This dataset includes worldwide data on a long timespan: - War: HCED Data v2.csv gather data about conflicts since 1468 BC to August 15, 2022. The data includes battle locations and years. This dataset has been created with the intention of producing a worldwide exhaustive catalogue of wars. - Population: population.csv holds records and estimates of world population, by location, since 10000 BCE.

Personally, I intend to use these two in conjunction with the popular Kaggle dataset Countries of the World, since I might need countries areas to estimate population densities.

Check out the output of my Cleaning War and Population Data notebook for a cleaner version of the dataset.

world_battles_and_demographics_master_table is my final version of the dataset, it holds a selected subset of the original information in a single place. Check out the output of my Wrangling War and Population Data if you're interestd in how I combined the tables.

These data were collected to study the trends and changes in the frequency, magnitude, severity, and intensity of international wars, civil wars, and international disputes. The data collection consists of two separate datasets. For each dataset, the unit of analysis is the participant in a particular conflict. While the two datasets are related, they are mutually exclusive in that each describes a particular type of war (interstate or civil) or a dispute. Part 1, Experience of Each Interstate System Member in Each War, provides information on each member's experience in each war. To be considered a nation participant, certain minimal criteria of population and diplomatic recognition were used. Qualifying nation participants are classified as to whether they were members of the European central system at the time of the war and, therefore, active and influential in European diplomacy. The geographical location of the war is coded as well as the severity of the war, as determined by its duration and the number of deaths resulting from battle. The pre-war population of each nation participant is also coded. Part 2, Major Civil Wars Between 1816 and 1980, is a study of 106 major civil wars involving 139 participants between 1816 and 1980. An internal war is classified as a major civil war if (1) military action was involved, (2) the national government at the time was actively involved, (3) effective resistance (as measured by the ratio of fatalities of the weaker to the stronger forces) occurred on both sides, and (4) at least 1,000 battle deaths resulted during the civil war. The geographical area in which the war was fought is also coded as well as whether nations outside the civil war actively and overtly participated on one side or the other. The duration, beginning, and ending dates of the civil war, and the pre-war population and number in the armed forces of each participant, are also included. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR09905.v1. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats and for additional years of data,

The dataset is a compilation of three different datasets: 1) Incidence of Civil War 1960-2006 (UCDP/PRIO Armed Conict Dataset) 2) GDP 1960-2006 (World Bank's World Development Indicators Dataset) 3) Population 1960-2006 (World Bank's World Development Indicators Dataset)

This dataset describes micro-level conflict activities relating to Nigeria, which was extracted from the ACLED. Nigeria is known to have witnessed a fair share of total civil conflicts in sub-Saharan Africa, including the acclaimed first modern warfare in the subcontinent – the Nigeria (versus Biafra) civil war . The large heterogeneous Nigerian population, divided along ethnic, religious and cultural lines continues to generate latent frictions and manifest conflicts. As of today, there are a number of deadly militias operating within the country, notably the Boko Haram and pastoral herders whose activities are recognised globally . This data may be relevant in understanding the nexus between the recent Nigeria's conflicts environment and national development along economic, social and political dimensions. In addition, it provides safety planning resources for individuals' safety and governments.

These instructional materials were prepared for use with AGRICULTURAL AND DEMOGRAPHIC RECORDS FOR HOUSEHOLDS IN THE NORTH, 1860 (ICPSR 7420), compiled by Fred Bateman and James D. Foust. The data file and accompanying documentation are provided to assist educators in (an SPSS portable file) instructing students about the history of agriculture and rural life in the North, just prior to the Civil War. An instructor's handout has also been included. This handout contains the following sections, among others: (1) General goals for student analysis of quantitative datasets, (2) Specific goals in studying this dataset, (3) Suggested appropriate courses for use of the dataset, (4) Tips for using the dataset, and (5) Related secondary source readings. Demographic, occupational, and economic information for over 21,000 rural households in the northern United States in 1860 are presented in the dataset. The data were obtained from the manuscript agricultural and population schedules of the 1860 United States Census and are provided for all households in a single township from each of the 102 randomly-selected counties in 16 northern states. Variables in the dataset include farm values, livestock, and crop production figures for the households that owned or operated farms (over half the households sampled), as well as value of real and personal estate, color, sex, age, literacy, school attendance, occupation, place of birth, and parents' nationality of all individuals residing in the sampled townships.

The “Contested Freedom” dataset is compiled entirely of information for free persons of color who resided in the city of Savannah, Georgia, registered between 1823 and 1842. The dataset contains 1,321 named individuals residing in Chatham County. Savannah’s free Black population was made up of previously enslaved people who were manumitted by their owners, Black children born to free mothers, and emigrés from St. Domingue who fled to Savannah directly after the Haitian Revolution. This dataset, extracted from the “Savannah, Georgia, Registers of Free People of Color, 1817-1864,” includes the years 1823-1829 and 1833, 1835, and 1842. This register was collected by the city of Savannah throughout the antebellum era and right before the close of the Civil War. The information includes: names, age, current residence, occupation(s), and guardian(s), and, in some instances, property (or lack thereof), number of slaves owned, and parentage.

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.

Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael

BackgroundGrasping the human cost of war requires comprehensive evaluation of multiple dimensions of conflict. While the number of civilian casualties is a frequently used indicator to evaluate intensity of violence in conflict, the inclusion of other indicators may provide a more complete understanding of how war impacts people and their communities. The Syrian conflict has been specifically marked by attacks against healthcare facilities, and the advancement of technology has provided an avenue for remote data analysis of conflict trends. This study aims to determine the feasibility of using publicly available, online data of attacks on healthcare facilities to better describe population-level violence in the Syrian Civil War.MethodsThis study utilized publicly available datasets from the Violations Documentation Center (VDC) and Physicians for Human Rights (PHR) to compare trends in attacks on healthcare facilities and civilian casualties from March 2011 to November 2017 in the Syrian Civil War. We used descriptive statistics, bivariate tests and a multivariable hypothesis testing model to measure the association between the two indicators while adjusting for confounding variables.ResultsWe examined for associations between attacks on healthcare facilities and overall civilian casualties. In the adjusted regression model, each attack on a healthcare facility in the Syrian conflict corresponded to an estimated 260 reported civilian casualties in the same month (95% CI: 227 to 294). This model adjusted for population displacement (using number of registered refugees as a proxy). The May 2014 interaction term, used a transition point of early/late war based on political events during that time, illustrated that each healthcare facility attack after May 2014 corresponded to a statistically significant decrease of 228 civilian deaths. This suggests that although attacks on healthcare facilities continued to contribute to overall civilian deaths, the scale that this was happening was lower after May 2014.ConclusionIn the Syrian Civil War, our findings suggest that the inclusion of other humanitarian indicators, such as attacks on hospitals, may add granularity to traditional indicators of violence (e.g. such as civilian casualties) to develop a more nuanced understanding of the warring tactics used and violence against civilians in the Syrian conflict. This exploratory case study represents a novel approach to utilizing open-source data along with statistical analysis to interpret violence against civilians. Future research could benefit from analyzing attacks on healthcare facilities and other civilian infrastructure concurrently with civilian casualty data for further data-driven utilization of open-source data.

How do large-mammal communities reassemble after being pushed to the brink of extinction? Few data are available to answer this question, as it is rarely possible to document both the decline and recovery of wildlife populations. Here we present the first in-depth quantitative account of war-induced collapse and postwar recovery in a diverse assemblage of large herbivores. In Mozambique’s Gorongosa National Park, we assembled data from 15 aerial wildlife counts conducted before (1968–1972) and after (1994–2018) the Mozambican Civil War (1977–1992). Pre-war total biomass density exceeded 9,000 kg km-2, but populations declined by >90% during the war. Since 1994, total biomass has substantially recovered, but species composition has shifted dramatically. Formerly dominant large herbivores—including elephant (Loxodonta africana), hippo (Hippopotamus amphibius), buffalo (Syncerus caffer), zebra (Equus quagga), and wildebeest (Connochaetes taurinus)—are now outnumbered by waterbuck (Kobus ellipsiprymnus) and other small to mid-sized antelopes. Waterbuck abundance has increased by an order of magnitude, with >55,000 individuals accounting for >74% of large-herbivore biomass in 2018. By contrast, elephant, hippo, and buffalo, which totaled 89% of pre-war biomass, now comprise just 23%. These trends mostly reflect natural population growth following the resumption of protection under the Gorongosa Restoration Project; reintroductions (465 animals of 7 species) accounted for a comparatively small fraction of the total numerical increase. Waterbuck are growing logistically, apparently as-yet unchecked by interspecific competition or predation (apex-carnivore abundance has been low throughout the post-war interval), suggesting a community still in flux. Most other herbivore populations have increased post-war, albeit at differing rates. Armed conflict remains a poorly understood driver of ecological change; our results demonstrate the potential for rapid post-war recovery of large-herbivore biomass, given sound protected-area management, but also suggest that restoration of community structure takes longer and may require active intervention.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Along side these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs. In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH –BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labor Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set. The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made. The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank. The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Mangement Team, made up of two professionals from each of the three statistical organizations. The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population’s living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population’s living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household. 3. To provide key contributions for development of government’s Poverty Reduction Strategy Paper, based on analyzed data.

The West Africa Coastal Vulnerability Mapping: Point and Gridded Locations of Fatalities, 2008-2013 data set consists of two layers: points representing the location of conflict events with fatalities within 200 kilometers from the coast during the time period from 2008 to 2013, and a raster layer created from the points using a kernel density interpolation of the number of fatalities. These layers were created from the Armed Conflict Location and Event Dataset (ACLED), which codes the dates and locations of all reported political violence events in over 50 developing countries. Political violence includes events that occur within civil wars and periods of instability. Armed conflict reduces human security and increases the sensitivity of populations to climate stressors.

Sear-B South-East Asia with low child and low adult mortality [Indonesia, Sri Lanka, Thailand]Estimated mid year population for 2003**Since a ceasefire was in effect for the civil war during this period, all deaths due to bombs and shootings were included under Violence rather than War.

Context

Africa is a continent that covers 6% of the Earth's surface and 20% of the land surface. Its area is 30,415,873 km2 with the islands, making it the third largest in the world if we count America as a single continent. With more than 1.3 billion inhabitants, Africa is the second most populous continent after Asia and represents 17.2% of the world population in 2020.

Africa abounds in very varied energy sources, distributed in distinct zones: abundance of fossil fuels (gas in North Africa, oil in the Gulf of Guinea and coal in southern Africa), hydraulic basins in Central Africa, deposit uranium; solar radiation in Sahelian countries; and geothermal capacities in East Africa. Despite this, it has been a prey to conflicts (socio-political, political, social, civil war, government mismanagement, etc.) since the independence of its countries. And also a land of fierce lust for powerful countries and large multinational corporations.

Content

data is acquired by ACLED (Armed Conflict Location & Event Data) project. The ACLED project report information on the type, agents, location, date, and other characteristics of political violence events, demonstrations and select politically relevant non-violent events. Also, ACLED focuses on tracking a range of violent and non-violent actions by political agents, including governments, rebels, militias, identity groups, political parties, external actors, rioters, protesters and civilians. Africa conflict 1997-2020 datasets is one of database of the ACLED project.

For detail acleddata.com Codebook: ACLED codebook Guide User Quick Guide

Acknowledgements

Thanks to “Armed Conflict Location & Event Data Project (ACLED); https://www.acleddata.com.”

Inspiration

Can you understand how conflicts evolve in Africa from 1997 to 2020 and what link is there between the energy ressources of certain regions of Africa and conflicts? (Make your Geopolitics, Geo-economics and Geo-energy skills in practical)

If you were to leave your home country, how far would you go, and for what reason? Just over the nearest border? Across an ocean? Or to the other side of the world?

People often equate international migration with long journeys. But most migrants actually travel shorter distances, as you might expect if you put yourself into their situation.

Understanding migration patterns helps governments around the world plan for population and economic changes.

This article addresses a simple but important question: how far do international migrants usually move from their home countries?

But before we look at how far migrants travel, it’s useful to keep in mind that most people don’t move to a different country. 96% of the world’s population lives in the country where they were born. That means the people we’ll focus on here are a small fraction of the global population.

Two examples: Syria and Venezuela Syria and Venezuela are two recent examples of countries with large-scale emigration, but for very different reasons — one caused by war, the other by economic collapse and political instability.

Since the start of its civil war in 2011, Syria has become a well-known case of large-scale emigration. By 2020, nearly half (48%) of all Syrian-born people — about 8.5 million — had left the country.

While we don’t have precise data on how far each migrant traveled, we do have reliable estimates of the countries they moved to. This data is published by the United Nations Department of Economic and Social Affairs.

As you can see on the chart, most Syrian emigrants have stayed close to home. The chart below shows Turkey, Lebanon, and Saudi Arabia as the top destinations, with Turkey alone hosting nearly 40% of them. Overall, a large majority of Syrian emigrants — 80% — have remained within Asia.

Demographic and socioeconomic characteristics of study groups.