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.

Migration flows are derived from the relationship between the location of current residence in the American Community Survey (ACS) sample and the responses given to the migration question "Where did you live 1 year ago?". There are flow statistics (moved in, moved out, and net moved) between county or minor civil division (MCD) of residence and county, MCD, or world region of residence 1 year ago. Estimates for MCDs are only available for the 12 strong-MCD states, where the MCDs have the same government functions as incorporated places. Migration flows between metropolitan statistical areas are available starting with the 2009-2013 5-year ACS dataset. Flow statistics are available by three or four variables for each dataset starting with the 2006-2010 5-year ACS datasets. The variables change for each dataset and do not repeat in overlapping datasets. In addition to the flow estimates, there are supplemental statistics files that contain migration/geographical mobility estimates (e.g., nonmovers, moved to a different state, moved from abroad) for each county, MCD, or metro area.

A map and ranking of the highest-volume interstate moves such as California to Texas and New York to Florida.

Abstract (en): These migration data come from the Census 2000 long-form questions about residence in 1995 and provide the number of people who moved between counties. There are two files, one for inflows from every county in the United States and another re-sorted by outflows to every county. Each file contains data for all 50 states and the District of Columbia, sorted by FIPS state and county codes. All persons living in housing units in the United States in 2000. self-enumerated questionnaireFor each county in the state, the number of migrants who moved to that county from another county is listed. These files contain records for FIPS state code in 2000, FIPS county code in 2000, county and state name in 2000 (current residence), FIPS state code in 1995, FIPS county code in 1995, county and state name in 1995 (previous residence), and the number of migrants who moved between those two counties (inflow).For each county in the state, the number of migrants who moved away from that county to another county is listed. These files contain records for FIPS state code in 1995, FIPS county code in 1995, county and state name in 1995 (previous residence), FIPS state code in 2000, FIPS county code in 2000, county and state name in 2000 (current residence), and the number of migrants who moved between those two counties (outflow).The data for Puerto Rico are not included in this version of the collection.

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Centre County, PA (DISCONTINUED) (NETMIGNACS042027) from 2009 to 2020 about Centre County, PA; State College; migration; flow; PA; Net; 5-year; and population.

This app shows the inbound and outbound flow of population to and from every state in the U.S., between 2015 and 2016. This is based on tax returns filed through the IRS. Click on any state to see information about population flows. The brightest, thickest lines have the most population moving along that flow line. The circles indicate the total population inbound or outbound. The chart is sorted by distance to the state, and lets you instantly compare the inflow and outflow of population between 2015 and 2016. The visualization was created from the Distributive Flow Lines tool to depict the flow of population in different directions throughout the country. To see your state or other states, click here. The data comes from the Internal Revenue Service (IRS) migration data based on tax stats. According to the U.S. Population Migration Data: Strengths and Limitations, if a state had less than 3 tax returns from another state, the value is suppressed. This is stated within the pop-up for these cases.

Net County-to-County Migration Flow (5-year estimate) for Miami County, OH was -576.00000 Persons in January of 2020, according to the United States Federal Reserve. Historically, Net County-to-County Migration Flow (5-year estimate) for Miami County, OH reached a record high of 99.00000 in January of 2019 and a record low of -1259.00000 in January of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for Net County-to-County Migration Flow (5-year estimate) for Miami County, OH - last updated from the United States Federal Reserve on July of 2025.

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.

Net County-to-County Migration Flow (5-year estimate) for Big Stone County, MN was -43.00000 Persons in January of 2020, according to the United States Federal Reserve. Historically, Net County-to-County Migration Flow (5-year estimate) for Big Stone County, MN reached a record high of 78.00000 in January of 2011 and a record low of -291.00000 in January of 2016. Trading Economics provides the current actual value, an historical data chart and related indicators for Net County-to-County Migration Flow (5-year estimate) for Big Stone County, MN - last updated from the United States Federal Reserve on June of 2025.

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.

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2022, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for King County, WA (DISCONTINUED) (NETMIGNACS053033) from 2009 to 2020 about King County, WA; Seattle; migration; flow; WA; Net; 5-year; and population.

Net County-to-County Migration Flow (5-year estimate) for Wasatch County, UT was 1159.00000 Persons in January of 2020, according to the United States Federal Reserve. Historically, Net County-to-County Migration Flow (5-year estimate) for Wasatch County, UT reached a record high of 1683.00000 in January of 2018 and a record low of -647.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for Net County-to-County Migration Flow (5-year estimate) for Wasatch County, UT - last updated from the United States Federal Reserve on July of 2025.

Data represents migration flow into/out of each county as well as intra-county movers and nonmovers"Counties or equivalent areas in the non-New England States; the District of Columbia; and minor civil divisions in the States of Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, and Connecticut"

Are international migration flows racially biased? Despite widespread consensus that racism and xenophobia affect migration processes, no measure exists to provide systematic evidence on this score. In this research note, I construct such a measure—the migration deviation. Migration deviations are the difference between the observed migration between states, and the flow that we would predict based on a racially blind model that includes a wide variety of political and economic factors. Using this measure, I conduct a descriptive analysis and provide evidence that migrants from majority black states migrate far less than we would expect under a racially blind model. These results pave a new way for scholars to study international racial inequality.

This dataset contains one of the main outputs of a study of internal migration among researchers in Mexico inferred from the affiliation addresses of Scopus publications from 1996-2018. Scopus data is owned and maintained by Elsevier.This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as they provide citations to this data repository (10.6084/m9.figshare.12619016) and the reference articles listed below, and license the new creations under the identical terms. For more details about the study, please refer toMiranda-González, Andrea, Samin Aref, Tom Theile, and Emilio Zagheni. "Scholarly migration within Mexico: Analyzing internal migration among researchers using Scopus longitudinal bibliometric data." EPJ Data Science (2020). https://doi.org/10.1140/epjds/s13688-020-00252-9The dataset is provided in a comma-separated values file (.csv file) and each row represents one movement of one researcher-active scholar from a state (source) to another state (target) in Mexico in a specific year (move_year). The data can be used to produce internal migration flows for the states or possibly other migration estimates. It can also be used as an edge-list for creating a network model of migration events between states (states being the nodes of the network and each movement being represented as a directed edge from source to target).A zip file of annual networks (directed and weighted) in gml format is also provided.

Net County-to-County Migration Flow (5-year estimate) for Delta County, TX was -252.00000 Persons in January of 2020, according to the United States Federal Reserve. Historically, Net County-to-County Migration Flow (5-year estimate) for Delta County, TX reached a record high of 189.00000 in January of 2018 and a record low of -357.00000 in January of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for Net County-to-County Migration Flow (5-year estimate) for Delta County, TX - last updated from the United States Federal Reserve on July of 2025.

Net County-to-County Migration Flow (5-year estimate) for Stewart County, GA was 1103.00000 Persons in January of 2020, according to the United States Federal Reserve. Historically, Net County-to-County Migration Flow (5-year estimate) for Stewart County, GA reached a record high of 1103.00000 in January of 2020 and a record low of -608.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Net County-to-County Migration Flow (5-year estimate) for Stewart County, GA - last updated from the United States Federal Reserve on July of 2025.

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.