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.

Note: These layers were compiled by Esri's Demographics Team using data from the Census Bureau's American Community Survey. These data sets are not owned by the City of Rochester.Overview of the map/data: This map shows the percentage of the population living below the federal poverty level over the previous 12 months, shown by tract, county, and state boundaries. Estimates are from the 2018 ACS 5-year samples. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer will be updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico.Census tracts with no population are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

A dataset listing New York counties by population for 2024.

What is heat vulnerability? Vulnerability to heat is how likely a person is to be injured or harmed during periods of hot weather. Heat vulnerability has been linked to individuals’ characteristics (health status, age, race, income, language spoken, etc.) as well as certain aspects of the community where one lives (environment, community demographics). These characteristics or “heat vulnerability factors” can play an important role in one’s ability to adapt to heat. What is the Heat Vulnerability Index? The effects of extreme heat on health can often be prevented. Heat-related deaths and illness are more common during the summer, especially in vulnerable populations. Since vulnerability and adaptability to extreme heat in New York State (NYS) is a growing concern, the New York State Department of Health (NYSDOH) created the Heat Vulnerability Index (HVI) to help local and state public health officials identify and map heatvulnerable areas and populations in NYS (excluding New York City which has its own HVI). The HVI can assist in directing adaptation resources based on characteristics of vulnerable populations in that community and can inform long-term heat-mitigation planning efforts in the community. The HVI can help local agencies make decisions to: set up cooling centers in rural and vulnerable areas where many do not have access to air-conditioning at home provide transportation to and from cooling centers in low income neighborhoods where there may not be public transportation or few people own vehicles include risk communication and alert messaging in multiple languages especially among communities with high proportions of people who do not understand English wellarrange home visits of people in high risk groups like the elderly living alone How was the HVI developed? The HVI was developed to identify census tracts with populations that may have increased heat vulnerability. It is based on thirteen environmental and socio-demographic heat vulnerability factors that were identified from previous studies. Census tracts are subdivisions of counties and are defined by the US Census Bureau to collect, provide and present statistical data. Census tract level information for these heat vulnerability factors was obtained from the 2006-2010 and 2008-2012 US Census Bureau American Community Surveys (ACS) and 2011 National Land Cover Database (NLCD) for 2,723 census tracts in NYS (excluding New York City). Census tracts with zero population or missing census tract data were excluded. The 13 factors were grouped into four categories that represent different aspects of heat vulnerability, which in turn were used to estimate the overall HVI for each census tract. The four heat vulnerability categories include 1) language vulnerability; 2) socio-economic vulnerability; 3) environmental and urban vulnerability; and 4) elderly isolation and elderly vulnerability. The HVI and four heat vulnerability categories were mapped to display populations in NYS that are most vulnerable to heat. More Information on HVI:Heat Vulnerability Index: Statewide and County HVI maps can be found at https://www.health.ny.gov/environmental/weather/vulnerability_index/index.htm For more information on the HVI: Nayak SG et al. Development of the heat Vulnerability Index. Public Health 2017. Open access at https://www.sciencedirect.com/science/article/pii/S003335061730327X