Painting a Portrait of U.S. Military Veterans - PowerPoint PPT Presentation

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Painting a Portrait of U.S. Military Veterans
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Painting a Portrait of U.S. Military Veterans

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  1. Painting a Portrait of U.S. Military Veterans Descriptive Analysis of Data from the U.S. Census and the U.S. Department of Veterans Affairs, 2000-2010 Dani Molina Graduate School of Education and Information Studies University of California, Los Angeles

  2. Background on Military Veterans • Over 1.5 million servicemembers have participated during Operation Iraqi Freedom (2003-2011) and/or Operation Enduring Freedom (2001-Present). • Many of them will be expected to transition into civilian life, becoming veterans. • Currently, there are no descriptive or empirical studies that investigate the characteristics and needs of veterans. • What economic, health, educational, and employment features can we learn about veterans?

  3. GIS Skills Utilized • Point graduated symbol • Aggregating attribute fields • Creating indices • Attribute sub-sets selections • Boundary sub-sets selections • Geoprocessing

  4. Veteran Population Decreasing

  5. Educational Beneficiaries Increasing

  6. Educational Attainment for Veterans and Nonveterans Veteran Educational Attainment by Regions

  7. Median Income for Veterans and Nonveterans

  8. Unemployment Status for Veterans and Nonveterans

  9. Disability Status for Veterans and Nonveterans

  10. Main Findings • Net loss of veterans overall considering the wars in Iraq and Afghanistan. • The number of veterans aspiring to earn a college or university degree has been steadily increasing since 2000. • Veterans are less likely to be unemployed and more likely to have higher median incomes (per household) and baccalaureate degrees than nonveterans. • However, veterans are significantly more likely to have a disability compared to nonveterans.

  11. Future Inquiries and Research • What services and/or programs, if any, might veterans need to transition into civilian life? • Disparate Impact Analysis (DIA) given race, age, sex, income, and other factors. • Focus on a specific location, such as Los Angeles, and explore associations between available data.

  12. Questions/Comments