Jasmine Tilley 12/6/10 UP 206A Final

 How do child poverty, school enrollment, household income, and adult educational attainment in LA County relate to one another?  What is the quality of the schools the poorest children in LA have access to?

 “child” = under the age of 18  “adult” = ages 18 and over, unless otherwise noted  “API” = Academic Performance Index  All data is by census tract

% Children Not Enrolled in School % Children in Poverty

% Adults >25 Without A HS Diploma % Adults in Poverty

% Children Not Enrolled in School % Adults >25 Without A HS Diploma

Census Tracts With the Most Children in Poverty Census Tract With Highest Population

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 Children are more likely to not be enrolled in school when their parents are poor and/or do not have a high school diploma.  The majority of poor children who are enrolled in school are enrolled in schools of very poor quality.

 American Fact Finder: Children in Poverty, School Enrollment, Poverty Status by Age, Educational Attainment, Census Tract data, LA Streets data  All school data retrieved on 12/1/10 from the LA Times at: la/neighborhoods/neighborhood/westlake/sc hools/?q=752+South+Alvarado+Street&lat= &lng= &g=Google+Maps la/neighborhoods/neighborhood/westlake/sc hools/?q=752+South+Alvarado+Street&lat= &lng= &g=Google+Maps

 Inset map - Children in Poverty, Census Tracts with the Most Children in Poverty, Census Tract with Highest Population  Geoprocessing- All maps  Graduated Symbol - Enrollment and API symbology  Attribute subset selections - All maps  Boundary subset selections - % Middle School Aged Children (tracts that intersect with buffer)  Aggregating attribute fields - % Adults Without a HS Diploma, % Middle School Aged Children  Creating indices - All maps with %’s  Original data - Schools  Geocoding - Address locator for schools  Metadata - Schools  Measurement - Buffer for schools slides  Modeling - Layouts showing API and Enrollment varied by grade level of school