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 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.

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).

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

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">

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.

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 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.

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.

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

This layer contains information for locating past and present legal city boundaries within Los Angeles County. The Los Angeles County Department of Public Works provides the most current shapefiles representing city annexations and city boundaries on the Los Angeles County GIS Data Portal. The Department also provides large format city annexation maps (pdf) on its FTP site. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California. Numerous records are freely available at the Land Records Information website, hosted by the Department of Public Works.Principal attributes include:NO: corresponds with numbers on the tables displayed on City Annexation Maps.ANNEX_No: is a text version of the "NO" field listed above. Because this field is only used for the Long Beach and Los Angeles Annexation Maps, this value is null for all other cities.NAME: is the official name under which the annexation was filed.TYPE: is used to indicate which legal action occurred.A - represents an Annexation to that city.D - represents a Detachment from that city.V - is used to indicate the annexation was rendered Void or withdrawn before an effective date could be declared.33 - Some older city annexation maps indicate a city boundary declared 'as of February 8, 1933'.ANNEX_AREA: is the land area annexed or detached, in square miles, per the recorded legal description.TOTAL_AREA:is the cumulative total land area for each city, arranged chronologically.SHADE: is used by some of our cartographers to store the color used on printed maps.INDEXNO: is a matching field used for retriving documents from our department's document management system.STATE (Secretary of State): Date filed with the Secretary of State.COUNTY (County Recorder): Date filed with the County Recorder.EFFECTIVE (Effective Date):The effective date of the annexation or detachment.CITY: The city to which the annexation or detachment took place.FEAT_TYPE: contains the type of feature each polygon represents:Land - Use this value for your definition query if you want to see only land features on your map.Water - Polygons with this attribute value represent internal navigable waters. Examples of internal waters are found in the Long Beach Harbor.3NM Buffer - Per the Submerged Lands Act, the seaward boundaries of coastal cities and unincorporated county areas are three nautical miles (a nautical mile is 1852 meters) from the coastline.

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

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.

A geographic depiction of city boundaries in Broward County, Florida.

City boundary data was last updated April 13th, 2021 and previously on February 22, 2021. A small edit was made between Tamarac and Fort Lauderdale just SW of the Executive Airport. In February adjustments were made between Pembroke Pines, Southwest Ranches and Cooper City where their geographies are coincidence and are meant to follow the northern boundaries of STR geography. Prior to this edit, the City of Coral Springs had annexed four parcels of land from unincorporated Broward County; Ordinances 2018-014 (1 parcel) and 2018-036 (3 parcels), effective Sept 15, 2019. Previously in May 2019, a correction was made to the boundaries of Southwest Ranches and Pembroke Pines at Dykes Road and Sheraton, just north of Sheraton, on the west side of Dykes. Prior to this change, a correction was made to the Lauderhill boundary at the Florida Turnpike interchange located at the Sunrise Blvd entrance on the east side of the turnpike in April 2019; the 1959 Lauderhill incorporation legal description, (Laws of Florida 59-1478) left this thirteen acre area as unincorporated. A 1994 boundary change between Plantation and Lauderhill, (Laws of Florida 94-427) de-annexed five parcels from Plantation and annexed them to Lauderhill in this area. However in 1996, Broward County's Strategic Planning and Growth Management Department made available data sets provided by Broward County’s Planning and Information Technology Division via a CD. This data set depicted this unincorporated area as being part of Lauderhill. This depiction remained such until a boundary adjustment in 2006-2007 incorrectly depicted this as being part of Plantation. In 2009 Broward County was made aware of this error and adjusted it partially using the CD boundary as a template. This resulted in the area being incorrectly assigned to Lauderhill. In September of 2018, Lauderhill revisited this boundary depiction by the County and in 2019 it was concluded this area is unincorporated following the 1959 and the 1994 boundary adjustment legal descriptions.

Prior to April 2019 there were other edits. The previous update of the data was Nov 7th, 2018, adusting the boundaries between Weston and Town of Davie to agree with House Bill 0871 which redefined a small area of their adjoining boundaries in the area of Weston Road and I-75. In July 2018, adjustments were made to the City of Margate to align with a city boundary shape file and written legal description as provided by John Shelton, GIS, City of Margate. The previous update was January 17th, 2018, correcting an unincorporated boundary line of the Triple H Ranch plat area within Parkland. This also reflects an adjustment made to Pembroke Pines southwest boundary between the Turnpike and SR 27 and the Sept 15th 2016 annexations of County unincorporated lands by Parkland. (City Ord 2016-06) and Coconut Creek (City Ord. 2015-027).Also a correction to the Hollywood/Davie boundary in the vicinity of Davie Blvd Ext and N 66 Ave and Oak St, per the City of Hollywood. Recent past boundary changes include annexations of county land to Pembroke Pines and Cooper City in 2015. And a Weston-Davie boundary adjustment in 2015; HB 871. And a July 2015 official resurvey of the City of Fort Lauderdale's boundaries which thus included adjustments to Oakland Park and Pompano Beach boundaries, (F. Gulliano, BC Engineering, M. Donaldson PSM, Fort Lauderdale). Also in 2015, a boundary adjustment was made to the eastern most boundary of Pompano Beach to match it to a more accurate depiction of the coastal erosion line by Broward County; (requested by the city to match their legal description). Further back, the were annexations for Parkland (2013) and Sunrise (Nov 2012) and updates to Lauderdale Lakes (per J. Petrov - BC Engineering 2012) and Plantation (I Reyes, GIS - Plantation 2012).

Source: BCGIS

Effective Date:

Last Update: 04/15/2021

Update Cycle: As needed.

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. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

The catchment area of a city is a group of municipalities, of a single enclave and enclave, which defines the extent of the influence of a cluster of population and employment on the surrounding municipalities, this influence being measured by the intensity of commuting to work. Urban area zoning follows the zoning into urban areas in 2010. An area consists of a pole and a crown. — Poles are determined mainly on the basis of density and total population criteria, using a methodology consistent with that of the municipal density grid. A threshold of jobs is added in order to prevent essentially residential municipalities with few jobs from being considered poles. Within the pole, the most populous commune is called the center commune. If a pole sends at least 15 % of its assets to work in another pole of the same level, the two poles are associated and together form the heart of a catchment area. — Municipalities that send at least 15 % of their assets to work in the pole are the crown of the area. The definition of the largest catchment areas of cities is consistent with the definition of “cities” and “functional urban areas” used by Eurostat and the OECD to analyse the functioning of cities. Zoning into catchment areas thus facilitates international comparisons and makes it possible to visualise the influence in France of major foreign cities. For example, seven areas have a town located abroad (Bâle, Charleroi, Geneva, Lausanne, Luxembourg, Monaco and Saarbrücken). The areas are classified according to the total number of inhabitants of the area in 2017. The main thresholds selected are: Paris, 700,000 inhabitants, 200,000 inhabitants and 50,000 inhabitants. Areas whose pole is located abroad are classified in the category corresponding to their total population (French and foreign). Urban catchment areas, dated 2020, were constructed with reference to commuting known in the 2016 Census. Downloadable files provide the characteristics of the city’s catchment areas (size slice, number of municipalities) and the municipal composition of the city’s catchment areas.

This is a polygon data set depicting the current comprehensive planning areas for Boise City. A comprehensive planning area is defined in Chapter 4-1, page 171, of Blueprint Boise, the current Boise City Comprehensive Plan as, "On the largest scale, the entire area (area of impact) for which the City has authority to prepare comprehensive plans. On a smaller scale, planning area refers to the various sub-areas (i.e. West Bench, Central Bench, Southwest, etc,) which the City has defined as making up the larger planning area. These sub-areas are defined by physical barriers and/or the character of existing developments within them, and may each have specific planning objectives and policies articulated in the Comprehensive Plan." Each polygon in this data set is a specific smaller scale (sub-area) planning area. Collectively, the polygons represent the geography for the Boise City large scale comprehensive planning area.This data set is a critical component of the official Land Use Map within the Boise City Comprehensive Plan. It is used to identify specific areas within Boise City and the Boise Area of Impact to which specific land use designations and policies are applied. The data set is used to assist Boise City staff to identify specific planning areas and manage the growth of those areas to be consistent with the policies and intentions set out in the Boise City Comprehensive Plan.The dataset is generally coincident with the Boise Area of Impact; and is updated through City Council approval when the Boise Area of Impact changes. The data is current to the date the data set was published.For more information, please visit City of Boise Planning & Development.

Actual value and historical data chart for South Africa Population In Largest City