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.

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;

Historical dataset showing Kazakhstan population density by year from 1992 to 2022.

Actual value and historical data chart for Kazakhstan Population Density People Per Sq Km

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.

View yearly updates and historical trends for Kazakhstan Population Density. Source: World Bank. Track economic data with YCharts analytics.

Population density of Kazakhstan went up by 1.47% from 7.4 people per sq. km in 2022 to 7.5 people per sq. km in 2023. Since the 1.45% improve in 2013, population density rocketed by 15.80% in 2023. Population density is midyear population divided by land area in square kilometers.

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.

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.”

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.

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.

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.

KZ：人口密度：每平方公里人口在12-01-2017达6.681Person/sq km，相较于12-01-2016的6.591Person/sq km有所增长。KZ：人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2017期间平均值为5.567Person/sq km，共57份观测结果。该数据的历史最高值出现于12-01-2017，达6.681Person/sq km，而历史最低值则出现于12-01-1961，为3.752Person/sq km。CEIC提供的KZ：人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的哈萨克斯坦 – 表 KZ.世界银行：人口和城市化进程统计。

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.

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

IntroductionDespite its endemic status and socioeconomic impacts, the spatial-temporal variation in rabies risk and its underlying determinants in Kazakhstan animal populations remain poorly understood. This study aimed to characterize the time-space dynamics of rabies in animal populations across Kazakhstan regions from 2013 to 2023 and identify the key drivers of transmission.MethodsUsing a Bayesian hierarchical regression model with spatial and temporal random effects, we analyzed national surveillance data on rabies cases in livestock, companion animals, and wildlife, alongside sociodemographic and animal population variables.ResultsThe model revealed that higher median income (odds ratio [OR]: 1.18, 95% posterior predictive interval [PPI]: 1.06–1.31), the presence of rabies in wildlife (OR: 1.55, 95% PPI: 1.27–1.89), and companion animal rabies incidence (low: 1–5 cases/year, OR: 1.39, 95% PPI: 1.06–1.85; high: ≥6 cases/year, OR: 2.07, 95% PPI: 1.46–2.96) were associated with increased livestock rabies risk, while higher human population density correlated with reduced risk (OR: 0.68, 95% PPI: 0.5–0.9). Spatial analysis identified persistent high-risk zones in western Kazakhstan and lower risk in southern regions, driven by ecological and socioeconomic heterogeneity.DiscussionThese findings highlight the relationship between wildlife reservoirs, domestic animal management, and socioeconomic factors in rabies transmission in Kazakhstan. By integrating these insights into national policy, Kazakhstan can advance toward the global target of eliminating dog-mediated human rabies deaths by 2030, serving as a model for Central Asia.

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.

The steppe marmot inhabits a wide range of open dry grasslands in Eurasia. Throughout this vast area, marmot habitats have undergone major changes due to human activities. Long-term ecological monitoring was conducted in the European steppe marmot settlements (Marmota bobak bobak) in Northeastern Ukraine in 2001–2019. The data obtained were compared with the observations made in M. b. schaganensis settlements in Kazakhstan during the expedition in 2017. The goals of our investigation were (1) to estimate M. bobak ecological plasticity based on general vegetation parameters of its habitats and settlement structure, (2) to relate the population density of the European subspecies to the food base of its habitats, (3) to evaluate the population response of M. b. bobak to the abandonment of cattle grazing, (4) to ascertain new ecological adaptations (if any) to the habitat changes, and (5) to reveal the steppe marmot’s status in the plant–herbivore interaction system in the grasslands of Northeastern Ukraine and Northern Kazakhstan. We have found differences in ecological features of M. b. bobak and M. b. schaganensis. The European subspecies was and continues to be a secondary pasture user. The Kazakhstan subspecies can be both secondary and primary users of the Asian dry steppes. Our studies have shown that the habitats of the European steppe marmot worsened dramatically (due to increased herbage height and cover of uneaten plant species together with litter) in comparison with those of the Kazakhstan subspecies. Presence in diverse habitats with a range of vegetation parameters as well as the differences between the settlement structures of M. b. bobak and M. b. schaganensis demonstrate the high ecological plasticity of the steppe marmot at the species level. At the same time, we have not found any new ecological adaptations that would ensure the survival of M. b. bobak settlements in modern conditions of the total cessation of cattle grazing.

Kazakhstan had the youngest population among the Commonwealth of Independent States (CIS) countries, with over 31 percent of the country's population being under 15 years of age and around a percent being 65 years or older. In Ukraine, on the other hand, a higher share of the population was between 14 to 65 years of age than the population under 15 years of age.