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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.

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Presentation on theme: "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."— Presentation transcript:

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, Dani Molina Graduate School of Education and Information Studies University of California, Los Angeles

2 Over 1.5 million servicemembers have participated during Operation Iraqi Freedom ( ) 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 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 Veteran Educational Attainment by Regions Educational Attainment for Veterans and Nonveterans

7 Median Income for Veterans and Nonveterans

8 Unemployment Status for Veterans and Nonveterans

9 Disability Status for Veterans and Nonveterans

10 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 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 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.

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