In 2025, China's cities made up one-fourth of the global land area, consisting of built-up urban areas with populations of 500,000 and more. India's urban areas made up **** percent of the land area classified as large urban cities worldwide.

In 2023, New York led the ranking of the largest built-up urban areas worldwide, with a land area of ****** square kilometers. Boston-Providence and Tokyo-Yokohama were the second and third largest megacities globally that year.

Data on European cities were collected in the Urban Audit and in the Large City Audit project. The projects' ultimate goal is to contribute towards the improvement of the quality of urban life: it supports the exchange of experience among European cities; it helps to identify best practices; it facilitates benchmarking at the European level and provides information on the dynamics within the cities and with their surroundings.

At the city level, the Urban Audit contains more than 130 variables and more than 50 indicators. These indicators are derived from the variables collected by the European Statistical System.

The data is published in 20 tables within 2 main groups, plus a perception survey table:

Cities and greater cities (urb_cgc)

Population on 1 January by age groups and sex - cities and greater cities (urb_cpop1)
Population structure - cities and greater cities (urb_cpopstr)
Population by citizenship and country of birth - cities and greater cities (urb_cpopcb)
Fertility and mortality - cities and greater cities (urb_cfermor)

Living conditions - cities and greater cities (urb_clivcon)

Education - cities and greater cities (urb_ceduc)

Culture and tourism - cities and greater cities (urb_ctour)
Labour market - cities and greater cities (urb_clma)
Economy and finance - cities and greater cities (urb_cecfi)
Transport - cities and greater cities (urb_ctran)
Environment - cities and greater cities (urb_cenv)

Functional Urban Area (urb_luz)

Population on 1 January by age groups and sex - Functional Urban Area (urb_lpop1)
Population structure - Functional Urban Area (urb_lpopstr)
Population by citizenship and country of birth - Functional Urban Area (urb_lpopcb)
Fertility and mortality - Functional Urban Area (urb_lfermor)
Living conditions - Functional Urban Area (urb_llivcon)
Education - Functional Urban Area (urb_leduc)
Labour market - Functional Urban Area (urb_llma)
Transport - Functional Urban Area (urb_ltran)
Environment - Functional Urban Area (urb_lenv)
Perception survey results (urb_percep)

Data has been collected on two spatial levels in the Urban Audit:

• The City (C) according to the administrative definition, as the basic level,
• The Functional Urban Area (FUA) being an approximation of the functional urban zone centered around the city

After each decennial census, the Census develops Urban Areas that consist of a central core and adjacent densely settled territory that together contain at least 2,500 residents. For transportation planning purposes, however, only areas with a population of 5,000 are considered urban. These areas of over 5,000 population are then adjusted outwardly to capture additonal areas and minimize situations where a given roadway goes into and out of the original census area.Areas with a population of between 5,000 and 49,99 are called Small Urban Areas. These areas are assigned a unique number that must be between 600 and 999. In 2010, there are 152 such areas in Texas.Areas with a population of between 50,000 and 199,999 are called Urbanized Areas and are assigned a unique number less than 600. In 2010, there are 21 such areas.Areas with a population of of 200,000 or more are called Large Urbanized Areas and are assigned a unique number less than 600. In 2010, there are 13 such areas.All Urbanized Areas and Large Urbanized Areas require a Metropolitan Planning Organization (MPO) to shepherd the transportation planning process. MPOs that cover Large Urbanized Areas are considered Transportation Management Areas.FHWA approved these boundaries in 2014.Date valid as of: December 2014Publish Date: May 2015Update Frequency: Every 10 years after decennial census

Retirement Notice: This item is in mature support as of June 2023 and will retire in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. The layers going from 1:1 to 1:1.5M present the 2010 Census Urbanized Areas (UA) and Urban Clusters (UC). A UA consists of contiguous, densely settled census block groups (BGs) and census blocks that meet minimum population density requirements (1000 people per square mile (ppsm) / 500 ppsm), along with adjacent densely settled census blocks that together encompass a population of at least 50,000 people. A UC consists of contiguous, densely settled census BGs and census blocks that meet minimum population density requirements, along with adjacent densely settled census blocks that together encompass a population of at least 2,500 people, but fewer than 50,000 people. The dataset covers the 50 States plus the District of Columbia within United States. The layer going over 1:1.5M presents the urban areas in the United States derived from the urban areas layer of the Digital Chart of the World (DCW). It provides information about the locations, names, and populations of urbanized areas for conducting geographic analysis on national and large regional scales.To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to USA Census Urban Areas.

Hong Kong HK: Population in Largest City: as % of Urban Population data was reported at 99.637 % in 2017. This records an increase from the previous number of 99.540 % for 2016. Hong Kong HK: Population in Largest City: as % of Urban Population data is updated yearly, averaging 99.382 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 100.000 % in 2010 and a record low of 94.548 % in 1974. Hong Kong HK: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong – Table HK.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

The urban accessibility (UA) classification measures the degree of urban influence New Zealand’s urban areas have on surrounding rural areas. It classifies the geographic accessibility of rural statistical area 1s (SA1s) and small urban areas according to their proximity, or degree of remoteness, to larger urban areas. This classification provides increased understanding of the heterogeneity of rural areas and small urban areas and will allow more extensive analysis and reporting. Understanding the degree of urban accessibility or remoteness is important as it has a major influence on the employment sector, accessibility to services, and population composition and change. The methodology uses drive time from an SA1 address weighted centroid to the outside boundary of the nearest major, large, and medium urban area (from Stats NZ urban rural (UR) classification) to classify rural SA1s and small urban areas to one of five categories of accessibility or remoteness. The Open Source Routing Machine service using the OpenStreetMap road network is used to calculate the drive times.

A concordance between SA1 and Urban Accessibility can be found on Aria.

Rural SA1s and small urban areas are classified to the following categories:

·High urban accessibility: 0 to15 minutes from major urban areas

·Medium urban accessibility: 15 to 25 minutes from major urban areas 0 to 25 minutes from large urban areas 0 to 15 minutes from medium urban areas

·Low urban accessibility: 25 to 60 minutes from major or large urban areas 15 to 60 minutes from medium urban areas

·Remote: 60 to 120 minutes from major, large or medium urban areas

·Very remote: more than 120 minutes from major, large or medium urban areas

For more information refer to: Urban accessibility - methodology and classification.

The full classification is shown below: 111 Major urban area

112 Large urban area

113 Medium urban area

221 High urban accessibility

222 Medium urban accessibility

223 Low urban accessibility

224 Remote

225 Very remote

331 Inland water

332 Inlet

333 Oceanic

Note: Areas of 221 High urban accessibility and 222 Medium urban accessibility may be regarded as peri-urban in nature and combined with urban areas for analytical purposes.

Topicality: 01-01-2023Projection: New Zealand Transverse Mercator (NZTM)This layer contains the archive of the functional urban areas as maintained by StatsNZ and defined by StatsNZ.The functional urban area (FUA) classification identifies small urban areas and rural areas that are integrated with major, large, and medium urban areas to create FUAs. The FUA classification uses the urban rural (UR) geography to demarcate urban areas, and statistical area 1 areas(SA1s) to demarcate surrounding hinterland (the commuting zone) within FUAs, and rural and water areas outside FUAs.This layer get updated yearly with the latest boundary data. You can use this layer when you need any year of boundary data in your map. By setting a filter on the dataset year you can filter on specific year of the dataset.For information about the fields in this dataset go to the Data tabFUA type (TFUA)FUAs are further categorised by population size. The urban core’s population rather than the entire FUA’s population is used to maintain consistency between the descriptions of UR urban area and FUA type. The categories are, by code: ​1. Metropolitan area – more than 100,000 residents living in the urban core2. Large regional centre – urban core population 30,000–99,9993. Medium regional centre – urban core population 10,000–29,9994. Small regional centre – urban core population 5,000–9,9999. Area outside functional urban area​The Greymouth urban area population is less than 10,000 but is classified as a medium regional centre, consistent with its treatment as a medium urban area in the UA classification. ​To differentiate from the UR classification, when referring to FUAs by name, their FUA type should also be mentioned, for example, Christchurch metropolitan area, Whangarei regional centre. ​FUA indicator (IFUA)The IFUA classifies UR2023 urban areas and rural SA1s according to their character within their FUA. The indicators, with their codes in brackets, are: urban area within functional urban area – urban core (101), secondary urban core (102), satellite urban area (103),rural area within functional urban area – hinterland (201)area outside functional urban area – land area outside functional urban area (901), water area outside functional urban area (902).The layer is further generalised by Eagle Technology for improved performance on the web, therefore it doesn't fully represent the official boundaries.If you only need the latest boundary data in your map you can use the current version of this dataset. All the current versions of Stats NZ Boundary layers can be found here.The official dataset can be found on https://datafinder.stats.govt.nz.This layer is offered by Eagle Technology (Official Esri Distributor). Eagle Technology offers services that can be used in the ArcGIS platform. The Content team at Eagle Technology updates the layers on a regular basis and regularly adds new content to the Living Atlas. By using this content and combining it with other data you can create new information products quickly and easily.If you have any questions or comments about the content, please let us now at livingatlas@eagle.co.nz

Actual value and historical data chart for United States Population In The Largest City Percent Of Urban Population

Thailand TH: Population in Largest City: as % of Urban Population data was reported at 29.142 % in 2017. This records an increase from the previous number of 28.917 % for 2016. Thailand TH: Population in Largest City: as % of Urban Population data is updated yearly, averaging 35.514 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 40.429 % in 1969 and a record low of 28.054 % in 2010. Thailand TH: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Thailand – Table TH.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

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. 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 2,644 Urban Areas (UAs) in this data release with either a minimum population of 5,000 or a housing unit count of 2,000 units. Each urban area is identified by a five-character numeric census code that may contain leading zeros.

2023 Functional Urban Area update

For the 2023 FUA, there have been minor updates from the 2018 FUAs to align with changes to urban rural (UR) boundaries and statistical area 1 (SA1) composition. FUA 2023 is still based on the analysis of 2018 Census of Population and Dwellings commuting data. The Wanaka urban area, whose population has grown to be more than 10,000 based on population estimates, has been reclassified to a medium urban area in the 2023 UR and a medium regional centre in the FUA type.

​

Description

This dataset is the definitive version of the Functional Urban Area boundaries as at 1 January 2023, as defined by Stats NZ.

​

The functional urban area (FUA) classification identifies small urban areas and rural areas that are integrated with major, large, and medium urban areas to create FUAs. In 2023, there are 53 FUAs,excluding ‘land area outside functional urban area’ (9001) and ‘water area outside functional urban area’ (9002). The FUA classificationuses the urban rural (UR) geography to demarcate urban areas, and statistical area 1 areas(SA1s) to demarcate surrounding hinterland (the commuting zone) within FUAs, and rural and water areas outside FUAs.

​

FUAs represent a populated urban core/s and its commuting zone. Workplace address and usual residence address data from the 2018 Census of Population and Dwellings were used to identify satellite urban areas (1,000–4,999 residents), rural settlements and other rural SA1s from which at least 40 percent of workers commuted to urban areas with more than 5,000 residents.

​

FUA numbering and naming

The FUA classification identifies FUAs by the name of the most highly populated urban area it contains, for example, the Christchurch FUA includes the Christchurch urban core and Rangiora, Kaiapoi, and Rolleston secondary urban cores. There is one exception to the naming rule. The Paraparaumu-Waikanae-Paekakariki conurbation and surrounding hinterland is named Kapiti Coast.

​

The FUA classification has a two-level hierarchical structure, joined together to create each FUA code. Level 1 is classified by FUA type (TFUA) a one-digit code and level 2, which has three-digit codes numbered approximately north to south. Some examples are: 1001 Auckland, 2001 Whangārei, 3001 Cambridge, and 4001 Kaitāia.

​

FUA type (TFUA)

FUAs are further categorised by population size. The urban core’s population rather than the entire FUA’s population is used to maintain consistency between the descriptions of UR urban area and FUA type. The categories are, by code:

​

1 Metropolitan area – more than 100,000 residents living in the urban core,

2 Large regional centre – urban core population 30,000–99,999,

3 Medium regional centre – urban core population 10,000–29,999,

4 Small regional centre – urban core population 5,000–9,999, and,

9 Area outside functional urban area.

​

The Greymouth urban area population is less than 10,000 but is classified as a medium regional centre, consistent with its treatment as a medium urban area in the UA classification.

​

To differentiate from the UR classification, when referring to FUAs by name, their FUA type should also be mentioned, for example, Christchurch metropolitan area, Whangarei regional centre.

​

FUA indicator (IFUA)

The IFUA classifies UR2023 urban areas and rural SA1s according to their character within their FUA. The indicators, with their codes in brackets, are:

• urban area within functional urban area – urban core (101), secondary urban core (102), satellite urban area (103),

• rural area within functional urban area – hinterland (201),

• area outside functional urban area – land area outside functional urban area (901), water area outside functional urban area (902).

​

Further information on the urban rural indicator is available on the Stats NZ classification tool Ariā.

For more information please refer to the Statistical standard for geographic areas 2023.

​

Clipped version

This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries.

​

Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

​

Digital data

Digital boundary data became freely available on 1 July 2007.

​

To download geographic classifications in table formats such as CSV please use Ariā

In 2020, about 82.66 percent of the total population in the United States lived in cities and urban areas. As the United States was one of the earliest nations to industrialize, it has had a comparatively high rate of urbanization over the past two centuries. The urban population became larger than the rural population during the 1910s, and by the middle of the century it is expected that almost 90 percent of the population will live in an urban setting. Regional development of urbanization in the U.S. The United States began to urbanize on a larger scale in the 1830s, as technological advancements reduced the labor demand in agriculture, and as European migration began to rise. One major difference between early urbanization in the U.S. and other industrializing economies, such as the UK or Germany, was population distribution. Throughout the 1800s, the Northeastern U.S. became the most industrious and urban region of the country, as this was the main point of arrival for migrants. Disparities in industrialization and urbanization was a key contributor to the Union's victory in the Civil War, not only due to population sizes, but also through production capabilities and transport infrastructure. The Northeast's population reached an urban majority in the 1870s, whereas this did not occur in the South until the 1950s. As more people moved westward in the late 1800s, not only did their population growth increase, but the share of the urban population also rose, with an urban majority established in both the West and Midwest regions in the 1910s. The West would eventually become the most urbanized region in the 1960s, and over 90 percent of the West's population is urbanized today. Urbanization today New York City is the most populous city in the United States, with a population of 8.3 million, while California has the largest urban population of any state. California also has the highest urbanization rate, although the District of Columbia is considered 100 percent urban. Only four U.S. states still have a rural majority, these are Maine, Mississippi, Montana, and West Virginia.

This database provides construction of Large Urban Regions (LUR) in Russia. A Large Urban Region (LUR) can be defined as an aggregation of continuous statistical units around a core that are economically dependent on this core and linked to it by economic and social strong interdependences. The main purpose of this delineation is to make cities comparable on the national and world scales and to make comparative social-economic urban studies. Aggregating different municipal districts around a core city, we construct a single large urban region, which allows to include all the area of economic influence of a core into one statistical unit (see Rogov & Rozenblat, 2020 for more details) thus, changing a city position in a global urban hierarchy. In doing so we use four principal urban concepts (Pumain et al., 1992): political definition, morphological definition, functional definition and conurbation that we call Large Urban Region. We constructed Russian LURs using criteria such as population distribution, road networks, access to an airport, distance from a core, presence of multinational firms. In this database, we provide population data for LURs and their administrative units.

2010 Rural-Urban Commuting Area Codes (revised 7/3/2019) , joined to SD, SPA, and CSA as of Dec. 2023.Data from https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/. Downloaded 1/9/2024.Primary RUCA Codes, 20101 Metropolitan area core: primary flow within an urbanized area (UA)2 Metropolitan area high commuting: primary flow 30% or more to a UA3 Metropolitan area low commuting: primary flow 10% to 30% to a UA4 Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC)5 Micropolitan high commuting: primary flow 30% or more to a large UC6 Micropolitan low commuting: primary flow 10% to 30% to a large UC7 Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC)8 Small town high commuting: primary flow 30% or more to a small UC9 Small town low commuting: primary flow 10% to 30% to a small UC10 Rural areas: primary flow to a tract outside a UA or UC99 Not coded: Census tract has zero population and no rural-urban identifier informationSecondary RUCA Codes, 20101 Metropolitan area core: primary flow within an urbanized area (UA)1No additional code1.1Secondary flow 30% to 50% to a larger UA2 Metropolitan area high commuting: primary flow 30% or more to a UA2No additional code2.1Secondary flow 30% to 50% to a larger UA3 Metropolitan area low commuting: primary flow 10% to 30% to a UA3No additional code4 Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC)4No additional code4.1Secondary flow 30% to 50% to a UA5 Micropolitan high commuting: primary flow 30% or more to a large UC5No additional code5.1Secondary flow 30% to 50% to a UA6 Micropolitan low commuting: primary flow 10% to 30% to a large UC6No additional code7 Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC)7No additional code7.1Secondary flow 30% to 50% to a UA7.2Secondary flow 30% to 50% to a large UC8 Small town high commuting: primary flow 30% or more to a small UC8No additional code8.1Secondary flow 30% to 50% to a UA8.2Secondary flow 30% to 50% to a large UC9 Small town low commuting: primary flow 10% to 30% to a small UC9No additional code10 Rural areas: primary flow to a tract outside a UA or UC10No additional code10.1Secondary flow 30% to 50% to a UA10.2Secondary flow 30% to 50% to a large UC10.3Secondary flow 30% to 50% to a small UC99 Not coded: Census tract has zero population and no rural-urban identifier informationData Sources:Population data for census tracts, by urban-rural components, 2010:U.S. Census Bureau, Census of Population and Housing, 2010. Summary File 1, FTP download: https://www.census.gov/census2000/sumfile1.htmlAssignment of census tracts to specific urban areas or to rural status was completed using ESRI's ArcMap software and Census Bureau shape files:U.S. Census Bureau. Tiger/Line Shapefiles, Census Tracts and Urban Areas, 2010: https://www.census.gov/programs-surveys/geography.htmlCensus tract commuting flows, 2006-2010:U.S. Census Bureau, American Community Survey 2006-2010 Five-year estimates. Special Tabulation: Census Transportation Planning Products, Part 3, Worker Home-to-Work Flow Tables. https://www.fhwa.dot.gov/planning/census_issues/ctpp/data_products/2006-2010_table_list/sheet04.cfmTract-to-tract commuting flow files were constructed from ACS data as part of a special tabulation for the Department of Transportation—the Census Transportation Planning Package. To derive estimates for small geographic units such as census tracts, information collected annually from over 3.5 million housing units was combined across 5 years (2006-2010). As with all survey data, ACS estimates are not exact because they are based on a sample. In general, the smaller the estimate, the larger the degree of uncertainty associated with it.

Land Use / Land Cover (LULC) information product over Abidjan (Ivory Coast) contains spatial explicit information on different land use and land cover occurring in the Larger Urban Area and Core City Area of the City of Abidjan for past (2005) and current (2018) dates. The Larger Urban Area LU/LC nomenclature is at an aggregated Level 1 or 2. The input data for the Larger Urban Area was Ikonos (2005) and Sentinel-2 (2019). The Core City Area has detailed LU/LC nomenclature that is either at Level 3 or 4. The input data for the Core City Area was the Very High Resolution (VHR) data of Ikonos (2005) and WorldView-2 (2018/2019)..

This dataset is associated with the McDonald et al. paper, entitled "The urban tree cover and temperature disparity in US urbanized areas: Quantifying the effect of income across 5,723 communities". Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use NAIP imagery to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other places. We compared tree cover to summer surface temperature, as measured using Thematic Mapper imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5⁰C hotter (surface temperature) than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks often have at least 30% less tree cover and are at least 4.0⁰C hotter. Even after controlling for population density and built-up intensity, the association between income and tree cover is significant, as is the association between race and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, a compensatory value of $56 billion dollars ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would close the tree cover disparity for 42 million people in low-income blocks.

Myanmar MM: Population in Largest City: as % of Urban Population data was reported at 31.226 % in 2017. This records an increase from the previous number of 31.123 % for 2016. Myanmar MM: Population in Largest City: as % of Urban Population data is updated yearly, averaging 29.823 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 39.457 % in 1960 and a record low of 28.402 % in 1991. Myanmar MM: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Myanmar – Table MM.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

The Urban Atlas is providing pan-European comparable land use and land cover data for Large Urban Zones with more than 100.000 inhabitants as defined by the Urban Audit. Urban Atlas' mission is to provide high-resolution hotspot mapping of changes in urban spaces and indicators for users such as city governments, the European Environment Agency (EEA) and European Commission departments.

France FR: Population in Largest City: as % of Urban Population data was reported at 20.512 % in 2017. This records an increase from the previous number of 20.490 % for 2016. France FR: Population in Largest City: as % of Urban Population data is updated yearly, averaging 21.394 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 25.582 % in 1960 and a record low of 20.472 % in 2014. France FR: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;