According to figures recently released by the United States Census, America’s largest metro areas are currently gaining population at impressive rates. The growth in these areas is in fact driving much of the population growth across the nation. Upon closer examination of the data, this growth is the result of two very different migrations – one coming from the location choices of Americans themselves, the other shaped by where new immigrants from outside the United States are heading.While many metro areas are attracting a net-inflow of migrants from other parts of the country, in several of the largest metros – New York, Los Angeles., and Miami, especially – there is actually a net outflow of Americans to the rest of the country. Immigration is driving population growth in these places. Sunbelt metros like Houston, Dallas, and Phoenix, and knowledge hubs like Austin, Seattle, San Francisco, and the District of Columbia are gaining much more from domestic migration.This map charts overall or net migration – a combination of domestic and international migration. Most large metros, those with at least a million residents, had more people coming in than leaving. The metros with the highest levels of population growth due to migration are a mix of knowledge-based economies and Sunbelt metros, including Houston, Dallas, Miami, District of Columbia, San Francisco, Seattle, and Austin. Eleven large metros, nearly all in or near the Rustbelt, had a net outflow of migrants, including Chicago, Detroit, Memphis, Philadelphia, and Saint Louis.Source: Atlantic Cities

Net migration for the United States was 4774029.00000 People in January of 2017, according to the United States Federal Reserve. Historically, Net migration for the United States reached a record high of 8859954.00000 in January of 1997 and a record low of 1556054.00000 in January of 1967. Trading Economics provides the current actual value, an historical data chart and related indicators for Net migration for the United States - last updated from the United States Federal Reserve on September of 2025.

Migration in Europe in 2024 marks a return to normality after the extreme disruptions experienced in 2022. While in 2022 ******* saw the largest negative net migration balance, with almost * million of its citizens fleeing the eastern European country in the aftermath of Russia's invasion, in 2024 it is in fact the country with the largest positive net migration balance. Over **** million Ukrainians have returned to their home country from abroad, leading Poland, Romania, and Hungary to have large net migration deficits, as they were key recipient countries for Ukrainians in 2022. The other countries which experienced large positive net migration balances in 2023 are all in Western Europe, as the UK, the Netherlands, France, Italy, and Spain all remain popular destinations for migrants.

Urban Displacement Project’s (UDP) Estimated Displacement Risk (EDR) model for California identifies varying levels of displacement risk for low-income renter households in all census tracts in the state from 2015 to 2019(1). The model uses machine learning to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP defines displacement risk as a census tract with characteristics which, according to the model, are strongly correlated with more low-income population loss than gain. In other words, the model estimates that more low-income households are leaving these neighborhoods than moving in.This map is a conservative estimate of low-income loss and should be considered a tool to help identify housing vulnerability. Displacement may occur because of either investment, disinvestment, or disaster-driven forces. Because this risk assessment does not identify the causes of displacement, UDP does not recommend that the tool be used to assess vulnerability to investment such as new housing construction or infrastructure improvements. HCD recommends combining this map with on-the-ground accounts of displacement, as well as other related data such as overcrowding, cost burden, and income diversity to achieve a full understanding of displacement risk.If you see a tract or area that does not seem right, please fill out this form to help UDP ground-truth the method and improve their model.How should I read the displacement map layers?The AFFH Data Viewer includes three separate displacement layers that were generated by the EDR model. The “50-80% AMI” layer shows the level of displacement risk for low-income (LI) households specifically. Since UDP has reason to believe that the data may not accurately capture extremely low-income (ELI) households due to the difficulty in counting this population, UDP combined ELI and very low-income (VLI) household predictions into one group—the “0-50% AMI” layer—by opting for the more “extreme” displacement scenario (e.g., if a tract was categorized as “Elevated” for VLI households but “Extreme” for ELI households, UDP assigned the tract to the “Extreme” category for the 0-50% layer). For these two layers, tracts are assigned to one of the following categories, with darker red colors representing higher displacement risk and lighter orange colors representing less risk:• Low Data Quality: the tract has less than 500 total households and/or the census margins of error were greater than 15% of the estimate (shaded gray).• Lower Displacement Risk: the model estimates that the loss of low-income households is less than the gain in low-income households. However, some of these areas may have small pockets of displacement within their boundaries. • At Risk of Displacement: the model estimates there is potential displacement or risk of displacement of the given population in these tracts.• Elevated Displacement: the model estimates there is a small amount of displacement (e.g., 10%) of the given population.• High Displacement: the model estimates there is a relatively high amount of displacement (e.g., 20%) of the given population.• Extreme Displacement: the model estimates there is an extreme level of displacement (e.g., greater than 20%) of the given population. The “Overall Displacement” layer shows the number of income groups experiencing any displacement risk. For example, in the dark red tracts (“2 income groups”), the model estimates displacement (Elevated, High, or Extreme) for both of the two income groups. In the light orange tracts categorized as “At Risk of Displacement”, one or all three income groups had to have been categorized as “At Risk of Displacement”. Light yellow tracts in the “Overall Displacement” layer are not experiencing UDP’s definition of displacement according to the model. Some of these yellow tracts may be majority low-income experiencing small to significant growth in this population while in other cases they may be high-income and exclusive (and therefore have few low-income residents to begin with). One major limitation to the model is that the migration data UDP uses likely does not capture some vulnerable populations, such as undocumented households. This means that some yellow tracts may be experiencing high rates of displacement among these types of households. MethodologyThe EDR is a first-of-its-kind model that uses machine learning and household level data to predict displacement. To create the EDR, UDP first joined household-level data from Data Axle (formerly Infogroup) with tract-level data from the 2014 and 2019 5-year American Community Survey; Affirmatively Furthering Fair Housing (AFFH) data from various sources compiled by California Housing and Community Development; Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data; and the Environmental Protection Agency’s Smart Location Database.UDP then used a machine learning model to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP modeled displacement risk as the net migration rate of three separate renter households income categories: extremely low-income (ELI), very low-income (VLI), and low-income (LI). These households have incomes between 0-30% of the Area Median Income (AMI), 30-50% AMI, and 50-80% AMI, respectively. Tracts that have a predicted net loss within these groups are considered to experience displacement in three degrees: elevated, high, and extreme. UDP also includes a “At Risk of Displacement” category in tracts that might be experiencing displacement.What are the main limitations of this map?1. Because the map uses 2019 data, it does not reflect more recent trends. The pandemic, which started in 2020, has exacerbated income inequality and increased housing costs, meaning that UDP’s map likely underestimates current displacement risk throughout the state.2. The model examines displacement risk for renters only, and does not account for the fact that many homeowners are also facing housing and gentrification pressures. As a result, the map generally only highlights areas with relatively high renter populations, and neighborhoods with higher homeownership rates that are known to be experiencing gentrification and displacement are not as prominent as one might expect.3. The model does not incorporate data on new housing construction or infrastructure projects. The map therefore does not capture the potential impacts of these developments on displacement risk; it only accounts for other characteristics such as demographics and some features of the built environment. Two of UDP’s other studies—on new housing construction and green infrastructure—explore the relationships between these factors and displacement.Variable ImportanceFigures 1, 2, and 3 show the most important variables for each of the three models—ELI, VLI, and LI. The horizontal bars show the importance of each variable in predicting displacement for the respective group. All three models share a similar order of variable importance with median rent, percent non-white, rent gap (i.e., rental market pressure calculated using the difference between nearby and local rents), percent renters, percent high-income households, and percent of low-income households driving much of the displacement estimation. Other important variables include building types as well as economic and socio-demographic characteristics. For a full list of the variables included in the final models, ranked by descending order of importance, and their definitions see all three tabs of this spreadsheet. “Importance” is defined in two ways: 1. % Inclusion: The average proportion of times this variable was included in the model’s decision tree as the most important or driving factor.2. MeanRank: The average rank of importance for each variable across the numerous model runs where higher numbers mean higher ranking. Figures 1 through 3 below show each of the model variable rankings ordered by importance. The red lines represent Jenks Breaks, which are designed to sort values into their most “natural” clusters. Variable importance for each model shows a substantial drop-off after about 10 variables, meaning a relatively small number of variables account for a large amount of the predictive power in UDP’s displacement model.Figure 1. Variable Importance for Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Figure 2. Variable Importance for Very Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet. Figure 3. Variable Importance for Extremely Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Source: Chapple, K., & Thomas, T., and Zuk, M. (2022). Urban Displacement Project website. Berkeley, CA: Urban Displacement Project.(1) UDP used this time-frame because (a) the 2020 census had a large non-response rate and it implemented a new statistical modification that obscures and misrepresents racial and economic characteristics at the census tract level and (b) pandemic mobility trends are still in flux and UDP believes 2019 is more representative of “normal” or non-pandemic displacement trends.

In 2024, the net migration rate in France reached *******. In recent years Europe and France have seen more people arrive than depart. The net migration rate is the difference between the number of immigrants (people coming into an area) and the number of emigrants (people leaving an area) throughout the year. France's highest net migration rate was reached in 2018 when it amounted to *******. Armed conflicts and economic migration are some of the reasons for immigration in Europe. The refugee crisis Studies have shown that there were ******* immigrant arrivals in France in 2022, which has risen since 2014. The migrant crisis, which began in 2015 in Europe, had an impact on the migration entry flows not only in France but in all European countries. The number of illegal border crossings to the EU over the Eastern Mediterranean route reached a record number of ******* crossings in 2015. Immigration in France Since the middle of the 19th century, France has attracted immigrants, first from European countries (like Poland, Spain, and Italy), and then from the former French colonies. In 2023, there were approximately *** million people foreign-born in France. Most of them were living in the Ile-de-France region, which contains Paris, and in Provence-Alpes-Côte d’Azur in the Southeastern part of the country. In 2022, the majority of immigrants arriving in France were from Africa and Europe.

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

We operated a bird banding station on the Point Loma peninsula in western San Diego County, California, during spring and summer from 2011 to 2015. The station was established in 2010 as part of a long-term monitoring program for neotropical migratory birds during spring migration and for breeding birds as part of the Monitoring Avian Productivity and Survivorship (MAPS) program. During spring migration (April and May), 2011–15, we captured 1,760 individual birds of 54 species, 91 percent (1,595) of which were newly banded, fewer than 1 percent (3) of which were recaptures that were banded in previous years, and 9 percent (143 hummingbirds, 2 hawks, and 17 other birds) of which we released unbanded. We observed an additional 22 species that were not captured. Thirty-four individuals were captured more than once. Bird capture rate averaged 0.49 ± 0.07 captures per net-hour (range 0.41–0.56). Species richness per day averaged 6.87 ± 0.33. Cardellina pusilla (Wilson’s warbler) was the most abundant spring migrant captured, followed by Empidonax difficilis (Pacific-slope flycatcher), Vireo gilvus (warbling vireo), Zonotrichia leucophrys (white-crowned sparrow), and Selasphorus rufus (rufous hummingbird). Captures of white-crowned sparrow decreased, and captures of Pacific-slope flycatcher increased, over the 5 years of our study. Fifty-six percent of known-sex individuals were male and 44 percent were female. The peak number of new species arriving per day ranged from April 1 (2013-six species) to April 16 (2012-five species). These data support the following publication: Lynn, Suellen, Madden, M.C., and Kus, B.E., 2017, Monitoring breeding and migration of neotropical migratory birds at Point Loma, San Diego County, California, 5-year summary, 2011–15: U.S. Geological Survey Open-File Report 2017-1042, 119 p., https://doi.org/10.3133/ofr20171042 and data can be found by navigating to USGS Bird Banding Laboratory: https://www.pwrc.usgs.gov/bbl/ and Institute for Bird Populations: http://www.birdpop.org/pages/maps.php.

In 2024, approximately 948,000 million people migrated to the United Kingdom, while 517,000 people migrated from the UK, resulting in a net migration figure of 431,000. There have consistently been more people migrating to the United Kingdom than leaving it since 1993 when the net migration figure was negative 1,000. Although migration from the European Union has declined since the Brexit vote of 2016, migration from non-EU countries accelerated rapidly from 2021 onwards. In the year to June 2023, 968,000 people from non-EU countries migrated to the UK, compared with 129,000 from EU member states. Immigration and the 2024 election Since late 2022, immigration, along with the economy and healthcare, has consistently been seen by UK voters as one of the top issues facing the country. Despite a pledge to deter irregular migration via small boats, and controversial plans to send asylum applicants to Rwanda while their claims are being processed, Rishi Sunak's Conservative government lost the trust of the public on this issue. On the eve of the last election, 20 percent of Britons thought the Labour Party would be the best party to handle immigration, compared with 13 percent who thought the Conservatives would handle it better. Sunak and the Conservatives went on to lose this election, suffering their worst defeat in modern elections. Historical context of migration The first humans who arrived in the British Isles, were followed by acts of conquest and settlement from Romans, Anglo-Saxons, Danes, and Normans. In the early modern period, there were also significant waves of migration from people fleeing religious or political persecution, such as the French Huguenots. More recently, large numbers of people also left Britain. Between 1820 and 1957, for example, around 4.5 million people migrated from Britain to America. After World War Two, immigration from Britain's colonies and former colonies was encouraged to meet labour demands. A key group that migrated from the Caribbean between the late 1940s and early 1970s became known as the Windrush generation, named after one of the ships that brought the arrivals to Britain.

Canada’s appeal as an immigration destination has been increasing over the past two decades, with a total of 464,265 people immigrating to the country in 2024. This figure is an increase from 2000-2001, when approximately 252,527 immigrants came to Canada. Immigration to the Great White North Between July 1, 2022 and June 30, 2023, there were an estimated 199,297 immigrants to Ontario, making it the most popular immigration destination out of any province. While the number of immigrants has been increasing over the years, in 2024 over half of surveyed Canadians believed that there were too many immigrants in the country. However, in 2017, the Canadian government announced its aim to significantly increase the number of permanent residents to Canada in order to combat an aging workforce and the decline of working-age adults. Profiles of immigrants to Canada The gender of immigrants to Canada in 2023 was just about an even split, with 234,279 male immigrants and 234,538 female immigrants. In addition, most foreign-born individuals in Canada came from India, followed by China and the Philippines. The United States was the fifth most common origin country for foreign-born residents in Canada.