Population density is a measurement of population per unit area or unit volume. It is frequently applied to living organisms, and particularly to humans. It is a key geographic term. (Wikipedia)

From the AfriPop website..."High resolution, contemporary data on human population distributions are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. The AfriPop project was initiated in July 2009 with an aim of producing detailed and freely-available population distribution maps for the whole of Africa. Based on the approaches outlined in detail here and here, and summarized on the methods page, fine resolution satellite imagery-derived settlement maps are combined with land cover maps to reallocate contemporary census-based spatial population count data. Assessments have shown that the resultant maps are more accurate than existing population map products, as well as the simple gridding of census data. Moreover, the 100m spatial resolution represents a finer mapping detail than has ever before been produced at national extents. The approaches used in AfriPop dataset production are designed with operational application in mind, using simple and semi-automated methods to produce easily updatable maps. Given the speed with which population growth and urbanisation are occurring across much of Africa, and the impacts these are having on the economies, environments and health of nations, such features are a necessity for both research and operational applications."Data Source: AfriPop.org

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Ethiopia: (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). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Nigeria: (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).

Methodology

These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click [here](https://dataforgood.fb.com/docs/methodology-high-resolution-population-density-maps-demographic-estimates/

For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/

Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

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.

Ethiopia data available from WorldPop here.

These 28 tiff files represent 2015 population estimates. However, please note that many of the country-level files include 2020 population estimates including: Angola, Benin, Botswana, Burundi, Cameroon, Cabo Verde, Cote d'Ivoire, Djibouti, Eritrea, Eswatini, The Gambia, Ghana, Lesotho, Liberia, Mozambique, Namibia, Sao Tome & Principe, Sierra Leone, South Africa, Togo, Zambia, and Zimbabwe. South Sudan, Sudan, Somalia and Ethiopia are intentionally omitted from this dataset. However, a country-level dataset for Ethiopia can be found at https://data.humdata.org/dataset/ethiopia-high-resolution-population-density-maps-demographic-estimates.

DATASET: Alpha version 2010 estimates of numbers of live births per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/). REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated births per grid square MAPPING APPROACH: Land cover based with DHS-derived subnational age-specific fertility rates applied, as described on the website (general mapping approach described in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882) FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - cod10bth-adj.tif = Democratic Republic of Congo (COD) births count map for 2010 adjusted to match UN national estimates on numbers of live births. DATE OF PRODUCTION: July 2013

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 Ethiopia. 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.

The population prospects in the Nile Basin present two maps of human population distribution in the Nile countries: an estimate for 2005, and a projection for 2030. Landscan 2004 provided the geo-referenced base layer. It was combined with the medium demographic growth variant for 2030, obtained for each Nile country from UNDESA. The maps show the high population densities in the Lake Victoria region, the Nile delta and valley, the Ethiopian highlands, and around Khartoum. Outside the Nile valley, large areas of the Democratic Republic of the Congo, Eritrea, Kenya, the Sudan and the United Republic of Tanzania are sparsely populated. It also shown that the settlement pattern in the lower riparians follows the Nile, while in the upper riparians it tends to follow – broadly – rainfall distribution. Data Sources in details: - United Nations Population Division (UNDESA), 2030 medium variant; - US Bureau of Census, 2005; - LandScan 2004 Distributed Population layer; - AFRICOVER urban areas; - ESRI Gazetteer; - Urban polygons digitized by the project for DR Congo, Egypt and Ethiopia.

The raster dataset consists of a 1km score grid for coffee storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location:

• Supply: Coffee.

• Demand: Human population density, Major cities population (national and bordering countries).

• Infrastructure/accessibility: main transportation infrastructure

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1)

Data publication: 2021-10-18

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Maribel Elias

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Crop Storage Location Score: Coffee (Ethiopia - ~ 1Km)

The raster dataset consists of a 1km score grid for dairy processing industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on cattle dairy production intensification potential, defined using crop production, livestock production systems and cattle distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, dairy distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”dairyIntensification” * 0.4) + ("Crop Production" * 0.3) + ("Cost to dry ports" * 0.2) + (“Major Cities Accessibility” * 0.1)

The raster dataset consists of a 1km score grid for vegetables storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Vegetables. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1)

The raster dataset represents top location score areas suitable for tropical fruits storage, filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection).

The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The top score is selected/clipped using the exclusive criteria.

Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20km (0.18 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map.

This 1km resolution raster dataset is produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

Data publication: 2021-10-18

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Maribel Elias

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Crop Storage Final Location: Tropical fruits (Ethiopia - ~ 1Km)

The raster dataset consists of a 1km score grid for wheat storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Wheat. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1).

The raster dataset consists of a 1km score grid for cereal storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Cereal. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1)

The raster dataset represents top location score areas suitable for coffee storage, filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection).

The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The top score is selected/clipped using the exclusive criteria.

Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20km (0.18 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map.

This 1km resolution raster dataset is produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

Data publication: 2021-10-18

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Maribel Elias

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Crop Storage Final Location: Coffee (Ethiopia - ~ 1Km)

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

The strongest driver of these thematic maps is monthly rainfall (or lack of it) aggregated to Woreda. Probably there is a population density component thrown in. In other words, areas that are dry with lots of people is flagged as a higher priority.Source: Disaster Risk Management and Food Security Sector (DRMFSS) and Emergency Nutrition Coordination Unit (ENCU), data downloaded from HumanitarianResponse.info.

The raster dataset consists of a 1km score grid for tropical fruits storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse siting: • Supply: Tropical fruit. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1)

The strongest driver of these thematic maps is monthly rainfall (or lack of it) aggregated to Woreda. Probably there is a population density component thrown in. In other words, areas that are dry with lots of people is flagged as a higher priority.Source: Disaster Risk Management and Food Security Sector (DRMFSS) and Emergency Nutrition Coordination Unit (ENCU), data downloaded from HumanitarianResponse.info.