Moving from the Census to the
American Community Survey
Richard Lycan
Population Research Center
Portland State University
North American
Cartographic Information Society
October, 2008
PRC – Who we are
PRC
►
An applied research center:
 College of Urban and Public Affairs
 Portland State University
►
The Oregon link with the U.S. Census Bureau for:
 Population Estimates
 Census State Data Center
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Courses in applied demography
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Contract demographic research
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Staffing a mix of demographers, geographers, and planners
►
George Hough, PRC Director, usually with me to answer the
tough questions
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Outline for Presentation
PRC
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Some background and history on the Census of
Population and Housing and the American
Community Survey (ACS)
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What demographers and other scholars are saying
about the pros and cons of the ACS
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Examples showing use of data from the ACS
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Sources for more information
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Part 1 –
Introduction to the Census
and the ACS
PRC
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There will be no 2010 Census long form data
on income, poverty, housing values, and
dozens of other socio-economic characteristics
of persons, households, and housing units
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Instead, these data are becoming available
from the American Community Survey (ACS)
on an annual basis
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A Very Short History of the Census
►
1787 - Article 1, Section 2 of the U.S. Constitution requires that a census of the population be
conducted every ten years so that the representatives in Congress and direct taxes might be
apportioned.
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1790 - Federal marshals conduct the first census by going door-to-door through the 13 states plus
the districts of Maine, Vermont, Kentucky, and the Southwest Territory (Tennessee).
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1940 - Statistical sampling techniques are introduced, which allow the Census Bureau to create a
“long form” answered by only a subset of the population, the source for topics such as income and
poverty.
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Mail surveys, computers, the internet, wars, Elvis, TIGER
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1990’s – Initial planning for “continuous measurement” approach used in ACS
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2000 - Last use of the “long form” in the Decennial Census. To be replaced by the annual American
Community Survey (ACS)
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2006 - Initial public release of the ACS data.
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2010 – Release of the ACS data for all geographies down to block group level
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An Oversimplified View of the 2000 Census
PRC
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Build master address file (MAF) of all residential addresses in US
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Refine and finalize the questionnaire
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Build the maps and geography files
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Test the census – survey, tabulate, evaluate
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Hire thousands of temporary workers
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Collect the data: mail-out, field follow-up
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Tabulate the results: make many adjustments, create summary tables
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Publish the results: print, CD, Internet
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Get ready for the 2010 Census
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The Paired Surveys
►
Since 1940 there have been two key components to the Decennial
Census
 The “short form” survey is comprised of approximately 7 items
asked of every household, the results sometimes known as
Summary File 1
 The “long form” survey comprised of approximately 40 items asked
“long form” the
in 2010
of about 1 in 6No
households,
results sometimes known as
Summary File 3
 The two components are linked in that data from the “short form”
are used to adjust results from the “long form” by age, race,
housing units, etc.
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How the ACS Collects Data
PRC
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Surveys approximately 250,000 households each month
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Uses professional survey staff rather than temporary hires as
in the Decennial Census
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Uses a Master Address File to attempt to identify all
households in the US. Also gathers data from group quarters
residents
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Uses a mail-out census form with follow-up telephone and
field interviews
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The survey contains 55 questions for persons and 30 for
housing units, about same as long form 2000 census
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Tabulation and Publication of the ACS
► Similar
to the 2000 Census
 Tables are similar to those for the sample data (SF3) in the 2000
Census
 Geographies are similar to those in the 2000 Census data – states,
counties, cities, tracts, school districts, zip code areas, and many
others, but no block level data. There will be Public Use Microsample (PUMS) data
► Differs
from the 2000 Census
 Data will become available sooner after collection
 Data will be available on an annual basis
 Some data will be available for a single year but much will only be
published for 3 and 5 year averages
 There will be no paired “short form” data to use to adjust the ACS
survey results
PRC
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Geographies for the ACS
►
The geographies for the ACS are basically the same as for the 2000 Census
 The hierarchy of states-counties-tracts-block groups
 Metro areas, cities, census designated places
 Other geographies – congressional districts, zip code areas, school districts, TAZ’s,
rural areas of 60,000+ population
►
Some boundaries change: political jurisdictions annually, school districts every two years
►
Block groups generally the smallest geography available, no block level data
►
Reference maps in PDF format and boundary files in SHP format on WWW.CENSUS.GOV
.
PRC
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The Release Schedule
►
►
One year, three year, and five year data
Data
now available
for larger areas such as
Single Year Estimates
65,000+
counties,
larger school
3 Year Estimates
20,000+ districts, PUMS areas
Type of data
5 Year Estimates
►
Year of release
2006 2007 2008 2009 2010 2011 2012
Population Size
and Area
Census tracts and
Block Groups
Issues of statistical reliability and confidentiality
limit detail
Averaging for five year data
2005
►
PRC
Multi-year
data
a solution
2006
2010 Release
2007
X
2008
2011 Release
2009
2010
X
Averaging for three year data
2005
2008 Release
2009 Release
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2006
X
2007
X
2008
Note that only
the beginning
and end years
differ in the data
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For Example: ACS Data For Oregon School Districts
►
Now – Single
year data only
available for
districts 65,000
and over
►
Nov. 2008 – three
year data for
districts 20,000
and over
►
Late 2010 – five
year data for all
districts, plus 3
year and 1 year
data
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Part 2 –
What Demographers and Others
Are Saying about the ACS
PRC
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Pay Attention to Sampling Errors
PRC
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ACS sample smaller: For five year data the ACS
sample is smaller than that for the Census long
form
►
We have sinned: Sampling errors a problem
with previous census data, but we tended to
ignore, especially in mapping of the data
►
You can’t ignore it: The ACS provides explicit
data on confidence limits whereas the 2000
census provided calculating formulas
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Data Will Be Better for Large Areas,
Poorer For Small Areas
PRC
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Annual data: At the state, county, large school district level
publication of annual data will allow tracking of changes
►
Better surveys: The use of professional surveyors and
careful use of telephone and field follow-ups will result in
more complete questionnaires and less “imputation” for
missing entries
►
Larger sampling errors: For small areas such as census
tracts data will only be available as five year averages.
Sampling errors may be large
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Weighting of the Sample
PRC
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Need to inflate sample: If you sample 1 in 10
households, the results need to be inflated by a
factor of roughly 10 (more complicated than this)
►
The long and the short of it: The Census long
form data could be weighted from the short form
data by households, population, race , age, sex
►
Weighting weaker for ACS: The ACS data will be
weighted from various Census Bureau estimates
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Some Suggestions on Using ACS
Data – Linda Gage
►
►
►
►
►
►
►
PRC
Don’t try to analyze all of the data at once, even if you use
all of the items.
Concentrate on the data items that you already use in your
work.
Don’t assume the Census data are more accurate than
ACS.
Compare Census and ACS data to administrative records
that you have available.
Consider whether the data make sense.
Learn to use and provide the standard errors provided with
the ACS.
Share what you learn about using the ACS with the Census
Bureau and other professionals.
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Part 3 –
Examples Using the ACS
PRC
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Example 1 – Viewing Tabular Data from
the 2005 ACS
►
The next data are for
Public Use Micro-Sample
Areas (PUMAs) and
school districts with
populations 65,000 and
greater
►
PUMA’s have more than
65,000 persons and
single year data are
available for all of them.
►
However, sampling errors
can be large.
PRC
Data for sub-county areas
Several counties combined to
Reach 100,000 threshold
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Data for Oregon School Districts
►
The “margin of error” is for the 90% confidence level. This is the type
of error reporting used in the ACS. These data are for a PUMA, many
of which have over 100,000 population
►
Note the range for kindergarten enrollment of 1,046 – 2,068
Selected Social Characteristics
PUMA 00100, Oregon
Population 3 years and over enrolled in
school
Nursery school, preschool
Kindergarten
Elementary school (grades 1-8)
High school (grades 9-12)
College or graduate school
PRC
Estimate
Margin of Error
Lower Bound
Upper Bound
27,935
1,510
1,557
13,179
6,900
4,789
+/-1,271
+/-468
+/-511
+/-852
+/-745
+/-1,033
26,664
1,042
1,046
12,327
6,155
3,756
29,206
1,978
2,068
14,031
7,645
5,822
EDUCATIONAL ATTAINMENT
Population 25 years and over
Less than 9th grade
9th to 12th grade, no diploma
75,473
5,263
6,974
+/-1,139
+/-717
+/-982
74,334
4,546
5,992
76,612
5,980
7,956
High school graduate (includes equivalency)
Some college, no degree
Associate's degree
Bachelor's degree
Graduate or professional degree
25,060
19,635
4,920
9,021
4,600
+/-2,142
+/-1,657
+/-926
+/-1,185
+/-803
22,918
17,978
3,994
7,836
3,797
27,202
21,292
5,846
10,206
5,403
83.8
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+/-1.5
+/-1.8
82.3
16.2
85.3
19.8
Percent high school graduate or higher
Percent bachelor's degree or higher
20
Or, consider the variability in enrollment data
for school districts
►
PRC
The values in red show estimates that have a margin of error at least
20% of the estimate, then at least 10%
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Example 2 - Analysis of change using
census tract level data
PRC
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Housing tenure – Large universe, modest change
from year to year. We examine the change from
2001 to 2003 using ACS five year data for 19992003 and 2001-2005.
►
Citizenship – Small universe (foreign born) and
modest change from year to year. We use the
same data as above.
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Housing Tenure Change - Census Tracts
►
A large universe.
Overlapping time
periods.
►
►
►
% in 2001
% in 2003
Varies from county
mean
►
►
►
►
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% Change
@ 60% significance
@ 80% significance
@ 90% significance
@ 95% significance
PRC
Tracts with values
significantly different
There remain a number of tracts for
which change
1999 2000 2001 2002 2003
than the county average
over time
is
statistically
significant
and
they appear
of 42.7%
2001 2002 2003 2004 2005
not to be random in space.
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Housing Tenure – Grid Map Generalization
►
►
►
Choropleth mapping
Allocation to census
block centroids
The highly generalized form of the contours
may lend itself to easier verbalization of the
spatial patterns.
How would one assess statistical
significance?
Grid map
generalization




PRC
0.25 sq
0.50 sq
1.00 sq
1.50 sq
mi
mi
mi
mi
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Change in Foreign Born Who Are Citizens –
Census Tracts
►
A smaller universe
than for housing.
Overlapping time
periods.
1999
1999
2000
2000
2001
2001
2002
2003
2004
2005
Only a2003
few tracts
show
that
2002
2004 Significantly
2005 change
different from
is
statistically significant,
fewer
than
county percent
of 37.4
one would expect
by
chance.
I
would
who are citizens
not publish this map.
% in 2001
► % in 2003
► Varies from county
mean?
►
►
►
►
►
►
This doesn’t look good!
% Change
@ 60% significance
@ 80% significance
@ 90% significance
@ 95% significance
PRC
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Example 3 – The Published Tables Are But One
Representation of Reality
►
The following example shows the large sampling variability in the block
group data using % rental households of all single family households.
►
The universe of households was recreated from the 100% data from
the 1990.
►
20 random samples were drawn on a 1 in 6 basis.
►
Choropleth maps were draw for each sample?
►
Which map is the right one, the best representation of reality?
PRC
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Here are 20 “might have been” samples for
1990 SF3 sample data
►
The following sequence
shows 20 possible sets
of data that might have
been obtained in the
sampling process.
►
Note the considerable
local variation but also
the persistence of some
of the high values
►
The lesson: Be cautious
about interpreting small
area sample census
data.
PRC
Which of these 20
representations of reality is
closest to the truth?
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No telling what you can find
in some weird sample
►
►
►
PRC
On running 100’s of
sampling simulations for
row houses and condos
we think we came up
with an image of Elvis
?
Can you see an image of
Elvis in this map?
?
Beware: you can get
some strange
geographical
coincidences in maps of
sample data
?
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Suggestions on mapping with ACS data
PRC
►
Reliability - Inform your readers that the map is based on sample data
and the reality might vary. Where you can, provide quantitative
measures of error.
►
Period averages - Inform your readers when the data are for a 3 or 5
year period, and what this means in the particular context.
►
Aggregate, Aggregate - Reduce standard errors by aggregating over
longer time periods or for larger geographies. Avoid mapping with
block group level data.
►
Broaden class intervals - Look at the standard errors in the data when
setting class intervals so as not to give a false impression of precision.
►
Educate your clients - Let them know the limits of the ACS data for
mapping. Say “no” to ill conceived requests.
►
Share - Share what you learn about mapping with the ACS with
colleagues. Provide feedback to the Census Bureau.
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Where does one get the ACS data?
►
ACS: http://www.census.gov/acs/www/
►
American Factfinder:
►
FTP Site: http://www2.census.gov/acs/MultiYearEstimates/
PRC
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Additional Resources
►
US Census Bureau ACS: http://www.census.gov/acs/www/
►
Population Reference Bureau: The American Community Survey,
http://www.prb.org/pdf05/60.3The_American_Community.pdf
►
National Academy of Sciences: Using the American Community Survey:
Benefits and Challenges, forthcoming in paperback,
http://books.nap.edu/catalog.php?record_id=11901
►
Population Research and Policy Review (2006), No 25: A Special Issue on the
ACS: http://www.springerlink.com/content/102983/
►
Missouri Census Data Center: Ten Things to Know about The ACS:
http://mcdc2.missouri.edu/pub/data/acs2005/Ten_things_to_know.shtml
►
Portland Demographic Trends CD – Copies available, see presenter.
PRC
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The End
Richard Lycan
Population Research Center
Portland State University
Portland, Oregon, 97207-0751
lycand@pdx.edu
PRC
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