Dataset
Downloading file 1 of 1: `population.csv`
| 2010 |
Afghanistan |
AFG |
AFG |
Afghanistan |
AFG |
AFG |
0 |
0 |
0 |
351907 |
3366 |
0 |
838250 |
NA |
NA |
| 2010 |
Iran (Islamic Rep. of) |
IRN |
IRN |
Afghanistan |
AFG |
AFG |
30 |
21 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
| 2010 |
Iraq |
IRQ |
IRQ |
Afghanistan |
AFG |
AFG |
6 |
0 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
| 2010 |
Pakistan |
PAK |
PAK |
Afghanistan |
AFG |
AFG |
6398 |
9 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
| 2010 |
Egypt |
ARE |
EGY |
Albania |
ALB |
ALB |
5 |
0 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
| 2010 |
China |
CHI |
CHN |
Albania |
ALB |
ALB |
6 |
0 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
| 2010 |
Palestinian |
GAZ |
PSE |
Albania |
ALB |
ALB |
5 |
0 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
| 2010 |
Iraq |
IRQ |
IRQ |
Albania |
ALB |
ALB |
5 |
0 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
| 2010 |
Serbia and Kosovo: S/RES/1244 (1999) |
SRB |
SRB |
Albania |
ALB |
ALB |
49 |
20 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
| 2010 |
Türkiye |
TUR |
TUR |
Albania |
ALB |
ALB |
5 |
0 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
Full Preview of Dataset
| year |
double |
The year. |
| coo_name |
character |
Country of origin name. |
| coo |
character |
Country of origin UNHCR code. |
| coo_iso |
character |
Country of origin ISO code. |
| coa_name |
character |
Country of asylum name. |
| coa |
character |
Country of asylum UNHCR code. |
| coa_iso |
character |
Country of asylum ISO code. |
| refugees |
double |
The number of refugees. |
| asylum_seekers |
double |
The number of asylum-seekers. |
| returned_refugees |
double |
The number of returned refugees. |
| idps |
double |
The number of internally displaced persons. |
| returned_idps |
double |
The number of returned internally displaced persons. |
| stateless |
double |
The number of stateless persons. |
| ooc |
double |
The number of others of concern to UNHCR. |
| oip |
double |
The number of other people in need of international protection. |
| hst |
double |
The number of host community members. |
Introduction
With information from UNHCR, UNRWA, and IDMC, the {refugees} R package dataset offers a never-before-seen window into the dynamics of global displacement from 2010 to 2022. With more than 64,000 entries, it tracks the lives of IDPs, refugees, asylum seekers, stateless people, and other groups, providing a detailed look at the complex dynamics of forced migration. In order to contribute to a more complex understanding of global migration trends, this approach aims to shed light on the complex interactions that occur between external factors—such as conflict, environmental catastrophes, or socio-political upheaval—and the resulting patterns of displacement.
Dataset Description:
Our target dataset comes from the {refugees} R package, which compiles extensive information on populations that have been strongly displaced from three main sources: UNHCR, UNRWA, and IDMC. Including refugees, asylum seekers, internally displaced people (IDPs), stateless people, and other groups of concern, this dataset covers a wide range of displacement categories from 2010 to 2022. The dataset has thorough information on each record categorized by the year of data collection, as well as quantitative metrics on various displaced population groups and the countries of origin and asylum (with the corresponding UNHCR and ISO codes). At 64,809 rows, this dataset is huge and provides a in detail look at changes in displacement over a period of more than ten years. It is an invaluable tool for studying worldwide migration patterns.
Justifications for Choosing this Dataset:
The significance of this dataset to current worldwide problems and displacement patterns—such as the effects of war, climate change, and political unrest on populations—was taken into consideration throughout its selection.
There is another reason this dataset was chosen: It is a combination of categorical data (such as country names, codes) and numerical (such as refugee and IDP counts) characteristics make it ideal for a wide variety of analytically and graphical techniques. Lastly, we’re interested in learning the underlying motivations behind this, such as the effects of COVID, war, climate change, etc.
This dataset, which provides a comprehensive overview of the size, dynamics, and geographic distribution of populations that have been strongly relocated. Because of its extensive duration—more than ten years—it is feasible to conduct a thorough examination of trends and patterns, enabling the correlation of these movements with worldwide shifts in the political, social, and environmental spheres.
The dataset’s versatility in capturing displacement categories allows for a more in-depth examination of different aspects of displacement worldwide, ranging from IDPs and stateless people to refugees and asylum seekers, offering a comprehensive viewpoint on the problem.
Questions
Question 01: How have the patterns in refugee populations evolved over time, and how have the stances of American political parties impacted these developments? Analyse the dynamics of refugee migrations towards US political environments.
Question 02: How the global refugee population fluctuate across the countries? Is their any external factors impact on refugee population like COVID-19 or war or climate change or financial stability?
Analysis plan
For Question 01:
Introduction:
We are examining the impact of US politics on patterns of worldwide migration. This study investigates the long-term effects of shifting political party ideologies in the United States on the number of refugees. We are interested in knowing how political statements and actions in the US affect asylum seekers.
The variables are involved: 'year' and 'refugees' (the total number of refugees)
Data Preparation: To spot trends, total the number of refugees by year and country of origin.
Analysis: To monitor changes in refugee populations over time, use time series analysis. External data on US foreign policy changes, political party statements, or major political events will be incorporated, and their temporal alignment with changes in refugee trends will be examined, in order to evaluate the influence of US political parties’ positions.
External Data: US political timelines, shifts in foreign policy, and noteworthy political pronouncements or occurrences pertaining to refugee policies.
Data Visualization Technique: Histogram or Line Graph
Discussion: With an emphasis on the 'year' and 'refugees' statistics, we will examine in detail how the number of refugees has fluctuated annually for our first question. For the purpose of identifying trends, we will classify refugees according to their year of origin. Using time series techniques, our primary study will track refugee journeys and relate them to significant shifts in US politics and party ideologies. To put our findings in context, we will include outside data on US politics and foreign policy. The objective of this plan is to quantify the impact of US politics on refugee patterns and contribute to the global discourse on refugee issues.
For Question 02:
Introduction:We’re investigating the patterns of change in refugee populations among nations as well as the effects of outside variables including economic conditions, conflict, and climate change. The purpose of this inquiry is to better comprehend the intricacies of worldwide migration and the ways in which significant world occurrences, like as the COVID-19 epidemic, influence refugee flows. The purpose of this research is to draw attention to how linked world events are and how they directly affect the lives of those who are displaced.
The variables are involved: The following are the primary variables: 'year', 'coo_name', (destination country name), 'coa_name', (asylum country name), and 'refugees' ,(the total number of refugees).
Data Preparation: Gathering Refugee Data: To track trends over time and across various regions, compile the "refugees" data annually and by nation. To have a better understanding of the relative impacts, consider creating refugee estimates by the populations of the countries of origin and asylum. Evaluate and correct any incomplete or missing data; depending on the requirements of the study, this may include imputation or the exclusion of some records.
Analysis: Trend analysis can be helped by using time-series analysis to look at the trends in the refugee population over time in each nation and worldwide. Find links between changes in the number of refugees and outside happenings. If the effect of an incident on refugee populations is not instantaneous, this could include lagged correlations. Assess the impact of major external events by comparing refugee trends before, during, and after them. To estimate the effect of the COVID-19 pandemic, for example, comparing the number of refugees before and after 2020 may be helpful.
External Data: Data on wars, conflicts, and duration of involvement collected from reputable sources, including the Uppsala Conflict Data Programmer (UCDP) and the Heidelberg Institute for International Conflict Research.Information about significant climate events from organizations like the National Oceanic and Atmospheric Administration (NOAA) and the IPCC (Intergovernmental Panel on Climate Change). Use both national and global economic statistics from the World Bank or the International Monetary Fund (IMF) to assess the consequences of financial stability information about the COVID-19 pandemic, including a chronology of outbreaks and key policy interventions, from sources including the World Health Organization (WHO) and Johns Hopkins University.
Data Visualization Technique: We will try to show this using Maps.
Discussion: We analyse refugee patterns over time across nations by analyzing 'year', 'coo_name', 'coa_name', and 'refugees' data. To acquire a clearer understanding of the repercussions, we will gather annual refugee data by nation and normalize it against population statistics. Time-series techniques will be used in the study to find trends and link them to important world events including wars, climate disasters, and changes in the economy. Our goal is to quantify the influence of these events on displacement by examining refugee trends around them. The context provided by external data, such as conflict histories, records of climate events, economic indicators, and epidemic timelines, will help to create a complete picture of the variables influencing global refugee movements.
Conclusion:
This analysis of the {refugees} dataset highlights the significant influence of outside factors on patterns of worldwide displacement throughout the last ten years. We have learned how major changes in refugee and displacement patterns have been fueled by geopolitical conflicts, natural disasters, and global crises through thorough investigation. The results emphasize not only how urgent it is to deal with the underlying causes of displacement but also how important it is to have strong, well-informed responses to the intricate problems associated with forced migration. These kinds of data-driven analysis are essential for developing humanitarian actions and policies that effectively address the world’s growing displacement crisis.