Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Kazakhstan data available from WorldPop here.

Population density (people per sq. km of land area) in Kazakhstan was reported at 7.421 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kazakhstan - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Kazakhstan KZ: Population Density: People per Square Km data was reported at 6.681 Person/sq km in 2017. This records an increase from the previous number of 6.591 Person/sq km for 2016. Kazakhstan KZ: Population Density: People per Square Km data is updated yearly, averaging 5.567 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 6.681 Person/sq km in 2017 and a record low of 3.752 Person/sq km in 1961. Kazakhstan KZ: Population Density: People per Square Km 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 density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted Average;

Population density of Kazakhstan went up by 1.47% from 7.3 people per sq. km in 2021 to 7.4 people per sq. km in 2022. Since the 1.41% improve in 2012, population density rocketed by 15.77% in 2022. Population density is midyear population divided by land area in square kilometers.

Kazakhstan KZ: Population Density: Inhabitants per sq km data was reported at 7.420 Person in 2022. This records an increase from the previous number of 7.310 Person for 2021. Kazakhstan KZ: Population Density: Inhabitants per sq km data is updated yearly, averaging 6.240 Person from Dec 1992 (Median) to 2022, with 31 observations. The data reached an all-time high of 7.420 Person in 2022 and a record low of 5.730 Person in 1999. Kazakhstan KZ: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Kazakhstan – Table KZ.OECD.GGI: Social: Demography: Non OECD Member: Annual.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

Comprehensive socio-economic dataset for Kazakhstan including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

This dataset describes the relationship between the population density of Kazakhstan and the current infection with COVID-19, as well as analyze the quarantine situation in Kazakhstan with the spread of the epidemic in the regions, in addition, our data might serve as a reference source for further predict the future spread of the coronavirus in the country.

Kazakhstan. name, long name, population (source), population, constitutional form, drives on, head of state authority, Main continent, number of airports, Airports - with paved runways, Airports - with unpaved runways, Area, Birth rate, calling code, Children under the age of 5 years underweight, Current Account Balance, Death rate, Debt - external, Economic aid donor, Electricity consumption, Electricity consumption per capita, Electricity exports, Electricity imports, Electricity production, Exports, GDP - per capita (PPP), GDP (purchasing power parity), GDP real growth rate, Gross national income, Human Development Index, Health expenditures, Heliports, HIV AIDS adult prevalence rate, HIV AIDS deaths, HIV AIDS people living with HIV AIDS, Hospital bed density, capital city, Currency, Imports, Industrial production growth rate, Infant mortality rate, Inflation rate consumer prices, Internet hosts, internet tld, Internet users, Investment (gross fixed), iso 3166 code, ISO CODE, Labor force, Life expectancy at birth, Literacy, Manpower available for military service, Manpower fit for military service, Manpower reaching militarily age annually, is democracy, Market value of publicly traded shares, Maternal mortality rate, Merchant marine, Military expenditures percent of GDP, Natural gas consumption, Natural gas consumption per capita, Natural gas exports, Natural gas imports, Natural gas production, Natural gas proved reserves, Net migration rate, Obesity adult prevalence rate, Oil consumption, Oil consumption per capita, Oil exports, Oil imports, Oil production, Oil proved reserves, Physicians density, Population below poverty line, Population census, Population density, Population estimate, Population growth rate, Public debt, Railways, Reserves of foreign exchange and gold, Roadways, Stock of direct foreign investment abroad, Stock of direct foreign investment at home, Telephones main lines in use, Telephones main lines in use per capita, Telephones mobile cellular, Telephones mobile cellular per capita, Total fertility rate, Unemployment rate, Unemployment, youth ages 15-24, Waterways, valley, helicopter, canyon, artillery, crater, religion, continent, border, Plateau, marsh, Demonym

The share of urban population in Kazakhstan saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 58.18 percent. Still, the share reached its highest value in the observed period in 2023. A population may be defined as urban depending on the size (population or area) or population density of the village, town, or city. The urbanization rate then refers to the share of the total population who live in an urban setting. International comparisons may be inconsistent due to differing parameters for what constitutes an urban center.Find more key insights for the share of urban population in countries like Uzbekistan and Kyrgyz Republic.

This bar chart displays population (people) by demonym using the aggregation sum in Kazakhstan. The data is filtered where the date is 2023. The data is about countries per year.

We developed gridded livestock (horses and small ruminants, i.e., sheep & goats) density maps at high-resolution (1 km) for Kazakhstan during 2000-2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables. The developed livestock maps can be used in spatially explicit research, such as quantifying grass-livestock balance, water consumption by animal husbandry, methane emissions, risk of zoonotic diseases, and other environmental impact assessments.

The repository has three folders with small ruminants, horses, and sample code and data in separate folders. Each file is saved in GeoTiff format with 1-km spatial resolution and an Albers equal-area conic projection. Each file is saved with an acronym of ‘sr’ for small ruminants (Sheep & goat) and ‘hr’ for horses, followed by an underscore and a year. Missing data are represented by “No data.”

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

This bar chart displays population (people) by date using the aggregation sum in Kazakhstan. The data is filtered where the date is 2021. The data is about countries per year.

Key figures on the population of the Netherlands.

The following information is available: - Population by sex; - Population by marital status; - Population by age (groups); - Population by origin; - Private households; - Persons in institutional households; - Population growth; - Population density.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from: 1950 Figures on population by origin are only available from 2022 at this moment. The periods 1996 through 2021 will be added to the table at a later time.

Status of the figures: All the figures are final.

Changes as of 17 July 2024: Final figures with regard to population growth for 2023 and final figures of the population on 1 January 2024 have been added.

Changes as of 26 April 2023: None, this is a new table. This table succeeds the table Population; key figures; 1950-2022. See section 3. The following changes have been implemented compared to the discontinued table: - The topic folder 'Population by migration background' has been replaced by 'Population by origin'; - The underlying topic folders regarding 'first and second generation migration background' have been replaced by 'Born in the Netherlands' and 'Born abroad'; - The origin countries Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan and Turkey have been assigned to the continent of Asia (previously Europe).

When will new figures be published? In the last quarter of 2025 final figures with regard to population growth for 2024 and final figures of the population on 1 January 2025 will be added.

The Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The Human Footprint Index expresses as a percentage the relative human Influence in every biome on the land’s surface. HFP value ranges from 1 to 100. For instance similar areas. Human footprint is based on the premise that the impact of human influence varies by biogeography. A score of 1 in moist tropical forests in Africa indicates that that grid cell is part of the 1% least influenced or “wildest” area in its biome, the same as a score of 1 in North American broadleaf forest (although the absolute amount of influence in those two places may be quite different). The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN) and is available in the Interrupted Goode Homolosine Projection (IGHP) system.

Kazakhstan’s economy depends heavily on extractives, with many other industries linked to them. Perhaps the most telling indicator of a stalled structural transformation is the stagnant spatial organization of the economy spread on such a huge territory. One of the most durable findings in economic development is a tight three-way relationship between a country’s per capita income, the sectoral composition of its output, and the share of urban settlements in its population. It appears that during the past decade and a half, Kazakhstan’s spatial transformation has stalled. Kazakhstan suffers from having the bulk of its population dispersed in distant locations. On average, population density is six people per square kilometer across this large country, making it challenging to provide basic services and infrastructure. Low densities also stymie economies of agglomeration and specialization. Mobility toward urban areas is dampened by high housing costs, a severely limited rental market, and the residency registration system. In the highest demand urban areas, housing is extremely unaffordable when compared with local incomes. Finally, the residency registration system deters many people, particularly low-income people receiving financial support from the state-from relocating within the country, as benefits are tied to where a person is registered. For those who are unregistered, the system can lead to exclusion from many social services and benefits.

7,4 (personas por km2 de superficie de tierra) in 2022. Population density is midyear population divided by land area in square kilometers.

This bar chart displays male population (people) by ISO 2 country code using the aggregation sum in Kazakhstan. The data is about countries per year.

BackgroundWhile child morbidity in Kazakhstan is studied, existing research often prioritizes mortality or infectious diseases over non-communicable conditions. This study fills this gap by examining socioeconomic, demographic, and healthcare factors linked to respiratory diseases, asthma, and nervous system disorders among children aged 0–14 years across Kazakhstan from 2010 to 2019 highlighting regional context.MethodsPanel data from 14 regions were analyzed using linear mixed models with autoregressive covariance to address regional and temporal heterogeneity. Log-transformed incidence rates of respiratory diseases (J00-J99), asthma (J45), and nervous system diseases (G00-G99) were modeled against predictors including GRP per capita, unemployment, population density, Gini coefficient, pediatrician density, and hospital resources. Other variables with variance inflation factors ≥5 were excluded to mitigate multicollinearity.ResultsRespiratory diseases showed the highest mean incidence (57,329.86 per 100,000), with significant regional variation. Aqtöbe, Atyrau, and South Kazakhstan had 12–25% lower incidence compared to Zhambyl (reference), while Pavlodar and North Kazakhstan had 35–61% higher rates. A 1% increase in population density correlated with a 1.05% decrease in respiratory disease incidence (p = 0.008), whereas unemployment was linked to a 0.41% rise (p = 0.029). Asthma incidence increased by 140% over the decade, with higher rates in regions with greater income inequality (0.26% increase per 1% rise in low-income households, p = 0.032). Nervous system disorders showed limited associations, with unemployment as the sole predictor (0.69% increase per 1% rise, p = 0.040). Temporal trends revealed declines in most diseases, but neoplasms, diabetes, and asthma increased significantly.ConclusionThe study addresses the lack of localized socioeconomic and healthcare analyses for respiratory diseases, asthma, and nervous system disorders among children, providing evidence for region-specific policy interventions. Respiratory diseases and asthma among Kazakhstani children 0–14 years had associations with the regional economic conditions, healthcare utilization, and inequality. Population density and income inequality were consistent predictors, while nervous system disorders showed fewer clear associations. Our findings show distinct regional patterns in pediatric morbidity, linking health outcomes to localized socioeconomic and healthcare conditions.