Animation: Visualizing Two Centuries of U.S. Immigration
America is a nation of immigrants, and though the country has seen a lot of new arrivals over the past two centuries, the rate of immigration has been far from steady.
War, famine, economic boom and bust, religious persecution, and government intervention have all caused wild swings in the rate of immigration from countries around the world.
Today’s striking animation, by Max Galka, is a great way to see changes in immigration over time. Inflows from specific countries rise and fall, and the top three countries of origin change numerous times over the years.
Below, is another way to look at the ebb and flow of American immigration since the early 1800s.
An important note. This data excludes forced migration (slavery) and illegal immigration.
Let’s look at the “waves” in more detail.
Wave one: The Old Immigration
From 1820 to 1870, over 7.5 million immigrants made their way over to the United States, effectively doubling the young country’s population in only half a decade.
Ireland, which was in the throes of the Potato Famine, saw half its population set sail for the U.S. during that time. This wave of immigration can still be seen in today’s demographics. There are now more Irish-Americans than there are Irish nationals.
The magnetic pull of the New World was profoundly felt in Germany as well. Growing public unrest in the region, caused by heavy taxation and political censorship, culminated in the German revolutions of 1848-49. Faced with severe hardship at home, millions of Germans made their way to America over the 1800s. It’s estimated that one-third of the total ethnic German population in the world now lives in the United States.
Wave Two: Gold Rush
Much of America’s early immigration was from various points in Europe, but there was one prominent exception: China.
The discovery of gold in California inspired Chinese workers to seek their fortune in America. After a crop failure in Southern China in 1852, tens of thousands of Chinese immigrants flooded into San Francisco.
Although the State of California was making millions of dollars off its Foreign Miners Tax, sentiment towards Chinese workers began to sour. Gold mines were being tapped out and white Californians blamed the Chinese for driving wages down.
Chinamen are getting to be altogether too plentiful in this country.
– John Bigler, Governor of California (1852-1856)
By 1882, the newly enacted the Chinese Exclusion Act had a chilling effect on Chinese immigration. The Exclusion Act has the dubious distinction of being the only American law barring a specific group from immigrating to the United States.
Wave Three: The New Immigration
The wave of immigration leading into the 20th century is referred to as The New Immigration.
In 1890, Ellis Island was designated as the main point of entry for newcomers entering the United States. In 1907 alone, Ellis Island processed a staggering 1,285,349 immigrants. To put this number in perspective, if all of those people settled in one place, they would’ve formed America’s fourth largest city almost overnight.
This massive influx of people into New York had profound implications on the city itself. In 1910, Manhattan’s population density was an astronomical 101,548 humans per square mile.
The immigrants arriving during this period – heavily represented by Italians, Hungarians, and Russians – were seeking religious freedom and economic opportunity. Certain industries, such as steel, meat-packing, and mining, were staffed by many new arrivals to the country.
During this time, one in four American workers were foreign-born.
The Great Depression
The National Origins Act’s quota system, which took effect in 1929, essentially slammed the door on most immigrants from Southern and Eastern Europe. Shortly after, the Great Depression further put a damper on immigration that would last well into the 20th century.
Wave Four: Mexico
After decades of sluggish immigration, the United States’ percentage of foreign-born citizens reached a low of 4.7% in 1970. But that was all about to change.
During the next decade, the number of states where Mexico was the top country of origin doubled in a single decade, and Mexicans became the dominant foreign-born population in the country. This migration was fueled by the Latin American debt crisis and later by NAFTA. The influx of cheap corn into Mexico caused hundreds of thousands of Mexicans from rural areas to search for more favorable economic opportunities. America was the obvious choice, particularly during the economic expansion of the 1990s.
This wave of immigration has shifted the country’s demographics considerably. Today, nearly one in five people in the United States are Hispanic.
Immigration trends are continually evolving, and America’s newest immigrants are often more likely to come from China or India. In fact, both countries surpassed Mexico as countries of origin for immigrants arriving in the U.S. in 2013. Today, the trend is even more pronounced.
Recent immigration numbers indicate that Asian immigrants will continue to shift America’s demographics in a new direction. Perhaps a new wave in the making?
Visualizing Over A Century of Global Fertility
Global fertility has almost halved in the past century. Which countries are most resilient, and which have experienced the most dramatic changes over time?
Visualizing Over A Century of World Fertility
In just 50 years, world fertility rates have been cut in half.
This sea change can be attributed to multiple factors, ranging from medical advances to greater gender equity. But generally speaking, as more women gain an education and enter the workforce, they’re delaying motherhood and often having fewer children in the process.
Today’s interactive data visualization was put together by Bo McCready, the Director of Analytics at KIPP Texas. Using numbers from Our World in Data, it depicts the changes in the world’s fertility rate—the average number of children per woman—spanning from the beginning of the 20th century to present day.
A Demographic Decline
The global fertility rate fell from 5.25 children per woman in 1900, to 2.44 children per woman in 2018. The steepest drop in this shift happened in a single decade, from 1970 to 1980.
In the interactive graphic, you’ll see graphs for 200 different countries and political entities showing their total fertility rate (FTR) over time. Here’s a quick summary of the countries with the highest and lowest FTRs, as of 2017:
|Top 10 Countries||Fertility rate||Bottom 10 Countries||Fertility Rate|
|🇳🇪 Niger||7.13||🇹🇼 Taiwan||1.22|
|🇸🇴 Somalia||6.08||🇲🇩 Moldova||1.23|
|🇨🇩 Democratic Republic of Congo||5.92||🇵🇹 Portugal||1.24|
|🇲🇱 Mali||5.88||🇸🇬 Singapore||1.26|
|🇹🇩 Chad||5.75||🇵🇱 Poland||1.29|
|🇦🇴 Angola||5.55||🇬🇷 Greece||1.3|
|🇧🇮 Burundi||5.53||🇰🇷 South Korea||1.33|
|🇺🇬 Uganda||5.41||🇭🇰 Hong Kong||1.34|
|🇳🇬 Nigeria||5.39||🇨🇾 Cyprus||1.34|
|🇬🇲 Gambia||5.29||🇲🇴 Macao||1.36|
At a glance, the countries with the highest fertility are all located in Africa, while several Asian countries end up in the lowest fertility list.
The notable decade of decline in average global fertility can be partially traced back to the actions of the demographic giants China and India. In the 1970s, China’s controversial “one child only” policy and India’s state-led sterilization campaigns caused sharp declines in births for both countries. Though they hold over a quarter of the world’s population today, the effects of these government decisions are still being felt.
Population Plateau, or Cliff?
The overall decline in fertility rates isn’t expected to end anytime soon, and it’s even expected to fall past 2.1 children per woman, which is known as the “replacement rate”. Any fertility below this rate signals fewer new babies than parents, leading to an eventual population decline.
Experts predict that world fertility will further drop from 2.5 to 1.9 children per woman by 2100. This means that global population growth will slow down or possibly even go negative.
Africa will continue to be the only region with significant growth—consistent with the generous fertility rates of Nigeria, the DRC, and Angola. In fact, the continent is expected to house 13 of the world’s largest megacities, as its population expands from 1.3 billion to 4.3 billion by 2100.
How Facebook is Using Machine Learning to Map the World Population
Machine learning technology is allowing researchers at Facebook to map the world population in unprecedented detail.
When it comes to knowing where humans around the world actually live, resources come in varying degrees of accuracy and sophistication.
Heavily urbanized and mature economies generally produce a wealth of up-to-date information on population density and granular demographic data. In rural Africa or fast-growing regions in the developing world, tracking methods cannot always keep up, or in some cases may be non-existent.
This is where new maps, produced by researchers at Facebook, come in. Building upon CIESIN’s Gridded Population of the World project, Facebook is using machine learning models on high-resolution satellite imagery to paint a definitive picture of human settlement around the world. Let’s zoom in.
Connecting the Dots
Will all other details stripped away, human settlement can form some interesting patterns. One of the most compelling examples is Egypt, where 95% of the population lives along the Nile River. Below, we can clearly see where people live, and where they don’t.
View the full-resolution version of this map.
While it is possible to use a tool like Google Earth to view nearly any location on the globe, the problem is analyzing the imagery at scale. This is where machine learning comes into play.
Finding the People in the Petabytes
High-resolution imagery of the entire globe takes up about 1.5 petabytes of storage, making the task of classifying the data extremely daunting. It’s only very recently that technology was up to the task of correctly identifying buildings within all those images.
To get the results we see today, researchers used process of elimination to discard locations that couldn’t contain a building, then ranked them based on the likelihood they could contain a building.
Facebook identified structures at scale using a process called weakly supervised learning. After training the model using large batches of photos, then checking over the results, Facebook was able to reach a 99.6% labeling accuracy for positive examples.
Why it Matters
An accurate picture of where people live can be a matter of life and death.
For humanitarian agencies working in Africa, effectively distributing aid or vaccinating populations is still a challenge due to the lack of reliable maps and population density information. Researchers hope that these detailed maps will be used to save lives and improve living conditions in developing regions.
For example, Malawi is one of the world’s least urbanized countries, so finding its 19 million citizens is no easy task for people doing humanitarian work there. These maps clearly show where people live and allow organizations to create accurate population density estimates for specific areas.
Visit the project page for a full explanation and to access the full database of country maps.
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