OVERVIEW:
Migration is a key feature of our increasingly interconnected world. It has also become a flashpoint for debate in many countries, which underscores the importance of understanding the patterns of global migration and the economic impact that is created when people move across the world’s borders. The past few decades have seen an incredible rise in global immigration, with numbers of immigrants three times higher than those recorded in 1960. Currently, 3.3% of the world’s population is living in a different country than the one it was born in. Although media attention has been focused heavily on refugee migration, refugees are only a small share of migrant numbers. Migration is increasingly driven by opportunity-seeking behaviors due to economic disparity. In light of this mass migration movement, it’s unsurprising that global migration has created major demographic changes in the world. Perhaps one of the most interesting observations stemming from this middle class growth is the massive cleavage that divides the middle class in developed countries versus that of the emerging economies. Although both enjoy the same lifestyle and opportunities, the middle-class of developed countries is chronically stressed and lacks confidence in the future. In contrast, the middle class of emerging economies is increasingly optimistic, opportunistic, and enjoys an 8% growth per year. As migration flow data are often incomplete and not comparable across nations, the data we obtained has an estimate of a number of movements by linking changes in migrant stock data over time. Using statistical missing data methods, the data is estimated on five-year migrant flows that are required to meet differences in migrant stock totals. The data collected is present from 1990 to 2010. The objective of this visualization project is to generate various insights from the data set and support it with the interactive and compelling visualization evidence.
Our first insight is based on the below facts that explain the common migration pattern. · The USA as one of the country having highest number of immigrants followed by United Arab Emirates, Spain and Italy · Less migration to countries such as Pakistan and Afghanistan The above insight is drawn upon the evidence captured from the below visualizations.
On combining the migration data set with the development factors of each country, we obtained rock solid evidence from the visualizations why certain countries were highly immigrated and others not. Moving more labor to higher-productivity settings boosts global GDP. Migrants of all skill levels contribute to this effect, whether through innovation and entrepreneurship or through freeing up natives for higher-value work There are many development factors considered, including, but not limited to, GDP, employment in Industry, Labor force participation, Life expectancy, net migration and population.
This insight speaks about the migration prediction of all countries using the regression analysis in R. Migration pattern predicated shows a different pattern of migration on performing the analysis. Regression analysis is done using R software. The statistical method used is the linear regression model. A Linear regression model uses a dependent variable and list of the independent variables. The dependent variable in our case is the Net Migration where Net migration is the net total of migrants during the period, that is, the total number of immigrants less the annual number of emigrants, including both citizens and noncitizens and the independent variables are factors identified in our decision tree
PREDICTION
One of the surprising pattern found is The United States of America, with a two-year-old decade migration of 5,192,065 is now predicated as 1,929,677. This reduction can be attributed to various reasons such as the increase in the population of the country, less GDP growth and other political reasons. The other pattern is that Germany’s projected migration is 1,624,378 which is higher than its two-year-old decade migrant number. The thought process behind this could be the refugee migration from the Arab countries as a result of Arab Springs. Europe too envisions high migration as the reason could be the same as mentioned for Germany. Another interesting fact to know that Mexico’s projected migration has reduced significantly and this could be due to efforts of the USA government to stop illegal migration from Mexico. India and China show same migration pattern wherein the projected migration is higher than that of previous two-year decade migration. One or more economic factors of these countries as discussed in small multiples could influence the migration number.
