africa. Humans originate and contribute significantly to desertification processes. Based on the CIESIN/CIAT population density grids we want to illustrate how population density changed in the UNDESERT study areas and countries during the last 20 years. Data for 1990 and 2000 were downloaded from the Gridded Population of the World, Version 3 (GPWv3) consisting of estimates of human population by 2.5 arc-minute grid cells and associated data sets dated circa 2000. Data for 2010 were copied from the Gridded Population of the World, Version 3 (GPWv3) consisting in a future estimate of human population by 2.5 arc-minute grid cells. The future estimate population values are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics.

Source: http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density Center for International Earth Science Information Network (CIESIN)/Columbia University, and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World, Version 3 (GPWv3): Population Density Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density. Accessed 28/10/2013 And http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-future-estimates Center for International Earth Science Information Network (CIESIN)/Columbia University, and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World, Version 3 (GPWv3): Population Density Grid, Future Estimates. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-future-estimates. Accessed 28/10/2013

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

Values of parameters.

New York has become one of the worst-affected COVID-19 hotspots and a pandemic epicenter due to the ongoing crisis. This paper identifies the impact of the pandemic and the effectiveness of government policies on human mobility by analyzing multiple datasets available at both macro and micro levels for New York City. Using data sources related to population density, aggregated population mobility, public rail transit use, vehicle use, hotspot and non-hotspot movement patterns, and human activity agglomeration, we analyzed the inter-borough and intra-borough movement for New York City by aggregating the data at the borough level. We also assessed the internodal population movement amongst hotspot and non-hotspot points of interest for the month of March and April 2020. Results indicate a drop of about 80% in people’s mobility in the city, beginning in mid-March. The movement to and from Manhattan showed the most disruption for both public transit and road traffic. The city saw its first case on March 1, 2020, but disruptions in mobility can be seen only after the second week of March when the shelter in place orders was put in effect. Owing to people working from home and adhering to stay-at-home orders, Manhattan saw the largest disruption to both inter- and intra-borough movement. But the risk of spread of infection in Manhattan turned out to be high because of higher hotspot-linked movements. The stay-at-home restrictions also led to an increased population density in Brooklyn and Queens as people were not commuting to Manhattan. Insights obtained from this study would help policymakers better understand human behavior and their response to the news and governmental policies.

Multiple linear regression table with R2, coefficient and p value for input features (population density, normalized busy airport, pre-infected count, pre-death count) and observed factors (post-infected count and post-death count).

ESD provides capital grant funding from the Regional Council Capital Fund available for the State’s Regional Economic Development Council Initiative, which helps drive regional and local economic development across New York State in cooperation with ten Regional Economic Development Councils (“Regional Councils”). Capital grant funding is available for capital-based economic development projects intended to create or retain jobs; prevent, reduce or eliminate unemployment and underemployment; and/or increase business or economic activity in a community or Region. One of the program categories within the program will provide enhanced incentives for projects located in economically distressed areas (census tracts) where investments are needed to spur economic growth. The definition of economically distressed areas (census tracts) can be found below.

For more information and full program guidelines, please see the full program guidelines within the 2025 Available Resources at: https://regionalcouncils.ny.gov/

Economically distressed area shall mean the following based on the census tract for where the project is located:

Severely distressed census tracts shall have at least 25 households receiving public assistance income in the 2023 ACS 5-year estimate and meet at least five of the criteria listed below:Moderately distressed census tracts shall have at least 25 households receiving public assistance income in the 2023 ACS 5-year estimate and meet at least three of the criteria listed below:Slightly distressed census tracts shall have at least 100 households receiving public assistance income in the 2023 ACS 5-year estimate and meet at least two of the criteria listed below:o
Population loss between the 2023 ACS 5-year estimate and the 2019 ACS 5-year estimate – an absolute loss in population.o
Unemployment rate (2023 ACS 5-year estimate) higher than the State’s rate.o
Private sector employment growth rate (2023 ACS 5-year estimate) over the preceding 5 years was lower than the State’s OR private sector employment (2023 ACS 5-year estimate) as a percentage of total employment was less than the State’s.o
Percentage of households receiving public assistance (2023 ACS 5-year estimate) was greater than the statewide percentage.o
Poverty rate (2023 ACS 5-year estimate) was greater than the State’s poverty rate.o
Per Capita Income change (2023 ACS 5-year estimate) over the preceding five years was less than the growth in the consumer price index (CPI) for all urban consumers nationally OR per capita income was less than the State’s per capita income.

Attributes:

Field Name

Data Type

Description

Census Tract

Number

The 11 digit geoid associated with each census tract in New York State. Census tracts are small, relatively permanent statistical subdivisions of a county that average about 4,000 inhabitants.

Stress Level

Number

The stress level number (1-4) associated with the census tract.

Stress Level Description

Text

The stress level description (Not Distressed, Slight Distress, Moderate Distress, Severe Distress) associated with the census tract.

Stress Level Color

Text

The stress level color (Gray, Light Orange, Dark Orange, Red) associated with the census tract.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.