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.

This horizontal bar chart displays urban land area (km²) by capital city using the aggregation sum in Western Africa. The data is about countries.

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.

CONTENT

This is a list of cities worldwide by population density. The population, population density and land area for the cities listed are based on the entire city proper, the defined boundary or border of a city or the city limits of the city. The population density of the cities listed is based on the average number of people living per square kilometer or per square mile. This list does not refer to the population, population density or land area of the greater metropolitan area or urban area, nor particular districts in any of the cities listed.

This horizontal bar chart displays urban land area (km²) by capital city using the aggregation sum in Europe. The data is about countries per year.

Hamamatsu was the largest major city in Japan based on city area in 2024, with a size of close to **** thousand square kilometers. It was followed by Shizuoka, with a size of more than **** square kilometers. Overconcentration in Tokyo Economic, political, and financial activity in Japan is heavily concentrated in Tokyo. With around **** million inhabitants, the metropolitan area of Tokyo is the largest urban conglomeration in the world. Most of Japan’s largest companies have their headquarters in Tokyo, and the region attracts many young people who move there to study or work. A breakdown of the net migration flow in Japan showed that the prefectures of Tokyo, Kanagawa, Saitama, and Chiba, all part of the Tokyo metropolitan area, attract the largest number of people. In contrast, the majority of prefectures, especially those located in rural parts of the country, lose a substantial part of their population every year. Demographic trend in rural regions The overconcentration of economic activity in Tokyo has an impact on the demographic situation in rural parts of the country. Japan’s population is shrinking and aging, and rural regions are particularly affected by this. Many young people leave their rural hometowns to seek better opportunities in urban parts of Japan, leaving behind an aging population. As a result, many rural communities in Japan struggle with depopulation and a notable share of municipalities are even threatened with disappearance in the coming decades.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/orbit-107.jpg" alt="Virtual City"> There are many different ways that engineers measure cities. What is the population? What is the total land area occupied by the city. In this exercise, we will see if you can create a model to predict the square feet contained in various virtual cities.

You are provided with images looking towards the city center. Each image is 512x512, compressed with JPEG. The following sample image shows a typical view of a virtual city. You can see towers of a variety of sizes and colors.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/2.jpg" alt="Typical City View">

Not all views of the city will be the same. The following view looks at a city much closer up; however, results in some cropping of important detail.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/10.jpg" alt="Up Close">

Some of the images will show you the city from somewhat above. Here you can see the city roads more clearly. https://data.heatonresearch.com/images/wustl/kaggle/city-size/321.jpg" alt="Above the City">

Some cities are very small.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/350.jpg" alt="Small City">

Other cities are larger.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/947.jpg" alt="Large City">

The largest city in Poland in terms of area was Gdańsk in 2024, with *** square kilometers. Followed by the capital, Warsaw, and Gdynia.

This horizontal bar chart displays land area (km²) by capital city using the aggregation sum in Caribbean. The data is about countries.

Not many studies have documented climate and air quality changes of settlements at early stages of development. This is because high quality climate and air quality records are deficient for the periods of the early 18th century to mid 20th century when many U.S. cities were formed and grew. Dramatic landscape change induces substantial local climate change during the incipient stage of development. Rapid growth along the urban fringe in Phoenix, coupled with a fine-grained climate monitoring system, provide a unique opportunity to study the climate impacts of urban development as it unfolds. Generally, heat islands form, particularly at night, in proportion to city population size and morphological characteristics. Drier air is produced by replacement of the countryside's moist landscapes with dry, hot urbanized surfaces. Wind is increased due to turbulence induced by the built-up urban fabric and its morphology; although, depending on spatial densities of buildings on the land, wind may also decrease. Air quality conditions are worsened due to increased city emissions and surface disturbances. Depending on the diversity of microclimates in pre-existing rural landscapes and the land-use mosaic in cities, the introduction of settlements over time and space can increase or decrease the variety of microclimates within and near urban regions. These differences in microclimatic conditions can influence variations in health, ecological, architectural, economic, energy and water resources, and quality-of-life conditions in the city. Therefore, studying microclimatic conditions which change in the urban fringe over time and space is at the core of urban ecological goals as part of LTER aims. In analyzing Phoenix and Baltimore long-term rural/urban weather and climate stations, Brazel et al. (In progress) have discovered that long-term (i.e., 100 years) temperature changes do not correlate with populations changes in a linear manner, but rather in a third-order nonlinear response fashion. This nonlinear temporal change is consistent with the theories in boundary layer climatology that describe and explain the leading edge transition and energy balance theory. This pattern of urban vs. rural temperature response has been demonstrated in relation to spatial range of city sizes (using population data) for 305 rural vs. urban climate stations in the U.S. Our recent work on the two urban LTER sites has shown that a similar climate response pattern also occurs over time for climate stations that were initially located in rural locations have been overrun bu the urban fringe and subsequent urbanization (e.g., stations in Baltimore, Mesa, Phoenix, and Tempe). Lack of substantial numbers of weather and climate stations in cities has previously precluded small-scale analyses of geographic variations of urban climate, and the links to land-use change processes. With the advent of automated weather and climate station networks, remote-sensing technology, land-use history, and the focus on urban ecology, researchers can now analyze local climate responses as a function of the details of land-use change. Therefore, the basic research question of this study is: How does urban climate change over time and space at the place of maximum disturbance on the urban fringe? Hypotheses 1. Based on the leading edge theory of boundary layer climate change, largest changes should occur during the period of peak development of the land when land is being rapidly transformed from open desert and agriculture to residential, commercial, and industrial uses. 2. One would expect to observe, on average and on a temporal basis (several years), nonlinear temperature and humidity alterations across the station network at varying levels of urban development. 3. Based on past research on urban climate, one would expect to see in areas of the urban fringe, rapid changes in temperature (increases at night particularly), humidity (decreases in areas from agriculture to urban; increases from desert to urban), and wind speed (increases due to urban heating). 4. Changes of the surface climate on the urban fringe are expected to be altered as a function of various energy, moisture, and momentum control parameters, such as albedo, surface moisture, aerodynamic surface roughness, and thermal admittance. These parameters relate directly to population and land-use change (Lougeay et al. 1996).

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

This statistic provides the share of the total land area that is built-up in the ** largest cities around the world in 2015. As of this year, about ***** percent of Los Angeles' land area was considered built-up.

This statistic provides the land area that is built-up and non-built up in the ** largest cities worldwide in 2015. As of this year, about ******** square meters of Los Angeles is still considered non-built-up land mass.

This dataset contains information on all United States of America counties.

I have scraped this data from the following Wikipedia website: https://en.wikipedia.org/wiki/List_of_United_States_counties_and_county_equivalents

Data scientists spend most of their time on data cleaning. Hence, this dataset can be ideal for sharpening your data-cleaning skills.

Columns specification: county: Name of each county. state: State name. founded: The year when it was founded. largest_city: Name of the largest city. pop_total: Population in total on that state. pop_den: Population density per square mile and km square. total_area: Total area(land + water) on mile square and km square. land_area: Total land area in mile square and km square. water_area: Total water area on mile square and km square.

This data layer produced by the National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program provides a geographic locale framework that classifies all U.S. territory into twelve categories ranging from Large Cities to Remote Rural areas. NCES uses this framework to describe the type of geographic area where schools and school districts are located. The criteria for these classifications are defined by NCES and rely on standard geographic areas developed and maintained by the U.S. Census Bureau. The NCES Locale boundaries are based on geographic areas represented in Census TIGER/Line. For more information about the NCES locale framework, and to download the data, see: https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries. The classifications include:City - Large (11): Territory inside an Urban Area with a population of 50,000 or more and inside a Principal City with population of 250,000 or more.City - Midsize (12): Territory inside an Urban Area with a population of 50,000 or more and inside a Principal City with population less than 250,000 and greater than or equal to 100,000.City - Small (13): Territory inside an Urban Area with a population of 50,000 or more and inside a Principal City with population less than 100,000.Suburb – Large (21): Territory outside a Principal City and inside an Urban Area with population of 250,000 or more.Suburb - Midsize (22): Territory outside a Principal City and inside an Urban Area with population less than 250,000 and greater than or equal to 100,000.Suburb - Small (23): Territory outside a Principal City and inside an Urban Area with population less than 100,000. Town - Fringe (31): Territory inside an Urban Area with a population less than 50,000 that is less than or equal to 10 miles from an Urban Area with a population of 50,000 or more.Town - Distant (32): Territory inside an Urban Area with a population less than 50,000 that is more than 10 miles and less than or equal to 35 miles from an Urban Area with a population of 50,000 or more.Town - Remote (33): Territory inside an Urban Area with a population less than 50,000 that is more than 35 miles of an Urban Area with a population of 50,000 or more.Rural - Fringe (41): Census-defined rural territory that is less than or equal to 5 miles from an Urban Area of 50,000 or more, as well as rural territory that is less than or equal to 2.5 miles from an Urban Area with a population less than 50,000.Rural - Distant (42): Census-defined rural territory that is more than 5 miles but less than or equal to 25 miles from an Urban Area with a population of 50,000 or more, as well as rural territory that is more than 2.5 miles but less than or equal to 10 miles from an Urban Area with a population less than 50,000.Rural - Remote (43): Census-defined rural territory that is more than 25 miles from an Urban Area with a population of 50,000 or more and is also more than 10 miles from an Urban Area with a population less than 50,000.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

This horizontal bar chart displays land area (km²) by capital city using the aggregation sum in Korea. The data is about countries per year.

Here we produced the first 10 m resolution urban green space (UGS) map for the main urban clusters across 371 major Latin American cities as of 2017. Our approach applied a supervised classification of Sentinel-2 satellite imagery and UGS samples derived from OpenStreetMap (OSM). The overall accuracy of this UGS map in 11 randomly selected cities was 0.87, evaluated by independently collected validation samples (‘ground truth’). We further improved mapping quality through a visual inspection and additional sample collection. The resulting UGS map enables studies to measure area, spatial configuration, and human exposures to UGS, facilitating studies about the relationship between UGS and human exposures to environmental hazards, public health outcomes, and environmental justice issues in Latin American cities.UGS in this map series includes grass, shrub, forest, and farmland, and non-UGS included buildings, pavement, roads, barren land, and dry vegetation.The UGS map series includes three sets of files:(1) binary UGS maps at 10 m spatial resolution in GEOTIFF format (UGS.zip), with each of the 371 cities being an individual map. Mapped value of 1 indicates UGS, 0 indicates non-UGS, and no data (with value of -32768) indicates areas outside the mapped boundary or water bodies;(2) a shapefile of mapped boundaries (Boundaries.zip). The boundary file contains city name, country name and its ISO-2 country code, and an ID field linking each city's boundary to the corresponding UGS map.(3) .prj files containing projection information for the binary UGS maps and boundary shapefile. The binary UGS maps are projected with World Geodetic System (WGS) 84 / Pseudo-Mercator projected coordinate system (EPSG: 3857), and the boundary shapefile is projected with WGS 1984 geographic coordinate system (EPSG: 4326)Reference: A 10 m resolution urban green space map for major Latin American cities from Sentinel-2 remote sensing images and OpenStreetMap, published by Scientific Data [link].Citation: Ju, Y., Dronova, I., & Delclòs-Alió, X. (2022). A 10 m resolution urban green space map for major Latin American cities from Sentinel-2 remote sensing images and OpenStreetMap. Scientific Data, 9, Article 1. https://doi.org/10.1038/s41597-022-01701-y

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates consists of country-level estimates of urban, rural and total population and land area country-wide and in the LECZ, if applicable. Additionally, the data set provides the number of urban extents, their population and land area that intersect the LECZ, by city-size population classifications of less than 100,000, 100,000 to 500,000, 500,000 to 1,000,000, 1,000,000 to 5,000,000, and more than 5,000,000. All estimates are based on GRUMP Alpha data products. The LECZ was generated using SRTM Digital Elevation Model data and includes all land area that is contiguous with the coast and 10 meters or less in elevation. All grids used for population, land area, urban mask, and LECZ were of 30 arc-second (~1 km ) resolution. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Institute for Environment and Development (IIED).

The World Bank's data for 2021 on total land area by country provides detailed information on the size of land in square kilometers for various countries worldwide. Here are some key highlights from the dataset:

Russia is the largest country by land area, with approximately 16.38 million square kilometers. Canada follows with around 9.98 million square kilometers. China has a land area of about 9.42 million square kilometers, making it the third largest. The United States (excluding territories) covers around 9.14 million square kilometers. Smaller countries and regions include:

Vatican City, with an area of about 0.44 square kilometers. Monaco, with 2 square kilometers

THIS DATA WAS LAST UPDATED IN 2024 and it is owned by https://data.worldbank.org/indicator/AG.LND.TOTL.K2?end=2021&start=2021&view=map

In 2024, the city in the United States with the highest share of parkland was Anchorage, Alaska, where approximately 84 percent of the city was parkland. In second place, with almost half the percentage of parkland was Fremont, California, where 43 percent of the city was parkland.