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.

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.

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.

GPWv4 is a gridded data product that depicts global population data from the 2010 round of Population and Housing Censuses. The Population Density, 2015 layer represents persons per square kilometer for year 2015. Data SummaryGPWv4 is constructed from national or subnational input areal units of varying resolutions. The native grid cell size is 30 arc-seconds, or ~1 km at the equator. Separate grids are available for population count, population density, estimated land area, and data quality indicators; which include the water mask represented in this service. Population estimates are derived by extrapolating the raw census counts to estimates for the 2010 target year. The development of GPWv4 builds upon previous versions of the data set (Tobler et al., 1997; Deichmann et al., 2001; Balk et al., 2006).The full GPWv4 data collection will consist of population estimates for the years 2000, 2005, 2010, 2015, and 2020, and will include grids for estimates of total population, age, sex, and urban/rural status. However, this release consists only of total population estimates for the year 2015. This data is being released now to allow users access to the population grids.Recommended CitationCenter for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ. Accessed DAY MONTH YEAR

This dataset contains the New York City Population By Community Districts.The community boards of the New York City government are the appointed advisory groups of the community districts of the five boroughs. There are currently 59 community districts, including twelve in Manhattan, twelve in the Bronx, eighteen in Brooklyn, fourteen in Queens, and three in Staten Island.

The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution.

Purpose: To provide estimates of population density for the years 2000, 2005, 2010, 2015, and 2020, based on counts consistent with national censuses and population registers, as raster data to facilitate data integration.

Recommended Citation(s)*: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. Accessed DAY MONTH YEAR.

Estimates of human population for the year 2015 by 2.5 arc-minute grid cells. 2015 global population density from CIESIN Gridded Population of the World version 4. Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ Accessed 5 April 2017.

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 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 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. 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 and beyond, 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.

This resource is a member of a series. 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) System (MTS). The MTS 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 because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division 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 Bureau 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.

Estimates of human population for the year 2015 by 2.5 arc-minute grid cells. 2015 global population density from CIESIN Gridded Population of the World version 4. Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ Accessed 5 April 2017.

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.

GPWv4 is a gridded data product that depicts global population data from the 2010 round of Population and Housing Censuses. The Population Density, 2015 layer represents persons per square kilometer for year 2015. Data Summary:GPWv4 is constructed from national or subnational input areal units of varying resolutions. The native grid cell size is 30 arc-seconds, or ~1 km at the equator. Separate grids are available for population count, population density, estimated land area, and data quality indicators; which include the water mask represented in this service. Population estimates are derived by extrapolating the raw census counts to estimates for the 2010 target year. The development of GPWv4 builds upon previous versions of the data set (Tobler et al., 1997; Deichmann et al., 2001; Balk et al., 2006).The full GPWv4 data collection will consist of population estimates for the years 2000, 2005, 2010, 2015, and 2020, and will include grids for estimates of total population, age, sex, and urban/rural status. However, this release consists only of total population estimates for the year 2015. This data is being released now to allow users access to the population grids.Recommended Citation:Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ. Accessed DAY MONTH YEAR

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

Values of parameters.

This study calculated the cumulative energy and greenhouse gas (GHG) life cycle and cost profiles of transitional aerobic membrane bioreactors (AeMBR) and anaerobic MBRs (AnMBR). Membrane bioreactors (MBR) represent a promising technology for decentralized wastewater treatment and can produce recycled water to displace potable water. Energy recovery is also possible with methane generated from AnMBRs. In this study, scenarios for these technologies were investigated for different scale systems serving various population densities under various climate conditions with multiple methane recovery options. Details of the GHG life cycle and cost profiles for the AeMBR and AnMBR can be found in AeMBR_LCI_Cost_9-9-15.xls and AnMBR_LCI_Cost_9-9-15.xls respectively. Results of the previously described comparisons can be found can be found in MBR_LCIAResults_9-9-15.xlsx. This dataset is associated with the following publication: Cashman, S., C. Ma, J. Mosley, J. Garland, B. Crone, and X. Xue. Energy and greenhouse gas life cycle assessment and cost analysis of aerobic and anaerobic membrane bioreactor systems: Influence of scale, population density, climate, and methane recovery. Bioresource Technology. Elsevier Online, New York, NY, USA, 254: 56-66, (2018).

The data in this file are population densities of native pearly mussels (three species of Unionidae) and non-native dreissenid mussels (two species: the zebra mussel Dreissena polymorpha and the quagga mussel Dreissena rostriformis bugensis) in the freshwater tidal Hudson River, New York. Population densities are areally weighted, so they give the average density over the entire study area.