Chart and table of Namibia population density from 1950 to 2025. United Nations projections are also included through the year 2100.

The population density in Namibia increased by 0.1 inhabitants per square kilometer (+2.93 percent) compared to the previous year. 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 refers to the average number of residents per square kilometer of land across a given country or region. It is calculated by dividing the total midyear population by the total land area.

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).

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

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

Namibia population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

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.

Namibia administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata.
Global version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population

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。

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

人口密度：每平方公里的居民在12-01-2022达3.510人，相较于12-01-2021的3.410人有所增长。人口密度：每平方公里的居民数据按年更新，12-01-1990至12-01-2022期间平均值为2.420人，共33份观测结果。该数据的历史最高值出现于12-01-2022，达3.510人，而历史最低值则出现于12-01-1990，为1.660人。CEIC提供的人口密度：每平方公里的居民数据处于定期更新的状态，数据来源于Organisation for Economic Co-operation and Development，数据归类于全球数据库的纳米比亚 – Table NA.OECD.GGI: Social: Demography: Non OECD Member: Annual。

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).