From 1820 to 2013, 79 million people obtained lawful permanent resident status in the United States. This month’s Map of the Month visualizes all of them based on their prior country of residence. The brightness of a country corresponds to its total migration to the U.S. at the given time. 1 dot = 10,000 people.Source: Metrocosm - Here's Everyone Who's Immigrated to the United States Since 1820 (includes animation showing immigration sources over time) - https://metrocosm.com/animated-immigration-map

These maps show LPRs and Naturalized Citizens in the United States for various years by state, class of admission and region/country of birth.

Author: A Myers, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 6Resource type: lessonSubject topic(s): migration, mapsRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 23. The end of the Cold War, shifting geopolitical dynamics, the intensification of the global economy and rapidly changing technologies have given renewed urgency to debates about the United States' identity, values and role in the world. (The U.S. in a New Global Age 1980-present)

Standard 5. The characteristics, distribution and migration of human populations on the earth's surface influence human systems (cultural, economic and political systems).

Standard 14. Globalization, the spread of capitalism and the end of the Cold War have shaped a contemporary world still characterized by rapid technological change, dramatic increases in global population and economic growth coupled with persistent economic and social disparities and cultural conflict. (The New Global Era 1989 to Present)Objectives: Students will be able to:

1. Locate and map immigrant settlement in Minnesota.
2. Summarize Minnesota immigrant information.
3. Describe the influence of push/pull factors on immigration to Minnesota.
4. Compare and contrast historic and contemporary immigrant groups in Minnesota. Summary: This lesson examines past and present immigrant groups in Minnesota and reasons for leaving their homes. In addition, students will examine the pull factors that attract immigrants to our state.

A web mapping application displaying net migration at the county level for the United States over ten years (2011-2020). Data for this map was sourced from the Internal Revenue Service.

Users can obtain demographic characteristics of the foreign-born population in each state. Topics include: language, education, income, and poverty. Background The American Community Survey and Census Data on the Foreign-Born interactive map was created by the Migration Policy Institute using Census data. This website provides information pertaining to the immigrant population in the United States. Topics include: demographics, language, education, income and poverty of the foreign-born population. User Functionality Users can click on states to generate fact sheets about the demographic, social, language, educ ation, workforce, income, and poverty characteristics of the population in each state. Data can be downloaded into SAS statistical software. Users can view demographic information by race/ethnicity, Hispanic origin, place of origin, citizenship status, sex/gender, and marital status. Data Notes Data are derived from the 1990 and 2000 Decennial Censuses and the 2007 American Community Surveys (ACS). Information is available on national and state levels. The website does not indicate when the data will be updated.

The whooping crane (Grus americana) is a listed endangered species in North America, protected under federal legislation in the United States and Canada. The only self-sustaining and wild population of Whooping Cranes nests at and near Wood Buffalo National Park near the provincial border of Northwest Territories and Alberta, Canada. Birds from this population migrate through the Great Plains of North America and winter along the Gulf Coast of Texas at Aransas National Wildlife Refuge and surrounding lands. These data represent migration corridors and precision estimates for this population that can be used for conservation planning activities, including targeting conservation, mitigation, and recovery actions and assessing threats.

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.

This layer shows children by nativity of parents by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of children who are in immigrant families (children who are foreign born or live with at least one parent who is foreign born). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B05009Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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.

Our model is a full-annual-cycle population model {hostetler2015full} that tracks groups of bat surviving through four seasons: breeding season/summer, fall migration, non-breeding/winter, and spring migration. Our state variables are groups of bats that use a specific maternity colony/breeding site and hibernaculum/non-breeding site. Bats are also accounted for by life stages (juveniles/first-year breeders versus adults) and seasonal habitats (breeding versus non-breeding) during each year, This leads to four states variable (here depicted in vector notation): the population of juveniles during the non-breeding season, the population of adults during the non-breeding season, the population of juveniles during the breeding season, and the population of adults during the breeding season, Each vector's elements depict a specific migratory pathway, e.g., is comprised of elements, {non-breeding sites}, {breeding sites}The variables may be summed by either breeding site or non-breeding site to calculate the total population using a specific geographic location. Within our code, we account for this using an index column for breeding sites and an index column for non-breeding sides within the data table. Our choice of state variables caused the time step (i.e. (t)) to be 1 year. However, we recorded the population of each group during the breeding and non-breeding season as an artifact of our state-variable choice. We choose these state variables partially for their biological information and partially to simplify programming. We ran our simulation for 30 years because the USFWS currently issues Indiana Bat take permits for 30 years. Our model covers the range of the Indiana Bat, which is approximately the eastern half of the contiguous United States (Figure \ref{fig:BatInput}). The boundaries of our range was based upon the United States boundary, the NatureServe Range map, and observations of the species. The maximum migration distance was 500-km, which was based upon field observations reported in the literature \citep{gardner2002seasonal, winhold2006aspects}. The landscape was covered with approximately 33,000, 6475-ha grid cells and the grid size was based upon management considerations. The U.S.~Fish and Wildlife Service considers a 2.5 mile radius around a known maternity colony to be its summer habitat range and all of the hibernaculum within a 2.5 miles radius to be a single management unit. Hence the choice of 5-by-5 square grids (25 miles(^2) or 6475 ha). Each group of bats within the model has a summer and winter grid cell as well as a pathway connecting the cells. It is possible for a group to be in the cell for both seasons, but improbable for females (which we modeled). The straight line between summer and winter cells were buffered with different distances (1-km, 2-km, 10-km, 20-km, 100-km, and 200-km) as part of the turbine sensitivity and uncertainty analysis. We dropped the largest two buffer sizes during the model development processes because they were biologically unrealistic and including them caused all populations to go extinct all of the time. Note a 1-km buffer would be a 2-km wide path. An example of two pathways are included in Figure \ref{fig:BatPath}. The buffers accounts for bats not migrating in a straight line. If we had precise locations for all summer maternity colonies, other approaches such as Circuitscape \citep{hanks2013circuit} could have been used to model migration routes and this would have reduced migration uncertainty.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

Author: N Daubner, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): migration, historyRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems). Objectives: Students will be able to:

1. Investigate and present reasons why Africans/African-Americans migrated to or within the United States and whether the migrations were voluntary or forced migrations.

2. Use maps to identify where and when migrations occurred in the U.S.

3. Analyze and explain the impact that their assigned migration had or is having on the U.S.

4. Explain differences and similarities between chosen migrations in an essay.Summary: Using a variety of resources, students will analyze statistics, maps, and selected readings and draw comparisons between their assigned migration period and other African-American migrations in United States history. The students will present their findings and write an essay describing similarities and differences between their assigned migration periods with other migration periods in United States history.

The Colockum elk herd inhabits a mix of public and private lands northeast of Ellensburg between Blewett Pass of the Cascade Range and west of the Columbia River (fig. 35). The population ranges between 4,000 and 5,000 animals and is partially migratory, with individuals displaying a mix of resident (63 percent of analyzed individuals) and migratory (34 percent of analyzed individuals) behaviors. During winter, many elk inhabit grassland, sagebrush, antelope bitterbrush, and ponderosa pine habitats in the Whiskey Dick, Quilomene, and Colockum Wildlife Areas and the eastern reaches of the Naneum State Forest. As spring green up of vegetation nears, migratory elk travel northwest toward summer ranges in the Wenatchee Mountains, north of Ellensburg. Resident elk inhabit the same areas as wintering migratory elk. Agricultural producers in the eastern Kittitas Valley often experience conflicts with elk consuming hay in fields. Additional concerns for the herd include semipermeable barriers along Interstate 90 and U.S. Highway 97 and disturbance from human recreation on the winter range. These mapping layers show the location of the Migration routes for elk (Cervus canadensis) in the Colockum population in Washington. They were developed from 89 migration sequences collected from a sample size of 35 animals comprising GPS locations collected every 3 hours.

Author: P Ofstedal, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): migration, population, mapsRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 3. Places have physical characteristics (such as climate, topography and vegetation) and human characteristics (such as culture, population, political and economic systems).

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems).Objectives: Students will be able to:

1. Analyze migration patterns into and out of their county and explain why these patterns exist.
2. Analyze the age of local in- and out-migrants and explain why these patterns exist.Summary: Students will investigate current in-migration and out-migration patterns in their county through two map websites.

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

The majority of landbirds breeding in eastern North America migrate through the Gulf of America (Gulf of Mexico; hereafter Gulf) region during spring and fall. Rarely do migratory landbirds make nonstop flights between breeding and non-breeding areas, rather they stopover in habitats (a.k.a. stopover sites) en route to rest and refuel. Forested habitats in the northern Gulf region provide the last possible stopover for fall migrants making a trans-Gulf flight south, and the first possible landfall for birds returning north in spring. Forested stopover sites also provide resources to millions of circum-Gulf migrants, yet the quality and quantity of these sites have both decreased over time. Thus, land managers and conservation planners have a critical need for data on the quality and quantity of available habitat in relation to where peak numbers of birds consistently stop to rest and forage. We developed a spatially explicit bioenergetics model to determine whether sufficient foo ...

1950 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1950. In 2002, NOAA published digitized shorelines for T-sheet (T-9393), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted.

Annual seasonal migration is one of the main characteristics of pastoralism. However, large-scale studies focusing on mapping seasonal migration patterns using advanced spatial analysis tools like the geographic information system (GIS), hitherto remain meager in India. The lack of such studies has many implications for holistically understanding pastoralism in India. The few spatial analysis studies conducted in the Himalayan region of India found a lack of amenities and conflict with large-scale state-promoted plantations under climate change-related projects. Similar studies have been absent in the country's Deccan Plateau region, which is home to a significant number of pastoralist communities and livestock populations. In this background, an exploratory study was conducted to map the seasonal migration routes of pastoralist communities in the Deccan Plateau region adopting the Ethnographic Geographic Information System Technique (EGIST). The objective of the present study is to digitally map the seasonal migration routes of the pastoralists and document the issues and challenges (if any), along the seasonal migration routes in the study area. Seasonal migration routes of seven villages from Andhra Pradesh and Telangana states were mapped using EGIST and found that pastoralists of the study area practice both short and long-seasonal migration in sync with the monsoon and local cropping season. Pastoralists of Telangana were found to migrate to the neighboring state of Andhra Pradesh (AP) during long-distance migration. However, pastoralists of AP predominantly move within the state. A few major challenges faced by pastoralists during their seasonal migration in the study area includes – labour shortages, disease outbreaks and conflict with the forest department personnel for accessing the traditional grazing lands located inside the Amarabad and Nagarjunsagar-Srisailam Tiger Reserves of Nallamala forest of AP and Telangana states of India.

[This is an archived copy of a dataset originally hosted at https://www.fisheries.noaa.gov/inport/item/55958 and https://coastalimagery.blob.core.windows.net/ccap-landcover/CCAP_bulk_download/Sea_Level_Rise_Wetland_Impacts/index.html. Dataset captured in July 2025 by UCSB Library Research Data Services.] These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://coast.noaa.gov/slr.

This metadata record describes the Marsh Migration data displayed in the SLR Viewer. These data represent the potential distribution of each wetland type based on their elevation and how frequently they may be inundated under potential future SLR scenarios, from 0 to 10ft of SLR. As sea level rises, higher elevations will become more frequently inundated, allowing for marsh migration landward. At the same time, some lower-lying areas will be so often inundated that the marshes will no longer be able to thrive, becoming lost to open water. These data are based on the assumption that specific wetland types exist within an established tidal elevation range, based on an accepted understanding of what types of vegetation can exist given varying frequency and time of inundation, as well as salinity impacts from such inundation.

The data were created using the NOAA OCM Coastal Change Analysis Program (CCAP) land cover data, the SLR Viewer's digital elevation models, and NOAA VDatum tidal surfaces.

The data are available in 0.5ft increments of net sea level change, from 0 to 10ft. To determine the appropriate level, the user must identify a SLR scenario and an applicable accretion rate for the area of interest. The easiest way to do this is to go into the SLR Viewer's Marsh Migration tab; select a location, SLR scenario, and timeframe; and identify the closest available 0.5ft increment to what the viewer shows. For more information, see the tutorial at https://coast.noaa.gov/elearning/marshmigration/.

Data are available for download at https://coast.noaa.gov/htdata/raster1/landcover/bulkdownload/slr_wetland/.

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://coast.noaa.gov/slr.

This metadata record describes the Marsh Migration data displayed in the SLR Viewer. These data represent the potential distribution of each wetland type based on their elevation and how frequently they may be inundated under potential future SLR scenarios, from 0 to 10ft of SLR. As sea level rises, higher elevations will become more frequently inundated, allowing for marsh migration landward. At the same time, some lower-lying areas will be so often inundated that the marshes will no longer be able to thrive, becoming lost to open water. These data are based on the assumption that specific wetland types exist within an established tidal elevation range, based on an accepted understanding of what types of vegetation can exist given varying frequency and time of inundation, as well as salinity impacts from such inundation.

The data were created using the NOAA OCM Coastal Change Analysis Program (CCAP) land cover data, the SLR Viewer's digital elevation models, and NOAA VDatum tidal surfaces.

The data are available in 0.5ft increments of net sea level change, from 0 to 10ft. To determine the appropriate level, the user must identify a SLR scenario and an applicable accretion rate for the area of interest. The easiest way to do this is to go into the SLR Viewer's Marsh Migration tab; select a location, SLR scenario, and timeframe; and identify the closest available 0.5ft increment to what the viewer shows. For more information, see the tutorial at https://coast.noaa.gov/elearning/marshmigration/.

Data are available for download at https://coast.noaa.gov/htdata/raster1/landcover/bulkdownload/slr_wetland/.