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All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name
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This database represents the historic, current and future estimates and projections with number of inhabitants for the world's largest urban areas from 1950-2050. The data covers cities and other urban areas with more than 750,000 people.
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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;
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Laos LA: Population in Largest City data was reported at 657,108.000 Person in 2017. This records an increase from the previous number of 649,550.000 Person for 2016. Laos LA: Population in Largest City data is updated yearly, averaging 268,948.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 657,108.000 Person in 2017 and a record low of 75,908.000 Person in 1960. Laos LA: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;
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This world cities layer presents the locations of many cities of the world, both major cities and many provincial capitals.Population estimates are provided for those cities listed in open source data from the United Nations and US Census.
https://www.wisconsin-demographics.com/terms_and_conditionshttps://www.wisconsin-demographics.com/terms_and_conditions
A dataset listing Wisconsin cities by population for 2024.
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Mexico MX: Population in Largest City data was reported at 21,500,251.000 Person in 2017. This records an increase from the previous number of 21,419,976.000 Person for 2016. Mexico MX: Population in Largest City data is updated yearly, averaging 15,225,498.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 21,500,251.000 Person in 2017 and a record low of 5,479,184.000 Person in 1960. Mexico MX: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;
The "Major Cities" layer is derived from the "World Cities" dataset provided by ArcGIS Data and Maps group as part of the global data layers made available for public use. "Major cities" layer specifically contains National and Provincial capitals that have the highest population within their respective country. Cities were filtered based on the STATUS (“National capital”, “National and provincial capital”, “Provincial capital”, “National capital and provincial capital enclave”, and “Other”). Majority of these cities within larger countries have been filtered at the highest levels of POP_CLASS (“5,000,000 and greater” and “1,000,000 to 4,999,999”). However, China for example, was filtered with cities over 11 million people due to many highly populated cities. Population approximations are sourced from US Census and UN Data. Credits: ESRI, CIA World Factbook, GMI, NIMA, UN Data, UN Habitat, US Census Bureau Disclaimer: The designations employed and the presentation of material at this site do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
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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.
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The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5
If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD
The following text is a summary of the information in the above Data Descriptor.
The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.
The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.
These maps represent a unique global representation of physical access to essential services offered by cities and ports.
The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).
travel_time_to_ports_x (x ranges from 1 to 5)
The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.
Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes
Data type Byte (16 bit Unsigned Integer)
No data value 65535
Flags None
Spatial resolution 30 arc seconds
Spatial extent
Upper left -180, 85
Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)
Temporal resolution 2015
Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.
Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.
The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.
Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points
The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).
Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.
Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.
This process and results are included in the validation zip file.
Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.
The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.
The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.
The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.
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Poland PL: Population in Largest City data was reported at 1,758,551.000 Person in 2017. This records an increase from the previous number of 1,749,352.000 Person for 2016. Poland PL: Population in Largest City data is updated yearly, averaging 1,618,666.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,758,551.000 Person in 2017 and a record low of 1,119,181.000 Person in 1960. Poland PL: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;
As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.
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Kazakhstan KZ: Population in Largest City data was reported at 1,547,257.000 Person in 2017. This records an increase from the previous number of 1,534,894.000 Person for 2016. Kazakhstan KZ: Population in Largest City data is updated yearly, averaging 1,069,962.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,547,257.000 Person in 2017 and a record low of 528,653.000 Person in 1960. Kazakhstan KZ: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kazakhstan – Table KZ.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;
This study focused on the effect of economic resources and racial/ethnic composition on the change in crime rates from 1970-2004 in United States cities in metropolitan areas that experienced a large growth in population after World War II. A total of 352 cities in the following United States metropolitan areas were selected for this study: Atlanta, Dallas, Denver, Houston, Las Vegas, Miami, Orange County, Orlando, Phoenix, Riverside, San Bernardino, San Diego, Silicon Valley (Santa Clara), and Tampa/St. Petersburg. Selection was based on the fact that these areas developed during a similar time period and followed comparable development trajectories. In particular, these 14 areas, known as the "boomburbs" for their dramatic, post-World War II population growth, all faced issues relating to the rapid growth of tract-style housing and the subsequent development of low density, urban sprawls. The study combined place-level data obtained from the United States Census with crime data from the Uniform Crime Reports for five categories of Type I crimes: aggravated assaults, robberies, murders, burglaries, and motor vehicle thefts. The dataset contains a total of 247 variables pertaining to crime, economic resources, and race/ethnic composition.
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Central African Republic CF: Population in Largest City data was reported at 985,965.000 Person in 2024. This records an increase from the previous number of 958,335.000 Person for 2023. Central African Republic CF: Population in Largest City data is updated yearly, averaging 487,345.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 985,965.000 Person in 2024 and a record low of 94,350.000 Person in 1960. Central African Republic CF: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;
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Panama PA: Population in Largest City data was reported at 1,745,913.000 Person in 2017. This records an increase from the previous number of 1,709,128.000 Person for 2016. Panama PA: Population in Largest City data is updated yearly, averaging 808,056.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,745,913.000 Person in 2017 and a record low of 282,895.000 Person in 1960. Panama PA: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;
The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.
https://datafinder.stats.govt.nz/license/attribution-4-0-international/https://datafinder.stats.govt.nz/license/attribution-4-0-international/
Urban rural 2023 update
UR 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure UR geographies are relevant and meet criteria before each five-yearly population and dwelling census. UR 2023 contains 13 new rural settlements and 7 new small urban areas. Updates were made to reflect real world change including new subdivisions and motorways, and to improve delineation of urban areas and rural settlements. The Wānaka 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 urban rural indicator.
In the 2023 classification there are:
This dataset is the definitive version of the annually released urban rural (UR) boundaries as at 1 January 2023 as defined by Stats NZ. This version contains 745 UR areas, including 195 urban areas and 402 rural settlements.
Urban rural (UR) is an output geography that classifies New Zealand into areas that share common urban or rural characteristics and is used to disseminate a broad range of Stats NZ’s social, demographic and economic statistics.
The UR separately identifies urban areas, rural settlements, other rural areas, and water areas. Urban areas and rural settlements are form-based geographies delineated by the inspection of aerial imagery, local government land designations on district plan maps, address registers, property title data, and any other available information. However, because the underlying meshblock pattern is used to define the geographies, boundaries may not align exactly with local government land designations or what can be seen in aerial images. Other rural areas, and bodies of water represent areas not included within an urban area.
Urban areas are built from the statistical area 2 (SA2) geography, while rural and water areas are built from the statistical area 1 (SA1) geography.
Non-digitised
The following 4 non-digitised UR areas have been aggregated from the 16 non-digitised meshblocks/SA2s.
6901; Oceanic outside region, 6902; Oceanic oil rigs, 6903; Islands outside region, 6904; Ross Dependency outside region.
UR numbering and naming
Each urban area and rural settlement is a single geographic entity with a name and a numeric code.
Other rural areas, inland water areas, and inlets are defined by territorial authority; oceanic areas are defined by regional council; and each have a name and a numeric code.
Urban rural codes have four digits. North Island locations start with a 1, South Island codes start with a 2, oceanic codes start with a 6 and non-digitised codes start with 69.
Urban rural indicator (IUR)
The accompanying urban rural indicator (IUR) classifies the urban, rural, and water areas by type. Urban areas are further classified by the size of their estimated resident population:
This was based on 2018 Census data and 2021 population estimates. Their IUR status (urban area size/rural settlement) may change if the 2023 Census population count moves them up or down a category.
The indicators, by name, with their codes in brackets, are:
urban area – major urban (11), large urban (12), medium urban (13), small urban (14),
rural area – rural settlement (21), rural other (22),
water – inland water (31), inlet (32), oceanic (33).
The urban rural indicator complements the urban rural geography and is an attribute in this dataset. Further information on the urban rural indicator is available on the Stats NZ classification and coding tool ARIA.
For more information please refer to the Statistical standard for geographic areas 2023.
Generalised version
This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.
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
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Ecuador EC: Population in Largest City data was reported at 2,844,281.000 Person in 2017. This records an increase from the previous number of 2,790,791.000 Person for 2016. Ecuador EC: Population in Largest City data is updated yearly, averaging 1,502,366.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2,844,281.000 Person in 2017 and a record low of 456,225.000 Person in 1960. Ecuador EC: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ecuador – Table EC.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;
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All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name