in New York City, the population density is more than 27 thousand people per sq. The average population density of metropolitan statistical areas (city and surrounding communities) is 284 people per sq. More than three hundred urban neighborhoods in the United States have populations up to 100 thousand New York City, with more than eight million residents, is the largest. It is projected that 89 percent of the nation’s population will live in urban areas by 2050. Today, it is determined that 83 percent of the American population resides in urban neighborhoods, up from 64 percent in 1950. citizens were more apparent to dwell in the highest-density urban areas in 2020 than in 1980, 1990, 2000, or 2010. states compare to the rest of the world by population density? Population density by metropolitan statistical area The map below created by Reddit user JoeFalchetto compares the population density of the U.S. The least populated state is Alaska (1.26 per sq mi). Rhode Island is the second-most densely populated U.S. is the most densely populated part of the nation. population, or nearly 2 out of every 3 Americans, live in the red line, known as the “100 Mile Zone.” is 332 million (2020), and the country ranks 146th in population density (87 pop/mi2 or 34 pop/km2).Īpproximately 65 percent of the U.S. per square mile has grown from 4.5 in 1790 to 87.4 in 2010. During this time, the number of people living in the U.S. Population density has been monitored for more than 200 years in the U.S. As an Amazon Associate, we earn from qualifying purchases. Median centres of population for the 48 U.S.The mean center of population for the U.S.Changes to the mean centre of population for the U.S.counties with highest population density, people per square mile (2020) counties with lowest density, people per squre mile (2020) Population density by metropolitan statistical area.states compare to the rest of the world by population density? However, it is important to note that this data is only as good as the census data on which it is based. Using high-quality, recent census data, the top-down approach provides detailed and valuable representations of the spatial distribution of human populations. Our statistical modelling techniques are based on the assessment of the relationship between geospatial covariates and population density, while the area-based method distributes the total population evenly either within the corresponding administrative unit or across its building footprints when available. Some national statistical agencies can publish very detailed data whilst others are very generalised (the totals are presented in just a few large administrative units).Ĭensus data as administrative unit totals, particularly when very generalised, can be disaggregated to grid cell level, for example at approximately 100x100m, through statistical modelling techniques or area-based calculations developed by GRID3 partner WorldPop. The size of these units and administrative unit level at which the census data are shared varies between countries. This bottom-up approach does not, however, replace the need for a full census, which usually includes a more precise collection of demographics and socioeconomics, as well as a housing census.Ĭensus data are typically released as administrative unit totals.
Modelling can also fill in data gaps for inaccessible areas. Population modelling can provide more regularly updated population estimates, complementing a decadal population census. In addition, grid cells facilitate the integration of various datasets into the model, providing a consistent framework to work with, and eliminating the impact of inaccuracies in boundary data that can affect the statistical model.
They can be aggregated over various levels of administrative units, but also over areal units that don’t follow administrative boundaries, such as a hospital catchment area. Gridded population estimates at 100x100m resolution are particularly useful as they provide decision-makers and data users with the flexibility to aggregate population estimates into different units in existing enumeration areas or a custom size space. GRID3 uses a Bayesian approach, which allows us to both predict gridded population estimates, and provide the uncertainty surrounding that prediction to help make better-informed decisions.