Understanding disparitiesA deep dive into the American Community Survey

Sean Green, Ph.D.Data Solutions StrategistCity of Seattle

Ways of capturing socioeconomic data

• Product registration surveys• Online behavior and purchase history• Phone surveys• Door-to-door surveys• Workplace/school surveys• The Census and American Community Survey

The United States Census

• The decennial census is mandated by Article I, Section 2 of the US Constitution• Is the most comprehensive source of population demographic information• Is used to apportion representation, and as such, requires a count of

individuals and households• From 1940 through 2000 a subset of Americans received a long form

which contained detailed socioeconomic questions• After 2000 the American Community Survey (ACS) was developed, and

eventually replaced the long form as a survey that could be used to provide estimates between decennial censuses

Three levels of detailMost comprehensive, fewer questions

Less comprehensive, more questions, more detail

Decennial Census

American CommunitySurvey

ACS PUMS

What you get with each

Decennial Census American Community Survey

Public-Use Microdata Sample

• Comprehensive counts• Total population• Estimates for

proportions of demographic groups

• Only available once a decade

• Sampling for smaller groups

• Questions regarding household income, educational attainment, and employment status

• Available yearly for areas of sufficient size

• Same questions as ACS• Anonymized individual

responses for custom tables

• Available yearly for areas of sufficient size

• Higher uncertainty bounds for small areas and groups

2010 Census Tracts vs. 2015 PUMAs

2010 Census Tracts vs. 2015 Zip Code Areas

What the data can tell us about disparities

Decennial Census American Community Survey

Public-Use Microdata Sample

• Proportionality of resources spent and services offered by geography

• How Seattle’s population compares to the population in other cities

• Socioeconomic data aggregated by geographic unit

• Which groups are faring better or worse over time in terms of employment, wages, and ease of transportation

• Socioeconomic data by household

• Individual and population-level factors that are correlated with disparities

• How have groups fared in our city by geography

Poverty rates from ACS

Source: American Community Survey 2007-2010 data

2007 2008 2009 20100%

5%

10%

15%

20%

25%

30%

35%

40%

32.9%

21.6% 21.5%

35.0%

17.4%

14.3% 14.6% 14.8%

9.4% 9.0% 8.6%10.9%

18.6%

22.7%

13.7%

25.8%

15.4%14.0%

9.8%

13.7%

Seattle Poverty Rates by Race

African American Asian Caucasian Hispanic Other

Educational Attainment from PUMs

Other

Hispanic

Caucasian

Asian/Pacific

African American

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%

12.3%

18.7%

4.9%

3.3%

13.2%

18.8%

23.6%

12.6%

7.2%

31.7%

47.4%

33.0%

41.4%

50.0%

37.9%

1.6%

2.6%

5.5%

12.0%

1.3%

17.0%

22.1%

34.5%

23.2%

11.1%

2.8%

0.0%

1.1%

4.8%

4.8%

Educational Attainment by race for Seattle Youths Aged 18-24

Less than HS

HS Diploma or Equivalent

Some College

Associate's Degree

Bachelor's Degree

MA or Higher

Source: American Community Survey PUMS 2009-2013

Youth Labor Force Status from PUMS

Source: American Community Survey PUMS 2009-2013

African American Asian Caucasian Hispanic Native American Other0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

49.6%

67.3%74.7%

66.0%

35.2%

54.8%

24.2%

10.2%11.0%

13.2%

16.6%

20.5%

26.2% 22.5%14.3%

20.8%

48.2%

24.7%

Seattle Labor Force Status by Race for Youth 16-24

Employed Unemployed Not in labor force

Demographic comparisonsAfrican Americans comprise a larger proportion of students in Public Schools than they do in the County or City population

Sources: American Community Survey PUMS 2009-2013, ACS 2009-2013, and OSPI. Seattle Public Schools data for all grades; Seattle and King County Data for all ages

16.5%

16.5%

45.8%

12.4%

0.8%7.9%

Seattle Public Schools

6.8%

13.2%

67.4%

6.2%

0.5%5.9%

Seattle

African American Asian/Pacific Islander Caucasian HispanicNative American Other

5.8%

14.8%

64.8%

8.6%

0.7%5.2%

King County

Data for Juvenile court referrals 2005-2014

Combining Census data with other sets

Calculating re-offense rate

Clustering to determine common histories

Tabplot:tableplot – Cleaning the data

Shiny - Making the data more accessible

Other useful tools and packages

• RCurl – Scrape data from HTML tables via HTTP• sqldf – Create data frames using SQL statements• My Maps – Create Google maps with polygons and spreadsheet

backends• Ggplot2 – Visualizations and static maps• Rpart – Recursive partitioning algorithm• Shiny – Visualizations and interactive maps • Data.seattle.gov – Seattle’s Open Data Initiative resource

Shortcomings in the data

• The Census does a poor job of capturing data on the homeless, incarcerated populations, and undocumented aliens• These groups often bear the brunt of disparities

• The ACS does not permit estimates of small population groups, such as many of our immigrant and refugee communities, which makes it difficult to know where to provide services• The data capture population-level counts, but do not provide an easy

solution for measuring changes due to migration• Aggregate statistics may improve without having treated the root problem

Last points

• Democratization of data• Important to balance informational needs with privacy concerns• It takes work but is important to know the limitations of the data • Once you know the data sets you can shed a light on disparities

Clustering: Beyond predetermined groupings