In 2022, about 1.4 million veterans were living in Texas - the most out of any state. Florida, California, Pennsylvania, and Virginia rounded out the top five states with the highest veteran population in that year.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana 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: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Access a market-leading database of 18 million verified military veterans, backed by our money-back quality guarantee. Our veteran mailing lists are meticulously updated and verified every month to ensure accuracy. Understanding that every campaign is unique, we provide a comprehensive range of demographic and psychographic filters to help you target the exact veteran audience you need.

Whether you aim to offer benefits, home loans, educational opportunities, or specialized services, our data ensures your message reaches the right audience, enabling you to connect effectively with both active and non-active military members. Discover how our targeted data solutions can enhance your engagement and drive success for your initiatives.

Here are some of the customizable segments you can create with our filters:

• Veteran Ethnicities Available
• Senior Veterans (65+)
• Affluent Veterans
• Veterans with Advanced Degrees
• Veteran's Hobbies
• Disabled Veterans
• Families with two or more veterans in the household

Our military veterans email campaign offers targeted outreach to qualified veteran leads with a guaranteed open rate, ensuring your message reaches a receptive audience. After the campaign, you can opt to receive a list of veterans who opened your email, providing a valuable pool of warm leads for follow-up. If you prefer to manage your own campaign, we also offer highly accurate veteran email lists, complete with unlimited usage rights for ongoing marketing efforts.

Additionally, you can extend your reach by using the same veteran email list for targeted Facebook ads, leveraging the power of multi-channel marketing. For a more tangible approach, our veterans mailing list allows you to engage veterans directly through direct mail, offering an uninterrupted opportunity to capture their attention. To maximize impact, we recommend synchronizing direct mail with a complementary digital ad campaign, enhancing your overall return on investment. With our active military database, you can connect with military personnel both on and off base.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show civilian veteran counts and percentages by US Congress in the Atlanta region. 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 2013-2017). 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. Naming conventions: 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)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes: SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NameCivPop18Plus_e# Civilian population 18 years and over, 2017CivPop18Plus_m# Civilian population 18 years and over, 2017 (MOE)pCivPop18Plus_e% Civilian population 18 years and over, 2017pCivPop18Plus_m% Civilian population 18 years and over, 2017 (MOE)CivVeteran_e# Civilian veterans, 2017CivVeteran_m# Civilian veterans, 2017 (MOE)pCivVeteran_e% Civilian veterans, 2017pCivVeteran_m% Civilian veterans, 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

About this dataset

Context

The dataset tabulates the Veteran town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Veteran town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Veteran town was 3,235, a 0.61% decrease year-by-year from 2022. Previously, in 2022, Veteran town population was 3,255, a decline of 1.30% compared to a population of 3,298 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Veteran town decreased by 41. In this period, the peak population was 3,352 in the year 2011. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Veteran town is shown in this column.
• Year on Year Change: This column displays the change in Veteran town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Veteran town Population by Year. You can refer the same here

This is a simple proportion analysis to determine the number of veterans who may be impacted by food scarcity in the United states by county. The population of veterans in each county (9L_VetPop2016_County) was used with the total population in each county (DataDownload3.18) to determine the proportion of veterans in each county. We assumed that veterans were just as likely as anyone else to be in food scarcity and multiplied the proportion of veterans in each county by the number of low access people in the county to determine the number of food insecure veterans by county. We also used statewide very low food secure percentage as a conservative estimate of the number of veterans affected by food scarcity.This dataset was not created to be a perfect representation of the exact number of food insecure veterans. In fact, it is a very rough calculation. However, this back of the envelope calculation shows that the number of food insecure veterans is likely very high. Using county level food access we find that up to 3 million veterans could be affected by low food access, as a conservative estimate, we use the state level "very low food security percentage" and find that a minimum of 200 thousand veterans are likely food insecure. For calculations see sheet "Calculations" in DataDownload3.18.xlsVeteran Population in counties of the United States.(9L_VetPOP2016_Count.csv)https://va.gov/vetdata/Veteran_Population.aspFood Insecurity By County (DataDownload3.18.xls)https://www.ers.usda.gov/data-products/food-environment-atlas/data-access-and-documentation-downloads/

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

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 2014-2018). 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 a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

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

Naming conventions:

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% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

In 2014, the San Diego Association of Governments applied for and received funding from the National Institute of Justice (NIJ) to conduct a process and impact evaluation of the Veterans Moving Forward (VMF) program that was created by the San Diego County Sheriff's Department in partnership with the San Diego Veterans Administration (VA) in 2013. VMF is a veteran-only housing unit for male inmates who have served in the U.S. military. When the grant was written, experts in the field had noted that the population of veterans returning to the U.S. with numerous mental health issues, including post-traumatic stress disorder (PTSD), traumatic brain injury (TBI), and depression, were increasing and as a result, the number of veterans incarcerated in jails and prisons was also expected to increase. While numerous specialized courts for veterans had been implemented across the country at the time, veteran-specific housing units for those already sentenced to serve time in custody were rarer and no evaluations of these units had been published. Since this evaluation grant was awarded, the number of veteran-only housing units has increased, demonstrating the need for more evaluation information regarding lessons learned. A core goal when creating VMF was to structure an environment for veterans to draw upon the positive aspects of their shared military culture, create a safe place for healing and rehabilitation, and foster positive peer connections. There are several components that separate VMF from traditional housing with the general population that relate to the overall environment, the rehabilitative focus, and initiation of reentry planning as early as possible. These components include the selection of correctional staff with military backgrounds and an emphasis on building on their shared experience and connecting through it; a less restrictive and more welcoming environment that includes murals on the walls and open doors; no segregation of inmates by race/ethnicity; incentives including extended dayroom time and use of a microwave and coffee machine (under supervision); mandatory rehabilitative programming that focuses on criminogenic and other underlying risks and needs or that are quality of life focused, such as yoga, meditation, and art; a VMF Counselor who is located in the unit to provide one-on-one services to clients, as well as provide overall program management on a day-to-day basis; the regular availability of VA staff in the unit, including linkages to staff knowledgeable about benefits and other resources available upon reentry; and the guidance and assistance of a multi-disciplinary team (MDT) to support reentry transition for individuals needing additional assistance. The general criteria for housing in this veteran module includes: (1) not being at a classification level above a four, which requires a maximum level of custody; (2) not having less than 30 days to serve in custody; (3) no state or federal prison holds and/or prison commitments; (4) no fugitive holds; (5) no prior admittance to the psychiatric security unit or a current psychiatric hold; (6) not currently a Post-Release Community Supervision Offender serving a term of flash incarceration; (7) not in custody for a sex-related crime or requirement to register per Penal Code 290; (8) no specialized housing requirements including protective custody, administration segregation, or medical segregation; and (9) no known significant disciplinary incidents.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana 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: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

About this dataset

Context

The dataset tabulates the Veteran town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Veteran town. The dataset can be utilized to understand the population distribution of Veteran town by age. For example, using this dataset, we can identify the largest age group in Veteran town.

Key observations

The largest age group in Veteran, New York was for the group of age Under 5 years years with a population of 405 (12.19%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Veteran, New York was the 20 to 24 years years with a population of 71 (2.14%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Veteran town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Veteran town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Veteran town Population by Age. You can refer the same here

United States - Population Level - Men, Total Veterans, 18 Years and over was 15189.00000 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Men, Total Veterans, 18 Years and over reached a record high of 23422.00000 in January of 2000 and a record low of 15189.00000 in June of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Men, Total Veterans, 18 Years and over - last updated from the United States Federal Reserve on July of 2025.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana 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: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

United States - Population Level - Men, Veterans, Other Service Periods, 18 Years and over was 3126.00000 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Men, Veterans, Other Service Periods, 18 Years and over reached a record high of 5567.00000 in October of 2007 and a record low of 3126.00000 in June of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Men, Veterans, Other Service Periods, 18 Years and over - last updated from the United States Federal Reserve on July of 2025.

United States - Population Level - Men, Veterans, Gulf War Era II, 18 Years and over was 4622.00000 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Men, Veterans, Gulf War Era II, 18 Years and over reached a record high of 4642.00000 in May of 2025 and a record low of 945.00000 in January of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Men, Veterans, Gulf War Era II, 18 Years and over - last updated from the United States Federal Reserve on July of 2025.

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 2017-2021 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:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (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)BeltLine (buffer)BeltLine Study (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 Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within 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)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState 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)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 2017-2021). 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: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

United States - Population Level - Women, Veterans, World War II or Korean War or Vietnam Era, 18 Years and over was 186.00000 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Women, Veterans, World War II or Korean War or Vietnam Era, 18 Years and over reached a record high of 398.00000 in September of 2008 and a record low of 186.00000 in June of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Women, Veterans, World War II or Korean War or Vietnam Era, 18 Years and over - last updated from the United States Federal Reserve on July of 2025.

Release Date: 2016-09-23..Table Name. . Statistics for Veteran Owners of Respondent Employer Firms by Owner's Specific Veteran Characteristics by Sector, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014. ..Release Schedule. . This file was released in September 2016.. ..Key Table Information. . These data are related to all other 2014 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2014 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2014 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. For Characteristics of Business Owners (CBO) data, all estimates are of owners of firms responding to the ASE. That is, estimates are based only on firms providing gender, ethnicity, race, or veteran status; or firms not classifiable by gender, ethnicity, race, and veteran status that returned an ASE online questionnaire with at least one question answered. The ASE online questionnaire provided space for up to four owners to report their characteristics.. CBO data are not representative of all owners of all firms operating in the United States. The data do not represent all business owners in the United States.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The top fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for Veteran Owners of Respondent Employer Firms by Owner's Specific Veteran Characteristics by Sector, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014 contains data on:. . Number of veteran owners of respondent firms with paid employees. Percent of number of veteran owners of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of owners of respondent firms. . Veteran. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Owner's specific veteran characteristics. . Served on active duty military service. Service-disabled. Served on post-9/11 active duty military service. Served on active duty military service in 2014. Served in the National Guard or as a military reservist in 2014. None of the above. Total reporting. Item not reported by veterans. . . . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Gender, ethnicity, race, and veteran status (ASECBO). Years in business (YIBSZFI). Owner's specific veteran characteristics (VETSPECIFIC). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SE1400CSCBO10 table at: https://www2.census.gov/programs-surveys/ase/data/2014/SE1400CSCBO10.zip. ..Contact Information. . To contact the Annual Survey of Entrepreneurs staff:. . Visit the website at https://www.census.gov/programs-surveys/ase.html.. Email general, nonsecure, and unencrypted messages to ewd.annual.survey.of.entrepreneurs@census.gov.. Call 301.763.1546 between 7 a.m. and 5 p.m. (EST), Monday through Friday.. Write to:. U.S. Census Bureau. Annual Survey of Entrepreneurs. 4600 Silver Hill Road. Washington, DC 20233. . . ...Data User Notice posted on June 9, 2017: Census Bureau staff identified a processing error that affects selected data from the 2014 Annual Survey of Entrepreneurs (ASE). As a result, 2014 estimates for the following data item was corrected: Standard error of percent of number of owners of respondent firms with paid employees. This processing error did not affect other categories in this table..[NOTE: Includes firms with payroll at any time during 2014. Employment reflects the number of paid employee...

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 2017-2021 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:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (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)BeltLine (buffer)BeltLine Study (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 Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within 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)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState 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)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 2017-2021). 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: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

The Department of Veterans Affairs (VA) provides healthcare services to its veterans across the USA including territories and possessions. Healthcare services are delivered through 18 geographically divided administrative areas called Veterans Integrated Services Networks (VISN). Each VISN is divided into healthcare areas called Markets and Submarkets. Each Submarket is divided into Sectors and each Sector comprises one or more counties. In 1995 a process was created to coordinate and review the realignment of the Heath Care Networks. The Capital Asset Realignment for Enhanced Services (CARES) process established VISN 'subsets' called Markets, Submarkets and Sectors which, being smaller than VISNs, allowed for more precise analyses for greater access measurement to health care.

The County layer is the base geographic unit of the VISN-Market-Submarket-Sector-County hierarchy. The key attribute in this data set is the FIPS which is defined as a string of 5 characters with unique alphanumeric combinations for each site. The first 2 are the State FIPS code and the next 3 designate the County FIPS code. Example: '01031' is the FIPS for Coffee County, Alabama.

A Sector is a cluster of geographically adjacent counties within a VA Submarket. The process of aggregating counties into sectors uses a combination of automated algorithms and manual inspection of maps. The key attribute in this data set is the SECTOR which is defined as a string of eight characters broken down into four parts in the order of VISN (2-char), Market (1-char), Submarket (1-char), and Sector(1-char) connected by a hyphen. For example, Sector 12-a-3-A indicates VISN 12, Market a, Submarket 3 and Sector A.

Sub-markets reflect a clustering of the enrollee population within a market and are an aggregation of Sectors. The key attribute in this data set is the SUBMARKET which is defined as a string of six characters broken down in three parts in the order of VISN (2-char), Market (1-char), and Submarket (1-char) connected by a hyphen. For example, Submarket 12-a-3 indicates VISN 12, Market a, and Submarket 3.

CARES defines Markets as "an aggregated geographic area having a sufficient population and geographic size to both benefit from the coordination and planning of health care services and to support a full healthcare delivery system (i.e. primary care, mental health care, inpatient care, tertiary care, and long term care)". Each Market is built from Submarkets. The key attribute in this data set is the MARKET which is defined as a string of four characters broken down in two parts in the order of HCN (2-char) and Market (1-char) connected by a hyphen. For example, Market 12-a indicates VISN 12 and Market a.

The key attribute in the VISN data set is defined as a string of two characters from 01-23, excluding 3, 11, 13, 14 and 18; a VISN also has an officially recognized VA title. For example, VISN 06 is the Mid-Atlantic Health Care Network. VISNs can span across neighboring countries to include areas that are not contiguous. For example, VISN 08 includes Florida and Puerto Rico in addition to most of Florida and southern Georgia, and VISN 20 includes Alaska and parts of the northwest conterminous United States. Each VISN is built from Markets, Submarkets, Sectors and Counties derived from Census (2010) County data.

Because VISNs are composed of VHA markets, VISN boundaries align with the outer edges of their constituent markets’ boundaries. Markets cross state borders wherever it is necessary to keep outpatient clinics (e.g. Community-Based Outpatient Clinics(CBOCs)) and their catchment areas in the same market as their parent medical centers. Thus, VISN boundaries also cross state borders. In 2016 senior leadership considered the challenge of conforming VISN boundaries to MyVA Districts, which coincide with state boundaries. It was agreed that VHA would not separate outpatient clinics from their parent medical centers due to added complexity. Many outpatient providers hold clinics at their mother facilities and clinics are on the same health record as their parent facilities. VISN and market maps created by VHA Policy and Planning conform to these principals and are the official maps for VHA VISNs and markets.

While the Planning Systems Support Group (PSSG) develops the feature classes depicting the various VHA geographies, the PSSG does not have the authority to modify or reorganize the boundaries. The boundaries are developed at higher levels of the VHA and passed to the PSSG to be translated into spatial features.

Recent work inspired by graph theory has begun to conceptualize mental disorders as networks of interacting symptoms. Posttraumatic stress disorder (PTSD) symptom networks have been investigated in clinical samples meeting full diagnostic criteria, including military veterans, natural disaster survivors, civilian survivors of war, and child sexual abuse survivors. Despite reliable associations across reported networks, more work is needed to compare central symptoms across trauma types. Additionally, individuals without a diagnosis who still experience symptoms, also referred to as subthreshold cases, have not been explored with network analysis in veterans. A sample of 1,050 Iraq/Afghanistan-era U.S. military veterans (851 males, mean age = 36.3, SD = 9.53) meeting current full-criteria PTSD (n = 912) and subthreshold PTSD (n = 138) were assessed with the Structured Clinical Interview for DSM-IV Disorders (SCID). Combat Exposure Scale (CES) scores were used to group the sample meeting full-criteria into high (n = 639) and low (n = 273) combat exposure subgroups. Networks were estimated using regularized partial correlation models in the R-package qgraph, and robustness tests were performed with bootnet. Frequently co-occurring symptom pairs (strong network connections) emerged between two avoidance symptoms, hypervigilance and startle response, loss of interest and detachment, as well as, detachment and restricted affect. These associations replicate findings reported across PTSD trauma types. A symptom network analysis of PTSD in a veteran population found significantly greater overall connectivity in the full-criteria PTSD group as compared to the subthreshold PTSD group. Additionally, novel findings indicate that the association between intrusive thoughts and irritability is a feature of the symptom network of veterans with high levels of combat exposure. Mean node predictability is high for PTSD symptom networks, averaging 51.5% shared variance. With the tools described here and by others, researchers can help refine diagnostic criteria for PTSD, develop more accurate measures for assessing PTSD, and eventually inform therapies that target symptoms with strong network connections to interrupt interconnected symptom complexes and promote functional recovery.