In 2022, the population density in Namibia increased by 0.1 inhabitants per square kilometer (+2.93 percent) compared to 2021. With 3.51 inhabitants per square kilometer, the population density thereby reached its highest value in the observed period. Notably, the population density continuously increased over the last years.Population density is calculated by dividing the total population by the total land area, to show the average number of people living there per square kilometer of land.

Population density (people per sq. km of land area) in Namibia was reported at 3.5099 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Namibia - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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. Namibia data available from WorldPop here.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Namibia: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Namibia NA: Population Density: People per Square Km data was reported at 3.078 Person/sq km in 2017. This records an increase from the previous number of 3.012 Person/sq km for 2016. Namibia NA: Population Density: People per Square Km data is updated yearly, averaging 1.652 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 3.078 Person/sq km in 2017 and a record low of 0.750 Person/sq km in 1961. Namibia NA: 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 Namibia – Table NA.World Bank.WDI: 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;

Namibia population density for 2022 was 3.51, a 2.81% increase from 2021.

<ul style='margin-top:20px;'>

<li>Namibia population density for 2021 was <strong>3.41</strong>, a <strong>3% increase</strong> from 2020.</li>
<li>Namibia population density for 2020 was <strong>3.31</strong>, a <strong>2.95% increase</strong> from 2019.</li>
<li>Namibia population density for 2019 was <strong>3.22</strong>, a <strong>2.84% increase</strong> from 2018.</li>
</ul>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.

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.

Namibia data available from WorldPop here.

Namibia NA: Population Density: Inhabitants per sq km data was reported at 3.510 Person in 2022. This records an increase from the previous number of 3.410 Person for 2021. Namibia NA: Population Density: Inhabitants per sq km data is updated yearly, averaging 2.420 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 3.510 Person in 2022 and a record low of 1.660 Person in 1990. Namibia NA: 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 Namibia – Table NA.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Date: 1991-07-01Date type: publicationDateURL: Not AvailableOriginator: Namibia Statistics AgencyResponsible party: Namibia Statistics AgencyResponsible party role: custodianAbstract:This layer displays enumeration areas for the year 1991. These are polygon features created from topographic maps.Purpose:To ensure complete and accurate geographical coverage of the country during enumeration. Mapping entails dividing the country into smaller unique geographic areas known as “Enumeration Area (EA)” to serve as small data collection units during enumeration.Status: completedMaintenance and update frequency: 5-10-yearsEntity/attribute description:GEO , POPSIZE_2 , EA_CODE_CS , EA_CODE_ME , EA_typeCompleteness:Enumeration Areas cover the whole country.Logical consistency:UnknownPositional accuracy:Digitized from topographic maps of 1: 50 000 scale.Temporal quality:Digitized from old topographic maps from the 1970s.Thematic accuracy:100% accuracyLineage:Demarcated based on the population and visible physical features on the ground.Process description:Digitized from topographic mapsSpatial reference information: EPSG:900999Metadata date: 2016-10-02Metadata date type: creationMetadata contact name: Namibia Statistics AgencyMetadata contact role code: pointOfContactMetadata contact person: Nevel Ngahahe-HangeroMetadata contact address postal code:P O Box 2133, WindhoekMetadata contact telephone: +264 61 431 3200Metadata standard name: ISO:19115 (NSA custom schema)Metadata standard version: 2014/NSA-1:2016 version

The world population data sourced from Facebook Data for Good is some of the most accurate population density data in the world. The data is accumulated using highly accurate technology to identify buildings from satellite imagery and can be viewed at up to 30-meter resolution. This building data is combined with publicly available census data to create the most accurate population estimates. This data is used by a wide range of nonprofit and humanitarian organizations, for example, to examine trends in urbanization and climate migration or discover the impact of a natural disaster on a region. This can help to inform aid distribution to reach communities most in need. There is both country and region-specific data available. The data also includes demographic estimates in addition to the population density information. This population data can be accessed via the Humanitarian Data Exchange website.

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. More information.

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Namibia. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Namibia. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Namibia. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

NA：人口密度：每平方公里人口在12-01-2017达3.078Person/sq km，相较于12-01-2016的3.012Person/sq km有所增长。NA：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1.652Person/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3.078Person/sq km，而历史最低值则出现于12-01-1961，为0.750Person/sq km。CEIC提供的NA：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，数据归类于全球数据库的纳米比亚 – Table NA.World Bank.WDI：人口和城市化进程统计。

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Namibia. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

3,5 (personas por km2 de superficie de tierra) in 2022. Population density is midyear population divided by land area in square kilometers.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Namibia. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

Historical exploitation, and a combination of current anthropogenic impacts, such as climate change and habitat degradation, impact the population dynamics of marine mammalian megafauna. Right whales (Eubalaena spp.) are large cetaceans recovering from hunting, whose reproductive and population growth rate appear to be impacted by climate change. We apply noninvasive genetic methods to monitor southern right whale (E. australis, SRW) and test the application of noninvasive genetics to minimise the observer effects on the population. Our aim is to describe population structure, and interdecadal and interannual changes to assess species status in the Great Acceleration period of Anthropocene. As a basis for population genetic analyses, we collected samples from sloughed skin during post-migration epidermal moult. Considering the exploration-exploitation dilemma, we collaborated with whale-watching companies, as part of a citizen science approach and to reduce ad hoc logistic operations and biopsy equipment. We used mitochondrial and microsatellite data and population genetic tools. We report for the first time the genetic composition and differentiation of the Namibian portion of the range. Population genetic parameters suggest that South Africa hosts the largest population. This corresponds with higher estimates of current gene flow from Africa compared to older samples. We have observed considerable interannual variation in population density at the breeding ground and an interdecadal shift in genetic variability, evidenced by an increase in the point estimate inbreeding. Clustering analyses confirmed differentiation between the Atlantic and Indo-Pacific, presumably originating during the ice ages. We show that population monitoring of large whales, essential for their conservation management, is feasible using noninvasive sampling within non-scientific platforms. Observed patterns are concurrent to changes of movement ecology and decline in reproductive success of the South African population, probably reflecting a large-scale restructuring of pelagic marine food webs. Methods The majority of samples used in this study were obtained noninvasively by collecting sloughed skin from whale watching boats conducting commercial trips during the austral winters of 2016 – 2018 in the area of Gansbaai and Walker Bay, South Africa. Pieces of skin were spotted in the water, picked up by a dip net and transferred with sterile tweezers to a tube containing 96% ethanol. Additional samples were collected from a research boat by remote biopsy using a crossbow and Cetadart darts (Lambertsen, 1987). All samples were stored at − 18 °C. Another 32 biopsy samples were available in archive held by University of Pretoria Mammal Research Institute Whale Unit. These samples were collected in two different regions, South Africa and Namibia, between 2003 and 2013. Tissue was pulverised in liquid nitrogen and DNA was extracted using either the QIAGEN DNeasy Blood & Tissue Kit or the GENEAID Genomic DNA Mini Kit. Seventeen microsatellite loci were grouped into multiplexes and amplified in 10 μl PCR reactions (Carroll et al., 2015). Multi-locus microsatellite genotyping was done according to sample type. For noninvasive samples, a multi-tube approach (Taberlet et al., 1996) was attempted, with each DNA extraction being amplified at all loci up to three times. For biopsy samples, all loci were amplified once. Sex was determined by amplification of the male specific SRY gene, multiplexed with an amplification of the ZFY/ZRX region as a positive control (Aasen and Medrano, 1990, Gilson et al., 1998). An approximately 550 base pair fragment of the left hypervariable domain of mtDNA control region adjacent to the Pro-tRNA gene was amplified according to Baker et al. (1999). The resulting PCR product was purified by either QIAGEN QIAquick PCR Purification Kit or GENEAID GenepHlow PCR Cleanup Kit and sequenced using BigDye chemistry on a 3130 Genetic Analyzer (Applied Biosystems). Chromatograms were visualized and edited in Geneious Prime v2020.2.4 (© Biomatters Ltd.). For the microsatellite data, quality filtering, allele calling and binning was performed in the program Genemapper 5 (Applied Biosystems). For samples where loci were run more than once, the final genotype was constructed by choosing the highest quality allele calls for each locus, as determined by the quality score in Genemapper. Any samples where the allele calls disagreed between runs were removed from the dataset. Genotypes that failed to amplify for seven or more loci were considered poor quality and were removed from the dataset. Genotype error rates were calculated per allele (Pompanon et al., 2005) using the internal control samples amplified in every PCR and replicate samples. We report the completeness of the final dataset in terms of number of loci per sample. First, genotypes within a year were reconciled to identify the number of unique whales sampled per austral winter season. Then, unique genotypes across years were compared to understand between year recaptures and the total number of whales sampled over the survey period. Cervus 3.0.7 was used to identify these within and between years genotype matches (Kalinowski et al., 2007) with the minimum number of matching loci set to at least eight. Pairs of genotypes that matched at eight loci but mismatched at up to three loci were reviewed and repeated if necessary to verify the individual’s identity or difference (Constantine et al., 2012).

ナミビアの人口密度を国土面積と総人口から算出し最新の推移グラフや日本との比較表、世界人口密度ランキング(狭い)等を用い、人口密度が低いのか高いのかを説明しています。各種データはcsv出力・ダウンロードも可能です。（EXCELでも使用可能）元データのソースはworldbank.orgで、当サイト(GraphToChart)が独自に計算・算出し全て無料で利用可能ですので、研究や分析レポートにお役立て頂ければ幸いです。