The world's most accurate population datasets (according to Data for Good at Meta). Seven maps/datasets for the distribution of various populations in African countries: (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).

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

Context

This project analyzes the 2020 World Happiness Report to draw conclusions about the general well being of Africa. It uses several CSV files consisting of survey responses formed from a Google Form survey, data from the 2020 World Happiness Report and data on countries only in Africa from the 2020 World Happiness Report. The main data set used includes over 150 countries and their happiness scores, freedom to make life choices, social support, healthy life expectancy, regional indicator, perceptions of corruption and generosity. This analysis was done to answer the following data-driven questions: 'Which African country ranked the happiest in 2020?' and 'Which variable predicts or explains Africa's happiness score?'

This project includes several programs created in R and Python.

Background

The Gallup World Poll (GWP) is conducted annually to measure and track public attitudes concerning political, social and economic issues, including controversial and sensitive subjects. Annually, this poll tracks attitudes toward law and order, institutions and infrastructure, jobs, well-being and other topics for approximately 150 countries worldwide. The data gathered from the GWP is used to create an annual World Happiness Report (WHR). The World Happiness Report is conducted to review the science of understanding and measuring the subjective well-being and to use survey measures of life satisfaction to track the quality of lives in over 150 countries.

At first glance, it seems that world happiness isn't important or maybe it's just an emotional thing. However, several governments have started to look at happiness as a metric to measure success. Happiness Scores or Subjective Well-being (SWB) are national average responses to questions of life evaluation. They are important because they remind policy makers and people in power that happiness is based on social capital, not just financial. Happiness is often considered an essential and useful way to guide public policies and measure their effectiveness. It is also important to note that happiness scores point out the importance of qualitative rather than quantitative. At times, quality is better than quantity.

Africa is the world's second largest and second most populous continent in the world. It consists of 54 countries meaning that Africa has the most countries. Africa has approximately 30% of the earth's mineral resources and has the largest reserves of precious metals. Africa reserves over 40% of the gold reserves, 60% on cobalt and 90% of platinum. However, Africa unfortunately has the most developmental challenges. It is the world's poorest and most underdeveloped continent. Africa is also almost 100% colonized with the exceptions of Ethiopia and Liberia. Given this information, one can wonder what the SWB or state of happiness is in Africa?

This site analyzes the 2020 World Happiness Report to draw conclusions to data-drive questions listed later on this page. The focus is specifically on countries in Africa. Even though there are 54 countries in Africa, only 43 participated in the 2020 WHR.

Content

The dataset used is generated from the 'World Happiness Report 2020'. This dataset contains the Happiness Score for over 150 countries for the year of 2020. The data gathered from the Gallup World Poll gives a national average of Happiness scores for countries all over the world. It is a annual landmark survey of the state of global happiness.

This dataset is from the data repository "Kaggle". On Kaggle's dataset page, I searched for Africa Happiness after filtering the search to CSV file type. I wasn't able to find any datasets that could answer my questions that didn't include other countries from different continents. I decided to use a Global Happiness Report to answer the questions I have. The dataset I am using was publish by Micheal Londeen and it was created on March 24, 2020. His main source is the World Happiness Report for 2020.

Variables

Happiness score or subjective well-being (variable name ladder ): The survey measure of SWB is from the Feb 28, 2020 release of the Gallup World Poll (GWP) covering years from 2005 to 2019. Unless stated otherwise, it is the national average response to the question of life evaluations. The English wording of the question is “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” This measure is also referred to as Cantril life ladder, or just life ladder in our analysis.

Healthy Life Expectancy (HLE). Healthy life expectancies at birth are based on the data extracted from the World Health Organization’s (WHO) Global Health Observatory dat...

Context

As the world is fighting against this invisible enemy a lot of data-driven students like me want to study it as well as we can. There is an enormous number of data set available on covid19 today but as a beginner, in this field, I wanted to find some more simple data. So here I come up with this covid19 data set which I scrapped from "https://www.worldometers.info/coronavirus". It is my way of learning by doing. This data is till 5/17/2020. I will keep on updating it.

Content

The dataset contains 194 rows and 12 columns which are described below:-

Country: Contains the name of all Countries. Total_Cases: It contains the total number of cases the country has till 5/17/2020. Total_Deaths: Total number of deaths in that country till 5/17/2020. Total_Recovered: Total number of individuals recovered from covid19. Active_Cases: Total active cases in the country on 5/17/2020. Critical_Cases: Number of patients in critical condition. Cases/Million_Population: Number of cases per million population of that country. Deaths/Million_Population: Number of deaths per million population of that country. Total_Tests: Total number of tests performed 5/17/2020 Tests/Million_Population: Number of tests performed per million population. Population: Population of the country Continent: Continent in which the country lies.

Acknowledgements

"https://www.worldometers.info/coronavirus/"

The Gridded Population of the World, Version 3 (GPWv3): Population Count Grid consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population count grids contain estimates of the number of persons per grid cell. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

The “richness index” represents the level of economical wellbeing a country certain area in 2010. Regions with higher income per capita and low poverty rate and more access to market are wealthier and are therefore better able to prepare for and respond to adversity. The index results from the second cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies four dominant variables, namely “GDPppp per capita”, “agriculture share GDP per agriculture sector worker”, “poverty rate” and “market accessibility”, assigning weights of 0.33, 0.26, 0.25 and 0.16, respectively. Before to perform the analysis all variables were log transformed (except the “agriculture share GDP per agriculture sector worker”) to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for the “poverty rate” and “market accessibility”) in order to be comparable. The 0.5 arc-minute grid total GDPppp is based on the night time light satellite imagery of NOAA (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161) and adjusted to national total as recorded by International Monetary Fund for 2010. The “GDPppp per capita” was calculated dividing the total GDPppp by the population in each pixel. Further, a focal statistic ran to determine mean values within 10 km. This had a smoothing effect and represents some of the extended influence of intense economic activity for the local people. Country based data for “agriculture share GDP per agriculture sector worker” were calculated from GDPppp (data from International Monetary Fund) fraction from agriculture activity (measured by World Bank) divided by the number of worker in the agriculture sector (data from World Bank). The tabular data represents the average of the period 2008-2012 and were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for the “poverty rate” were estimated by NOAA for 2003 using nighttime lights satellite imagery. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The 0.5 arc-minute grid “market accessibility” measures the travel distance in minutes to large cities (with population greater than 50,000 people). This dataset was developed by the European Commission and the World Bank to represent access to markets, schools, hospitals, etc.. The dataset capture the connectivity and the concentration of economic activity (in 2000). Markets may be important for a variety of reasons, including their abilities to spread risk and increase incomes. Markets are a means of linking people both spatially and over time. That is, they allow shocks (and risks) to be spread over wider areas. In particular, markets should make households less vulnerable to (localized) covariate shocks. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-05-15

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

Richness index (2010)

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

Context

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease has since spread worldwide, leading to an ongoing pandemic.

Symptoms of COVID-19 are variable, but often include fever, cough, headache, fatigue, breathing difficulties, and loss of smell and taste. Symptoms may begin one to fourteen days after exposure to the virus. At least a third of people who are infected do not develop noticeable symptoms. Of those people who develop symptoms noticeable enough to be classed as patients, most (81%) develop mild to moderate symptoms (up to mild pneumonia), while 14% develop severe symptoms (dyspnea, hypoxia, or more than 50% lung involvement on imaging), and 5% suffer critical symptoms (respiratory failure, shock, or multiorgan dysfunction). Older people are at a higher risk of developing severe symptoms. Some people continue to experience a range of effects (long COVID) for months after recovery, and damage to organs has been observed. Multi-year studies are underway to further investigate the long-term effects of the disease.

COVID-19 transmits when people breathe in air contaminated by droplets and small airborne particles containing the virus. The risk of breathing these in is highest when people are in close proximity, but they can be inhaled over longer distances, particularly indoors. Transmission can also occur if splashed or sprayed with contaminated fluids in the eyes, nose, or mouth, and, rarely, via contaminated surfaces. People remain contagious for up to 20 days and can spread the virus even if they do not develop symptoms.

Several testing methods have been developed to diagnose the disease. The standard diagnostic method is by detection of the virus' nucleic acid by real-time reverse transcription-polymerase chain reaction (rRT-PCR), transcription-mediated amplification (TMA), or by reverse transcription loop-mediated isothermal amplification (RT-LAMP) from a nasopharyngeal swab.

Preventive measures include physical or social distancing, quarantining, ventilation of indoor spaces, covering coughs and sneezes, hand washing, and keeping unwashed hands away from the face. The use of face masks or coverings has been recommended in public settings to minimize the risk of transmissions.

While work is underway to develop drugs that inhibit the virus (and several vaccines for it have been approved and distributed in various countries, which have since initiated mass vaccination campaigns), the primary treatment is symptomatic. Management involves the treatment of symptoms, supportive care, isolation, and experimental measures.

Source - https://en.wikipedia.org/wiki/COVID-19

Content

This Dataset is a collection of records for COVID-19 (World and Continent wise).

Structure of the Dataset

https://i.imgur.com/sbvsXhr.png" alt="">

Acknowledgements

This Dataset is created from: https://www.worldometers.info/. If you want to learn more, you can visit the Website.

Cover Photo by Hakan Nural on Unsplash

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

The Gridded Population of the World, Version 3 (GPWv3): Population Density Grid, Future EstimatesFuture Estimates consists of estimates of human population for the years 2005, 2010, and 2015 by 2.5 arc-minute grid cells. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The future estimate population values are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics. All of the grids have been adjusted to match United Nations national level population estimates. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

COVID19-Algeria-and-World-Dataset

A coronavirus dataset with 104 countries constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the COVID-19. The assumptions for the different factors are as follows:

• Geography: some continents/areas may be more affected by the disease
• Climate: cold temperatures may promote the spread of the virus
• Healthcare: lack of hospital beds/doctors may lead to more human losses
• Economy: weak economies (GDP) have fewer means to fight the disease
• Demography: older populations may be at higher risk of the disease

The last column represents the number of daily tests performed and the total number of cases and deaths reported each day.

Data description

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Countries in the dataset by geographic coordinates

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• Europe: 33 countries
• Asia: 28 countries
• Africa: 21 countries
• North America: 11 countries
• South America: 8 countries
• Oceania: 3 countries

Statistical description of the data

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Data distribution

https://raw.githubusercontent.com/SamBelkacem/COVID19-Algeria-and-World-Dataset/master/Images/Data%20distribution.png">

Download

The dataset is available in an encoded CSV form on GitHub.

Python code

The Python Jupyter Notebook to read and visualize the data is available on nbviewer.

Data update

The dataset is updated every month with the latest numbers of COVID-19 cases, deaths, and tests. The last update was on March 01, 2021.

Data construction

The dataset is constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the coronavirus. Note that we selected only the main factors for which we found data and that other factors can be used. All data were retrieved from the reliable Our World in Data website, except for data on:

• Continents: www.kaggle.com/statchaitya/country-to-continent
• Geographic-coordinates: www.kaggle.com/eidanch/counties-geographic-coordinates
• Temperatures: www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
• Share of the population over 65 years old: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS
• GDP/Capita: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

Citation

If you want to use the dataset please cite the following arXiv paper, more details about the data construction are provided in it.

@article{belkacem_covid-19_2020,
  title = {COVID-19 data analysis and forecasting: Algeria and the world},
  shorttitle = {COVID-19 data analysis and forecasting},
  journal = {arXiv preprint arXiv:2007.09755},
  author = {Belkacem, Sami},
  year = {2020}
}

Contact

If you have any question or suggestion, please contact me at this email address: s.belkacem@usthb.dz

The most important key figures about population, households, population growth, births, deaths, migration, marriages, marriage dissolutions and change of nationality of the Dutch population.

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: 1899

Status of the figures: The 2023 figures on stillbirths and perinatal mortality are provisional, the other figures in the table are final.

Changes as of 23 December 2024: Figures with regard to population growth for 2023 and figures of the population on 1 January 2024 have been added. The provisional figures on the number of stillbirths and perinatal mortality for 2023 do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the provisional figures show a relatively larger number of children born at an unknown gestational age. Based on an internal analysis for 2022, it appears that in the majority of these cases, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded.

Changes as of 15 December 2023: None, this is a new table. This table succeeds the table Population; households and population dynamics; 1899-2019. See section 3. The following changes have been made: - The underlying topic folders regarding '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 the new figures be published? The figures for the population development in 2023 and the population on 1 January 2024 will be published in the second quarter of 2024.

The aim of this project was to analyze and provide insights on the global vaccination rollout. The goal was to develop a dashboard with daily metrics that capture the speed and scale of the vaccination program.

To achieve this, I utilized Tableau, a popular and robust data visualization software. After evaluating multiple datasets, I selected the Our World in Data COVID vaccination dataset. This dataset is regularly updated and provides the most recent official numbers from governments and health ministries worldwide.

The dashboard includes several key metrics:

Share of the population that has been partly, fully and not vaccinated. Continent and country wise vaccination. Vaccine doses administered. Correlation between GDP and vaccination program performance.

The dashboard offers the flexibility to filter data by date, country, and continent. Additionally, it incorporates visually appealing map charts to enhance the user experience.

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

Important Note: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Maize (Zea mays), also known as corn, is a crop of world wide importance. Originally domesticated in what is now Mexico, its tolerance of diverse climates has lead to its widespread cultivation. Globally, it is tied with rice as the second most widely grown crop. Only wheat is more widely grown. In Africa it is grown throughout the agricultural regions of the continent from the Nile Delta in the north to the country of South Africa in the south. In sub-Saharan Africa it is relied on as a staple crop for 50% of the population. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofmaize harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by theInternational Food Policy Research Institutein 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing theSpatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of maize as a staple food see theHarvest Choice webpage. For data on other agricultural species in Africa see these layers:Cassava Groundnut (Peanut) Millet Potato Rice Sorghum Sweet Potato and Yam Wheat Data for important agricultural crops in South America are availablehere. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer hasquery,identify, andexportimage services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixelswhich allows access to the full dataset. The source data for this layer are availablehere. This layer is part of a larger collection oflandscape layersthat you can use to perform a wide variety of mapping and analysis tasks. TheLiving Atlas of the Worldprovides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started follow these links: Landscape Layers - a reintroductionLiving Atlas Discussion Group

Accurate delineation of the urban and rural areas has a broad range of implications on the quality and reliability of agricultural production and socio-economic statistics, design of household survey, establishment of agricultural development strategies and policies, and effective resource allocation. Two most widely-used urban/rural mapping dataset across Africa, GRUMP (Global Rural and Urban Mapping Project; http://sedac.ciesin.columbia.edu/data/collection/grump-v1) and SAGE Urban Extents (https://nelson.wisc.edu/sage/data-and-models/schneider.php), uses the underlying datasets of 2000-2002. There are various pilot studies attempting to update the dataset in major metropolitan areas or specific countries, but no African continent-wide effort has been made to date. To address this, using the GRUMP 2000 data as the baseline, we used a set of recently-published datasets to identify the newly extended urban areas across Africa. Three main data sources were the nightlights data from Defense Meteorological Satellite Program (DMSP) 2010-2013, WorldPop 2010, and the MODIS Global Land Cover 2010-2013. Country-level urban population headcounts and their share of total population were acquired from the World Bank for 2010-2013 and used to control the total size of the urban population from the analysis is consistent with the statistics data at 1 km resolution.

The Latin America population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

 This documentation describes the Latin American Population Database, a
 collaborative effort between the International Center for Tropical
 Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID,
 Sioux Falls) and the World Resources Institute (WRI). This work is
 intended to provide a population database that compliments previous
 work carried out for Asia and Africa. This data set is more detailed
 than the Africa and Asia data sets. Population estimates for 1960,
 1970, 1980, 1990 and 2000 are also provided. The work discussed in the
 following paragraphs is also related to NCGIA activities to produce a
 global database of subnational population estimates (Tobler et
 al. 1995), and an improved database for the Asian continent (Deichmann
 1996a).

This site is part of a network of digital infrastructure built by Code for Africa (CfA) as a free open source software for use by human rights defending organisations. Reuse it to empower your own communities. CfA is Africa's largest non-profit civic technology and open data catalyst, with labs across the continent. CfA content on this site is released under a Creative Commons 4.0 International License. Refer to our attributions page for attributions of other work on the site.