This annual study provides migration pattern data for the United States by State or by county and are available for inflows (the number of new residents who moved to a State or county and where they migrated from) and outflows (the number of residents who left a State or county and where they moved to). The data include the number of returns filed, number of personal exemptions claimed, total adjusted gross income, and aggregate migration flows at the State level, by the size of adjusted gross income (AGI) and by age of the primary taxpayer. Data are collected and based on year-to-year address changes reported on U.S. Individual Income Tax Returns (Form 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, U.S. Population Migration Data.

The US Migration dataset contains information about the migration patterns of people in the United States between 1995 and 2000.

The IRS publishes migration data for the US population based upon the individual tax returns filed with the IRS, where they track on a year-by-year basis

• where people were coming from - the prior state of residency
• where people moving to - the new state of residency
• number of returns filed - approximate number of households that migrated
• number of exemptions - approximate number of individuals
• the adjusted gross income (AGI) - recorded in thousands of dollars

The raw data published on the IRS website clearly shows patterns of evolution - changing patterns of what is recorded, how it is record, and naming conventions used - making it a challenge to track changes in the underlying data over time. The current dataset attempts to address these shortcomings by normalizing the record layout, standardizing the conventions, and collecting the annual into a single, coherent dataset.

An individual record is laid out with 9 fields

Y1 Y1_STATE_FIPS Y1_STATE_ABBR Y1_STATE_NAME Y2 Y2_STATE_FIPS Y2_STATE_ABBR Y2_STATE_NAME NUM_RETURNS NUM_EXEMPTIONS AGI Here, Y1 refers to the first year (from where the people are migrating) while Y2 refers to the second year (to where the people are migrating). As this is annual data, Y2 should always be the next year after Y1. Associated with each year are three different ways of identifying a state - the name of the state, it's two-letter abbreviaion, and it's FIPS code. Granted, carrying around three IDs per state is redundant; however, the various IDs are useful in different contexts. One thing to note - the IRS data represents migration into and out of the country via the introduction of a fake state, identified by STATE_NAME=FOREIGN, STATE_ABBR=FR, and STATE_FIPS=57.

From any given state, the dataset records migration to 52 destinations

• either not moving, or staying in the same state
• migrating to one of the other 49 states
• migrating to Washington DC
• migrating overseas (i.e., to the FOREIGN state)

Similarly, the dataset represents the migation into any given state as being from one of 52 destinations. Typically, the numbers associated with "staying put" constitute, by far, the largest contingent of tax payers for the given state. The one exception to this description is the FOREIGN state. The dataset does not record "staying put" outside of the country; there is no record for FOREIGN-to-FOREIGN migration. As such, there are 51, not 52, destinations paired with migration to-and-from the FOREIGN state.

Overview of migration-driven growth in Southern states including Texas, Georgia, and North Carolina.

The IRS Statistics of Income Division (SOI), in collaboration with the U.S. Census Bureau, has released migration data for the United States for several decades. These data are an important source of information detailing the movement of individuals from one location to another. SOI bases these data on year-to-year address changes reported on individual income tax returns filed with the IRS. They present migration patterns by State or by county for the entire United States and are available for inflows—the number of new residents who moved to a State or county and where they migrated from, and outflows—the number of residents leaving a State or county and where they went. The data are available for Filing Years 1991 through 2016 and include:

• Number of returns filed, which approximates the number of households that migrated
• Number of personal exemptions claimed, which approximates the number of individuals
• Total adjusted gross income, starting with Filing Year 1995
• Aggregate migration flows at the State level, by the size of adjusted gross income (AGI) and age of the primary taxpayer, starting with Filing Year 2011.

Family trees contain information on individuals such as birth and death places and years, and kinship ties, e.g., parent-child, spouse, and sibling relationships. Such information makes it possible to construct population-scale trees and study population dynamics and migration over many generations and far into the past. Despite the recent advances, existing spatial and temporal abstraction techniques for space-time flow data have limitations due to the lack of knowledge about the effects of temporal partitioning on flow patterns and their visualization. In this study, we extract state-to-state migration patterns over a period between 1789 and 1924 from a set of cleaned, geocoded and connected family trees from Rootsweb.com. We use the child ladder approach, one that captures changes in family locations by comparing birthplaces and birthyears of consecutive siblings. Our study has two major contributions. First, we introduce a methodology to reveal patterns and trends for analyzing and mapping of migration across space and time using a family tree dataset. Specifically, we evaluate a series of temporal partitioning methods to capture how changes in temporal partitioning influence the results of patterns and trends. Second, we visualize longitudinal population mobility in the US using time-series flow maps. This is one of the first studies to uncover dynamic migration patterns on a larger spatial and temporal scale, than the more typical micro studies of individual movement. Our findings are reflective of the migration patterns of European descendants in the U.S., while native Americans, Blacks, Mexican populations are not represented in the data. [KC1]

[KC1]Need to discuss about this more in limitations, and maybe put in in the abstract and/or introduction. Since this is a methodological paper to map migration from trees, I don’t think we need to add this in the title.

How far do people migrate between childhood and young adulthood? Where do they go? How much does one's location during childhood determine the labor markets that one is exposed to in young adulthood?

This project sheds light on these questions using newly constructed and publicly available statistics on the migration patterns of young adults in the United States. Use this resource to discover where people in your hometown moved as young adults.

Researchers at Harvard University and the Census Bureau have linked federal tax filings, Census records, and other government data to track the migration patterns of young US residents. Specifically, for each person born in the US between 1984 and 1992, the researchers compared where they lived at age 16 to where they lived at age 26. The project’s public dataset counts the approximate number who moved to/from each pair of commuting zones — overall and disaggregated by race/ethnicity and parental income level.

cr: https://migrationpatterns.org/

Provides international migration data that will assist the U.S. Census Bureau, other government agencies, and other researchers to improve the quality of international migration estimates and to determine changes in migration patterns that are related to the nations population composition.

Sea level rise in the United States will lead to large scale migration in the future. We propose a framework to examine future climate migration patterns using models of human migration. Our framework requires that we distinguish between historical versus climate driven migration and recognizes how the impacts of climate change can extend beyond the affected area. We apply our framework to simulate how migration, driven by sea level rise, differs from baseline migration patterns. Specifically, we couple a sea level rise model with a data-driven model of human migration and future population projections, creating a generalized joint model of climate driven migration that can be used to simulate population distributions under potential future sea level rise scenarios. The results of our case study suggest that the effects of sea level rise are pervasive, expanding beyond coastal areas via increased migration, and disproportionately affecting some areas of the United States.

The pandemic appears to have accelerated moves from larger urban areas to smaller urban areas.

This study contains county-level net migration estimates, by five-year age cohorts, sex, race, and Hispanic origin, for the intercensal period from 2010 to 2020. This file is part of a series of estimates done for each decade since 1950. Details on how net migration and corresponding net migration rates are calculated are described in the methodology document. In addition, data is available through mapping and charting interfaces at Net Migration Patterns for U.S. Counties.

Migration Dataset- Exploratory Data Analysis This project explores global migration trends using data extracted from UN migration–related sources. The analysis includes data cleaning, handling missing values, detecting outliers, generating descriptive statistics, and creating visualizations aimed at understanding worldwide refugee and migration patterns.

  Dataset Summary

-Source: Kaggle (uploaded by M P Ajith Bharadwaj) -Time period: 1950-2020-Features: 16 numeric + 1 categorical… See the full description on the dataset page: https://huggingface.co/datasets/Mayab2/migration.

Understanding the impacts of the COVID-19 pandemic on domestic migration patterns is crucial for addressing resource needs and migration forecasting. Only a limited number of studies, however, have explored these dynamics through the lens of network analysis. Using Internal Revenue Service (IRS) county-to-county migration data, this article conceptualizes the U.S. domestic migration system as complex networks and employs tools such as backbone extraction, centrality analysis, and community detection to examine the spatiotemporal shifts in the migration landscape before and during the pandemic. Our findings reveal a diversification in migration destinations, yet the overall network structure exhibits stability, with central hubs maintaining their pivotal roles and regional communities showing substantial resilience over time. Disruptions, when observed, were generally regional and modest in magnitude. Further, an analysis of adjusted gross income data within the IRS data sets uncovers a pronounced spatial clustering of migrant wealth, suggesting a deliberate selection of destinations by wealthier migrants. This research offers a novel perspective on understanding domestic migration in the face of external shocks, such as the COVID-19 pandemic, enriching the discourse on migration studies and providing invaluable insights for policy formulation and urban development.

Floods are increasingly frequent and severe due to climate change, thereby impacting migration within the United States. Considering that Black and Brown populations are disproportionately exposed to floods, less likely to receive disaster-related government funds, and vulnerable during subsequent displacement, an examination of differences in migration patterns across racial/ethnic groups is critical. The prevailing conjecture is that after floods, Black and Brown populations will migrate while White ones remain in place. We test this hypothesis by examining the effect of floods on migration across all U.S. county-pairs between 2006-2016 and find that this hypothesis is incorrect: generally, after floods Black populations remain in place and White populations migrate. However, this pattern reverses when the Federal Emergency Management Agency provides financial support. Notably, migration by Hispanic and Asian populations is not significantly affected by floods. These results provide the first evidence of racial disparities in climate migration.

This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.

Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: https://doi.org/10.1038/s41562-023-01689-4

You can explore the data in our online net-migration explorer: https://wdrg.aalto.fi/global-net-migration-explorer/

Short introduction to the data

For the dataset, we collected, gap-filled, and harmonised:

a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and

sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).

These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).

We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.

Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.

List of datasets

Birth and death rates:

raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)

raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)

tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

Net-migration:

raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)

polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)

polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)

polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)

Files to run online net migration explorer

masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (https://wdrg.aalto.fi/global-net-migration-explorer/). The source code of this application is available in https://github.com/vvirkki/net-migration-explorer. Running the application locally requires these two .rds files from this repository.

Metadata

Grids:

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; each band for each year over 2000-2019

Units:

Birth and death rates: births/deaths per 1000 people per year

Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 2000-2019

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Units:

Net-migration: persons per 1000 people per year

The Wenatchee Mountains mule deer herd inhabits a matrix of private and public lands along the eastern slope of the Cascade Range in Chelan and Kittitas Counties in Washington (fig. 24). Historically, the Wenatchee Mountains mule deer herd was separated into two subherds, Chelan and Kittitas; however, recent GPS collar data indicated the mule deer south of U.S. Highway 2 and north of Interstate 90 represent one population. Their high-use winter range extends along the foothills west and south of Wenatchee, Washington and throughout the foothills of the Kittitas Valley near Ellensburg. Their low-use winter range occurs along the foothills west of the Columbia River north of Interstate 90. In the spring, migratory individuals travel west into the Wenatchee Mountains to their summer range, which includes regional wilderness areas. Between 2020 and 2021, collaring efforts focused on the foothills near Wenatchee and in the surrounding foothills near Ellensburg. Collar data analysis indicated the Wenatchee Mountains mule deer population is partially migratory. A high proportion of migratory individuals inhabit the northern winter range of the Wenatchee Mountains, and resident individuals more commonly inhabit the foothills of the Kittitas Valley. In 2022, collaring efforts of mule deer (n=25) in the northern winter range foothills near Wenatchee targeted the higher proportion of the migratory population, to more clearly identify the movement corridors intersecting U.S. Highway 97 near Blewett Pass. The herd has several challenges, including the increasing frequency of large-scale wildfires and residential developments, which continue to degrade and reduce available winter habitat. Disturbance from human recreation on the winter range continues to be a concern. Additionally, U.S. Highway 97 and State Route 970 receive high volumes of traffic in the region and present semipermeable barriers to spring and fall migration. These mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the Wenatchee population in Washington. They were developed from 184 migration sequences collected from a sample size of 59 animals comprising GPS locations collected every 4 hours.

Migration data for the United States are based on year-to-year address changes reported on individual income tax returns filed with the IRS. They present migration patterns by State or by county for the entire United States and are available for inflows—the number of new residents who moved to a State or county and where they migrated from, and outflows—the number of residents leaving a State or county and where they went. The data are available for Filing Years 1991 through 2016 and include:

• Number of returns filed, which approximates the number of households that migrated
• Number of personal exemptions claimed, which approximates the number of individuals
• Total adjusted gross income, starting with Filing Year 1995
• Aggregate migration flows at the State level, by the size of adjusted gross income (AGI) and age of the primary taxpayer, starting with Filing Year 2011.

This Data Brief updates the figures that appeared in “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?” with data for 2023:Q4 for all series. Migration estimates enable us to track which urban neighborhoods and metro areas are returning to their old migration patterns and where the pandemic has permanently shifted migration trends.

United States Naturalization Trends

Naturalization Trends in the US 1999-2017

By Throwback Thursday [source]

About this dataset

The United States Naturalizations 1999-2017 dataset provides comprehensive information on the naturalization trends in the United States over a period of 19 years. It includes data on the year and type of naturalization, as well as the country or region of origin for individuals who were naturalized during this time frame. The dataset offers valuable insights into the overall patterns and shifts in naturalization rates, enabling researchers to analyze and understand the demographic dynamics within the United States. With this dataset, users can explore how factors such as political events, policy changes, and global migration patterns have influenced naturalization trends over time. By examining both new and derivative naturalizations from various countries or regions, researchers can gain a deeper understanding of immigration patterns within specific communities and identify potential factors that contribute to higher rates of citizenship acquisition. Ultimately, this dataset serves as a valuable resource for policymakers, analysts, academics, and anyone interested in studying immigration trends or assessing their impact on American society

How to use the dataset

Understanding the Columns

The dataset consists of several columns that provide valuable information about naturalization trends in the United States from 1999 to 2017. Here's a brief description of each column:

• Year: The year in which the naturalizations took place (numeric).

• Type: The type of naturalization, categorized as either New Naturalizations or Derivative Naturalizations (text).

• Country or Region: The country or region of origin for individuals who were naturalized (text).

Analyzing Yearly Trends

One way you can use this dataset is by analyzing yearly trends in naturalizations. You can group the data by year and explore how many people from different countries or regions became US citizens each year.

For example, you might want to investigate if there are any significant changes in the number of new naturalizations over time or if certain countries show higher rates of derivative naturalizations compared to others.

Comparing Types of Naturalizations

Another interesting analysis could be comparing different types of naturalizations – new and derivative – and examining their patterns over time.

By grouping the data by type and year, you can generate insights into how these categories vary annually and if there are any notable trends between them.

Exploring Country/Region-specific Data

If you're interested in studying specific countries' contribution towards US naturalizations, it's worth exploring data based on country or region.

By filtering the dataset by a particular country or region name, you can gain insight into its citizens' tendencies for migration and becoming US citizens over time.

Visualizing Data for Better Understanding

To visualize this data effectively, consider using charts such as line plots, bar graphs, heatmaps, or even maps (for country/region-specific analysis). Visual representations can help you grasp trends, make comparisons, and communicate your findings more easily.

Drawing Conclusions

By examining this dataset, you can draw conclusions about naturalization trends in the United States from 1999 to 2017 without focusing on specific dates. You may identify patterns that highlight changes in the number of naturalizations by year or uncover interesting insights about countries and their contributions to US naturalizations.

Remember that this dataset provides an overview of naturalization trends; however, it does not include additional factors such as socio-economic conditions or policy changes that may impact these trends. Therefore,

Research Ideas

• Analyzing naturalization trends: This dataset can be used to analyze and understand the trends and patterns of naturalizations in the United States from 1999 to 2017. It can provide insights into how the number of naturalizations has changed over time and identify any significant increases or decreases.
• Identifying countries or regions with high naturalization rates: By analyzing the data, it is possible to identify which countries or regions have higher rates of naturalization in the United States. This information can be useful for studying migration patterns and understanding factors that contribute to higher levels of immigration from certain places.
• Comparing different types of naturalizations: The dataset pro...

Move Reason Move Reason % Wanted new or better housing 13.6% To establish own household 10.4% New job or job transfer 9.7% Other family reason 9.3% For cheaper housing 9.3% For easier commute 6.0% Wanted to own home, not rent 5.9% Change in marital status 5.8% Relationship with unmarried partner 5.4% Wanted better neighborhood 4.9% Other housing reason 4.6% Other reasons 4.0% Attend/leave college 3.5% Health reasons 2.6% To look for work or lost job 1.4% Retired 1.4% Foreclosure or eviction 0.6% Other job-related reason 0.6% Change of climate 0.6% Natural disaster 0.4%