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.

This statistic provides projected figures for the Gross Metropolitan Product (GMP) of the United States in 2021, by metropolitan area. Only the 100 leading metropolitan areas are shown here. In 2022, the GMP of the New York-Newark-Jersey City metro area is projected to be around of about **** trillion U.S. dollars. Los Angeles metropolitan areaA metropolitan area in the U.S. is characterized by a relatively high population density and close economic ties through the area, albeit, without the legal incorporation that is found within cities. The Gross Metropolitan Product is measured by the Bureau of Economic Analysis under the U.S. Department of Commerce and includes only metropolitan areas. The GMP of the Los Angeles-Long Beach-Anaheim metropolitan area located in California is projected to be among the highest in the United States in 2021, amounting to *** trillion U.S. dollars. The Houston-The Woodlands-Sugar Land, Texas metro area is estimated to be approximately *** billion U.S. dollars in the same year. The Los Angeles metro area had one of the largest populations in the country, totaling ****** million people in 2021. The Greater Los Angeles region has one of the largest economies in the world and is the U.S. headquarters of many international car manufacturers including Honda, Mazda, and Hyundai. Its entertainment industry has generated plenty of tourism and includes world famous beaches, shopping, motion picture studios, and amusement parks. The Hollywood district is known as the “movie capital of the U.S.” and has its historical roots in the country’s film industry. Its port, the Port of Los Angeles and the Port of Long Beach are aggregately one of the world’s busiest ports. The Port of Los Angelesgenerated some ****** million U.S. dollars in revenue in 2019.

This statistics shows a list of the top 20 largest-metropolitan areas in the United States in 2010, by land area. Riverside-San Bernardino-Ontario in California was ranked first enclosing an area of 70,612 square kilometers.

Annual population estimates as of July 1st, by census metropolitan area and census agglomeration, single year of age, five-year age group and gender, based on the Standard Geographical Classification (SGC) 2021.

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. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

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. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

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. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

Chart and table of population level and growth rate for the Sao Paulo, Brazil metro area from 1950 to 2025.

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. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

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. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the urban footprint. There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

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.

After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

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. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

Unemployment data are at a county level. Median income data are from the Oregon part of the Portland-Vancouver-Hillsboro, OR-WA Metro Area.All data are disaggregated by race and/or ethnicity. Margins of error are provided for each estimate.

In 2023, the population of the Salt Lake City metropolitan area in the United States was about 1.27 million people. This represented a slight increase when compared to the previous year.

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.

After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems. Methods See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

Data Cleaning All code used is in the zipped folder Data S5 in the eLife publication. Before cleaning the data, we ensured that all reported trees for each city were located within the greater metropolitan area of the city (for certain inventories, many suburbs were reported - some within the greater metropolitan area, others not). First, we renamed all columns in the received .csv sheets, referring to the metadata and according to our standardized definitions (Table S4). To harmonize tree health and condition data across different cities, we inspected metadata from the tree inventories and converted all numeric scores to a descriptive scale including “excellent,” “good”, “fair”, “poor”, “dead”, and “dead/dying”. Some cities included only three points on this scale (e.g., “good”, “poor”, “dead/dying”) while others included five (e.g., “excellent,” “good”, “fair”, “poor”, “dead”). Second, we used pandas in Python (W. McKinney & Others, 2011) to correct typos, non-ASCII characters, variable spellings, date format, units used (we converted all units to metric), address issues, and common name format. In some cases, units were not specified for tree diameter at breast height (DBH) and tree height; we determined the units based on typical sizes for trees of a particular species. Wherever diameter was reported, we assumed it was DBH. We standardized health and condition data across cities, preserving the highest granularity available for each city. For our analysis, we converted this variable to a binary (see section Condition and Health). We created a column called “location_type” to label whether a given tree was growing in the built environment or in green space. All of the changes we made, and decision points, are preserved in Data S9. Third, we checked the scientific names reported using gnr_resolve in the R library taxize (Chamberlain & Szöcs, 2013), with the option Best_match_only set to TRUE (Data S9). Through an iterative process, we manually checked the results and corrected typos in the scientific names until all names were either a perfect match (n=1771 species) or partial match with threshold greater than 0.75 (n=453 species). BGS manually reviewed all partial matches to ensure that they were the correct species name, and then we programmatically corrected these partial matches (for example, Magnolia grandifolia-- which is not a species name of a known tree-- was corrected to Magnolia grandiflora, and Pheonix canariensus was corrected to its proper spelling of Phoenix canariensis). Because many of these tree inventories were crowd-sourced or generated in part through citizen science, such typos and misspellings are to be expected. Some tree inventories reported species by common names only. Therefore, our fourth step in data cleaning was to convert common names to scientific names. We generated a lookup table by summarizing all pairings of common and scientific names in the inventories for which both were reported. We manually reviewed the common to scientific name pairings, confirming that all were correct. Then we programmatically assigned scientific names to all common names (Data S9). Fifth, we assigned native status to each tree through reference to the Biota of North America Project (Kartesz, 2018), which has collected data on all native and non-native species occurrences throughout the US states. Specifically, we determined whether each tree species in a given city was native to that state, not native to that state, or that we did not have enough information to determine nativity (for cases where only the genus was known). Sixth, some cities reported only the street address but not latitude and longitude. For these cities, we used the OpenCageGeocoder (https://opencagedata.com/) to convert addresses to latitude and longitude coordinates (Data S9). OpenCageGeocoder leverages open data and is used by many academic institutions (see https://opencagedata.com/solutions/academia). Seventh, we trimmed each city dataset to include only the standardized columns we identified in Table S4. After each stage of data cleaning, we performed manual spot checking to identify any issues.

Chart and table of population level and growth rate for the Los Angeles metro area from 1950 to 2025.

description: The TIGER/Line shapefiles and related database files (.dbf) (NTAD) 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. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.; abstract: The TIGER/Line shapefiles and related database files (.dbf) (NTAD) 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. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

These data provide a spatial representation of the population change 1980 - 2000. Map Shows the census tracts that have experienced a doubling of population between 1980 and 1990 and between 1990 and 2000 in the central Arizona-Phoenix area.