Content

There are three csv files.One is of world population by year (1955-2020).The second file is of world population by region and the third file is of world population by countries.In which there is population of each country in our world till year 2020. Features in these 3 datasets :

• Country : Name of Country
• Population : Population of each country
• Yearly % Change : Population change by a year in precentage
• Yearly Change : Population change by a year
• Median Age : Megian age of total population in each country
• Fertility Rate : Number of live birth per 1,000 women of reproductive age(ages 15 to 49 year) per year
• Density : Number of people per unit area
• Urban population : Urban Population in each country
• Migrants : Migrant population in each country
• World Share : % of population of each country/total world population

Acknowledgements

I have scraped these 3 datasets from worldometers.info website using BeautifulSoup

Inspiration

Analyse the increase of world population in last 10 year and do the world population forecast .Find 10 largest countries by population and population density

This dataset contains carbon fluxes for the 10 largest countries in the world (here EU27 is treated as a country) using GOSAT and OCO-2 observational constraints for 2017-2019.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

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

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

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

The average for 2023 based on 154 countries was 95 percent. The highest value was in Luxembourg: 394.22 percent and the lowest value was in Sudan: 2.47 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

United States US: Income Share Held by Highest 10% data was reported at 30.600 % in 2016. This records an increase from the previous number of 30.100 % for 2013. United States US: Income Share Held by Highest 10% data is updated yearly, averaging 30.100 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 30.600 % in 2016 and a record low of 25.300 % in 1979. United States US: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

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

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

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...

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

We present the GLOBAL ROADKILL DATA, the largest worldwide compilation of roadkill data on terrestrial vertebrates. We outline the workflow (Fig. 1) to illustrate the sequential steps of the study, in which we merged local-scale survey datasets and opportunistic records into a unified roadkill large dataset comprising 208,570 roadkill records. These records include 2283 species and subspecies from 54 countries across six continents, ranging from 1971 to 2024.Large roadkill datasets offer the advantage ofpreventing the collection of redundant data and are valuable resources for both local and macro-scale analyses regarding roadkill rates, road and landscape features associated with roadkill risk, species more vulnerable to road traffic, and populations at risk due to additional mortality. The standardization of data - such as scientific names, projection coordinates, and units - in a user-friendly format, makes themreadily accessible to a broader scientific and non-scientific community, including NGOs, consultants, public administration officials, and road managers. The open-access approach promotes collaboration among researchers and road practitioners, facilitating the replication of studies, validation of findings, and expansion of previous work. Moreover, researchers can utilize suchdatasets to develop new hypotheses, conduct meta-analyses, address pressing challenges more efficiently and strengthen the robustness of road ecology research. Ensuring widespreadaccess to roadkill data fosters a more diverse and inclusive research community. This not only grants researchers in emerging economies with more data for analysis, but also cultivates a diverse array of perspectives and insightspromoting the advance of infrastructure ecology.MethodsInformation sources: A core team from different continents performed a systematic literature search in Web of Science and Google Scholar for published peer-reviewed papers and dissertations. It was searched for the following terms: “roadkill* OR “road-kill” OR “road mortality” AND (country) in English, Portuguese, Spanish, French and/or Mandarin. This initiative was also disseminated to the mailing lists associated with transport infrastructure: The CCSG Transport Working Group (WTG), Infrastructure & Ecology Network Europe (IENE) and Latin American & Caribbean Transport Working Group (LACTWG) (Fig. 1). The core team identified 750 scientific papers and dissertations with information on roadkill and contacted the first authors of the publications to request georeferenced locations of roadkill andofferco-authorship to this data paper. Of the 824 authors contacted, 145agreed to sharegeoreferenced roadkill locations, often involving additional colleagues who contributed to data collection. Since our main goal was to provide open access to data that had never been shared in this format before, data from citizen science projects (e.g., globalroakill.net) that are already available were not included.Data compilation: A total of 423 co-authors compiled the following information: continent, country, latitude and longitude in WGS 84 decimal degrees of the roadkill, coordinates uncertainty, class, order, family, scientific name of the roadkill, vernacular name, IUCN status, number of roadkill, year, month, and day of the record, identification of the road, type of road, survey type, references, and observers that recorded the roadkill (Supplementary Information Table S1 - description of the fields and Table S2 - reference list). When roadkill data were derived from systematic surveys, the dataset included additional information on road length that was surveyed, latitude and longitude of the road (initial and final part of the road segment), survey period, start year of the survey, final year of the survey, 1st month of the year surveyed, last month of the year surveyed, and frequency of the survey. We consolidated 142 valid datasets into a single dataset. We complemented this data with OccurenceID (a UUID generated using Java code), basisOfRecord, countryCode, locality using OpenStreetMap’s API (https://www.openstreetmap.org), geodeticDatum, verbatimScientificName, Kingdom, phylum, genus, specificEpithet, infraspecificEpithet, acceptedNameUsage, scientific name authorship, matchType, taxonRank using Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters) and link of the associatedReference (URL).Data standardization - We conducted a clustering analysis on all text fields to identify similar entries with minor variations, such as typos, and corrected them using OpenRefine (http://openrefine.org). Wealsostandardized all date values using OpenRefine. Coordinate uncertainties listed as 0 m were adjusted to either 30m or 100m, depending on whether they were recorded after or before 2000, respectively, following the recommendation in the Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters).Taxonomy - We cross-referenced all species names with the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy using Java and GBIF’s API (https://doi.org/10.15468/39omei). This process aimed to rectify classification errors, include additional fields such as Kingdom, Phylum, and scientific authorship, and gather comprehensive taxonomic information to address any gap withinthe datasets. For species not automatically matched (matchType - Table S1), we manually searched for correct synonyms when available.Species conservation status - Using the species names, we retrieved their conservation status and also vernacular names by cross-referencing with the database downloaded from the IUCNRed List of Threatened Species (https://www.iucnredlist.org). Species without a match were categorized as "Not Evaluated".Data RecordsGLOBAL ROADKILL DATA is available at Figshare27 https://doi.org/10.6084/m9.figshare.25714233. The dataset incorporates opportunistic (collected incidentally without data collection efforts) and systematic data (collected through planned, structured, and controlled methods designed to ensure consistency and reliability). In total, it comprises 208,570 roadkill records across 177,428 different locations(Fig. 2). Data were collected from the road network of 54 countries from 6 continents: Europe (n = 19), Asia (n = 16), South America (n=7), North America (n = 4), Africa (n = 6) and Oceania (n = 2).(Figure 2 goes here)All data are georeferenced in WGS84 decimals with maximum uncertainty of 5000 m. Approximately 92% of records have a location uncertainty of 30 m or less, with only 1138 records having location uncertainties ranging from 1000 to 5000 m. Mammals have the highest number of roadkill records (61%), followed by amphibians (21%), reptiles (10%) and birds (8%). The species with the highest number of records were roe deer (Capreolus capreolus, n = 44,268), pool frog (Pelophylax lessonae, n = 11,999) and European fallow deer (Dama dama, n = 7,426).We collected information on 126 threatened species with a total of 4570 records. Among the threatened species, the giant anteater (Myrmecophaga tridactyla, VULNERABLE) has the highest number of records n = 1199), followed by the common fire salamander (Salamandra salamandra, VULNERABLE, n=1043), and European rabbit (Oryctolagus cuniculus, ENDANGERED, n = 440). Records ranged from 1971 and 2024, comprising 72% of the roadkill recorded since 2013. Over 46% of the records were obtained from systematic surveys, with road length and survey period averaging, respectively, 66 km (min-max: 0.09-855 km) and 780 days (1-25,720 days).Technical ValidationWe employed the OpenStreetMap API through Java todetect location inaccuracies, andvalidate whether the geographic coordinates aligned with the specified country. We calculated the distance of each occurrence to the nearest road using the GRIP global roads database28, ensuring that all records were within the defined coordinate uncertainty. We verified if the survey duration matched the provided initial and final survey dates. We calculated the distance between the provided initial and final road coordinates and cross-checked it with the given road length. We identified and merged duplicate entries within the same dataset (same location, species, and date), aggregating the number of roadkills for each occurrence.Usage NotesThe GLOBAL ROADKILL DATA is a compilation of roadkill records and was designed to serve as a valuable resource for a wide range of analyses. Nevertheless, to prevent the generation of meaningless results, users should be aware of the followinglimitations:- Geographic representation – There is an evident bias in the distribution of records. Data originatedpredominantly from Europe (60% of records), South America (22%), and North America (12%). Conversely, there is a notable lack of records from Asia (5%), Oceania (1%) and Africa (0.3%). This dataset represents 36% of the initial contacts that provided geo-referenced records, which may not necessarily correspond to locations where high-impact roads are present.- Location accuracy - Insufficient location accuracy was observed for 1% of the data (ranging from 1000 to 5000 m), that was associated with various factors, such as survey methods, recording practices, or timing of the survey.- Sampling effort - This dataset comprised both opportunistic data and records from systematic surveys, with a high variability in survey duration and frequency. As a result, the use of both opportunistic and systematic surveys may affect the relative abundance of roadkill making it hard to make sound comparisons among species or areas.- Detectability and carcass removal bias - Although several studies had a high frequency of road surveys,the duration of carcass persistence on roads may vary with species size and environmental conditions, affecting detectability. Accordingly, several approaches account for survey frequency and target speciesto estimate more

The global gender gap index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2024, the country offering most gender equal conditions was Iceland with a score of 0.94. Overall, the Nordic countries make up four of the five most gender equal countries in the world. The Nordic countries are known for their high levels of gender equality, including high female employment rates and evenly divided parental leave. Sudan is the least gender equal country Sudan is found on the other end of the scale, ranked as the least gender equal country in the world. Conditions for civilians in the North African country has worsened significantly after a civil war broke out in April 2023. Especially girls and women are suffering and have become victims of sexual violence. Moreover, nearly nine million people are estimated to be at acute risk of famine. The Middle East and North Africa has the largest gender gap Looking at the different world regions, the Middle East and North Africa has the largest gender gap as of 2023, just ahead of South Asia. Moreover, it is estimated that it will take another 152 years before the gender gap in the Middle East and North Africa is closed. On the other hand, Europe has the lowest gender gap in the world.

KU herpetology houses one of the largest herpetology collections in the world (340,000 specimens representing more than 5000 species from 156 countries). The KU collections include the world’s largest collection of neotropical amphibian and reptile specimens (200,000+) as well as substantial numbers of Nearctic (80,000+) and Asian (20,000+) specimens. KU holdings are particularly strong for the U.S., Ecuador, Mexico, the Dominican Republic, Costa, Rica, Haiti, the Philippines, Peru and Panama. The collection from Kansas is the state’s largest (20,000+). The type collection includes nearly 400 primary types, mostly amphibians. KU Herpetology also maintains 5000 cleared-and-stained osteological preparations, nearly 5000 dried skeletons, and one of the world’s largest collections of amphibian larvae (6000+ lots). The KU digital archive includes more than 12,000 digital images and more than 1500 acoustic recordings.

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

The increase in hydro dams in the Mekong River amidst the prevalence of multidrug-resistant malaria in Cambodia has raised concerns about global public health. Political conflicts during Covid-19 pandemic led cross-border movements of malaria cases from Myanmar and caused health care burden in Thailand. While previous publications used climatic indicators for predicting mosquito-borne diseases, this research used globally recognizable World Bank indicators to find the most impactful indicators related with malaria and shed light on the predictability of mosquito-borne diseases. The World Bank datasets of the World Development Indicators and Climate Change Knowledge Portal contain 1494 time series indicators. They were stepwise screened by Pearson and Distance correlation. The sets of five and four contain respectively 19 and 149 indicators highly correlated with malaria incidence which were found similarly among five and four GMS countries. Living areas, ages, career, income, technology accessibility, infrastructural facilities, unclean fuel use, tobacco smoking, and health care deficiency have affected malaria incidence. Tonle Sap Lake, the largest freshwater lake in Southeast Asia, could contribute to the larval habitat. Seven groups of indicator topics containing 92 indicators with not-null datapoints were analyzed by regression models, including Multiple Linear, Ridge, Lasso, and Elastic Net models to choose 7 crucial features for malaria prediction via Long Short Time Memory network. The indicator of people using at least basic sanitation services and people practicing open defecation were health factors had most impacts on regression models. Malaria incidence could be predicted by one indicator to reach the optimal mean absolute error which was lower than 10 malaria cases (per 1,000 population at risk) in the Long Short Time Memory model. However, public health crises caused by political problems should be analyzed by political indexes for more precise predictions.

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2022, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data