Understanding Census/American Community Survey Data & Geographies

Darlanne Hoctor MulmatDarlanne.Mulmat@sandag.org619-699-7326

Andy GordonAndrew.Gordon@sandag.org619-699-6965

The goal of this workshop is to provide an overview of the basics of the Census and the American Community Survey, including the geographic boundaries used. Our contact information is shared for you to use as future questions arise.

Evolution

2000 & Prior Starting in 2010 Starting 2005

Basic Demographics (age, gender, race/ethnicity, owner/renter)

Short Form Short Form AmericanCommunity Survey (ACS)

Social & Economic Data Long Form Not collected ACS

Timing Once every 10 years Once every 10 years Annually

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• Since 1790, every person in the nation has been counted every ten years through the U.S. Census as mandated by the Constitution.

• The data collected is used to determine the number of seats each state has in the U.S. House of Representatives and also is used to distribute billions in federal funds to local communities.

• In 2010, the process for administering the surveys related to the Census was changed. • As this table shows, prior to 2010 (that is for the 2000 Census and earlier), a short form was

completed by all households in the U.S. to count the population and record basic demographic information, and a long form was administered to a sample of households to obtain more detailed social and economic data. This information was collected once every ten years.

• In 2005, the American Community Survey began as a replacement for the long form. This survey is administered to a smaller sample of households with data available on an annual basis. The count of the enter population has continued every ten years (in 2010 and the next one in 2020) with the short form.

Data CollectedDemographics Social Economic Housing

Sex* Families* Income Tenure*

Age* Education Poverty Occupancy*

Race* Marital Status Benefits (.g., Food Stamps)

Structure Type

Hispanic Origin* Fertility Employment Status Housing Value

Household Relationship*

Grandparents Occupation Taxes & Insurance

Group Quarters* Veteran Status Industry Utilities

Disability Status Journey to Work Mortgage

Language at Home Place of Work Monthly Rent

Citizenship Health Insurance

Migration

*Collected through the 2010 Census and ACS

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• These are the variables included on the Census and the American Community Survey (ACS). • The ones marked with an asterisk are collected through the Census and the ACS. • Everything without an asterisk is collected through the ACS only. • “Census & ACS Data Explained” (the document sent with the confirmation of your

registration for this workshop) provides details for each variable, including the specific questions from the survey form.

Race/Ethnicity

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• Since Race and Ethnicity are presented separately in this tabulation from American FactFinder (DP05), the data count individuals more than once: one time as part of “race” and one time as part of “hispanic or latino”.

Race/Ethnicity (cont.)

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• This tabulation does not double count.

Unemployment Rate

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• The accepted definition is unemployed people divided by civilian labor force. Therefore, use number in blue below, not the number in red.

ACS Design

• 3.54 million addresses

• 2.6% of total population

Sample Size

• Characteristics/trends

• Not counts

Purpose

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• Before accessing the data, it is helpful to understand how the ACS is designed. • The sample size for the ACS is about 3.54 million addresses 2.6 percent of the total

population. • No household is surveyed more than once in five years, but if someone moves they could be

surveyed more often. • The purpose of the ACS is to measure characteristics, not provide official population counts.

ACS Data Collection Process

Continuous

Monthly Sampling

Follow-up

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• Another important aspect of the American Community Survey is that the data are collected continuously throughout the year so there is no seasonal bias.

• Each month questionnaires are sent to 1/12 of the total annual sample (which is almost 300,000 households spread across the U.S.), with follow-up when surveys are not returned.

• The continuous process is advantageous because staff members are employed consistently throughout the year creating a better trained and experienced team.

• Surveys conducted between January 1st and December 31st of a given year are aggregated to produce annual estimates for that calendar year and released annually.

Weighting

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SAMPLE POPULATIONRace/Ethnicity

• Since the ACS is based on a sample of residents, the results are weighted based on the Census Bureau’s annual population estimates for the nation, each state, and every county.

• The estimates provide details by gender, age, race, Hispanic origin, and total household units. • For the tables in American FactFinder (the online tool provided by the Census Bureau for

accessing the data), the total is estimated by summing the weights for the group of interest with that characteristic.

• For example, the estimate of single people living in San Diego County is derived by applying the population weights to the number of people in the sample that indicated on the ACS that they were single.

Missing Data

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• Missing data results from when a respondent fails to answer a question or provides an invalid response. The Census Bureau uses two methods for handling missing data.

• Assignment is when rules are used to determine acceptable answers. A response to one question implies the value for a missing response to another question. For example, first name can be used to assign a value to gender.

• Allocation involves statistical procedures that apply answers from similar housing units or people who provided valid data to the respondent with missing information.

Period Estimates

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Cen

sus

AC

S

1 Year 3 Years

5 Years

• In contrast to the Census in which the characteristics reflect a specific point in time (April 1st of the year), the American Community Survey estimates describe the average characteristics during a specified period of time, reflecting the fact that data collection is spread evenly across the entire period so there is no over representation in any particular month or year within the period.

• There are three time periods: 1 year, 3 year, and 5 year.

Time Periods

Data File Basis forEstimates

Survey Years Population Threshold

1-Year 2013 2013 65,000+

3-Year 2011-2013 2011 2012 2013 20,000+

5-Year 2009-2013 2009 2010 2011 2012 2013 All

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• This table summarizes the data files available for the American Community Survey. The one-year estimates are released every September, the three-year in October, and the five-year in December.

• The availability of data for each time period depends on the number of people in the geographic area.

• The one-year estimates are available for areas with populations of 65,000 or more, three year for populations of 20,000 or more, and five year for all areas including Census Tracts and Block Groups.

• The estimates for block groups have very high margins of error, which brings us to the next topic.

Margin of Error (MOE)

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No Kevin – there isn’t any margin of error in math problems.

• Since the American Community Survey data are based on a sample and not a census of the entire population, the information is subject to error. That is, the results may vary from what would have occurred if the entire population was surveyed.

• The Census Bureau uses a 90 percent confidence level when computing the margin of error, which are included with the tabulations.

• The margin of error is a measure of how close the information is to the actual figure if everyone was surveyed, (the smaller the margin of error, the better the estimate)

• The margin of error is added and subtracted from the estimate to get the range of possible values within the 90 percent confidence interval. That is, there is a 90 percent chance that the actual figure is between these two values. Wider ranges are less reliable and smaller ones are more reliable.

Margin of Error Example

Range: 1,419,378 and 1,443,506

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Employment Status Estimate Margin of Error

Population 16 years and over 2,565,944 +/-2,911

In labor force 1,648,154 +/-11,862

Civilian labor force 1,572,337 +/-12,081

Employed 1,431,442 +/-12,064

Unemployed 140,895 +/-6,991

Armed Forces 75,817 +/-4,095

Not in labor force 917,790 +/-11,567

Source: 2013 ACS 1-year estimates for San Diego County

Here is a specific example for employment in San Diego County. The estimate is that 1,413,442 of the population 16 years of age and older are employed and the margin of error is + or - 12,064. This means that we are 90 percent sure that the actual number is between 1,419,378 and 1,443,506.

Relative Margin of Error

• Formula: (MOE / Estimate) * 100

• Suggested Guideline

– Good: Below 33%

– Use with Caution: 33%-49%

– Unreliable: 50% or greater

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• How do we know if the range suggests a reliable estimate or not? • Relative margin of error is helpful in answering this question. • This slide shows the formula. • While there are no rules regarding what is considered an acceptable relative margin of error,

these ranges are provided as a starting point.

Relative MOE Example

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Employment Status Estimate Margin of Error Relative MOE

Population 16 years and over 4,131 268 6% y

In labor force 2,819 200 7% y

Civilian labor force 2,806 196 7% y

Employed 2,588 200 8% y

Unemployed 218 90 41% y

Armed Forces 13 22 169% y

Not in labor force 1,312 244 19% ySource: 2013 ACS 5-year estimates for Census Trace 83.30 in San Diego County

• This specific example uses those thresholds for a single census tract. • The green lights on this table indicate that these estimates are reliable. • For unemployment, where the range is from 128 to 308, there is a yellow light suggesting

caution. • And the number in the armed forces, which has a range of -9 to 35, is clearly unreliable as

shown by the red light.

Time Period Choice

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• The choice of which estimate to use (1-year, 3-year, or 5-year) is a balancing act. • The decision for using multi-year estimates is more than simply a choice between using the

one-year or the multi-year estimates, because for many areas there also will be the choice of which multi-year estimate to use, three-year or five-year.

• For small areas, only five-year estimates are released (for example for Del Mar, census tracts), but for larger areas, one-, three-, and five-year estimates are available. For example, there are three sets of data for San Diego County – one-year estimates for 2013, three-year estimates reflecting 2011-2013, and five-year estimates for the period of 2009-2013. Decisions should be based on what is most appropriate based on the application of the data (e.g., requirements of a grant application).

• In making this choice, it is important to consider the tradeoff between the most current data and the most reliable data. The three-year and five-year estimates for an area have larger samples and smaller margins of error than the one-year estimates, but they are less current because the larger samples include data that were collected in earlier years.

• The main advantage of using multi-year estimates is the increased statistical reliability for smaller geographic areas and small population groups. For example, when looking at data for people who do not speak English well or at all and the specific languages they speak. The total number of cases in each category is very small, even when the focus is San Diego County as a whole. Multi-year data are the best choice.

• There are no hard-and-fast rules on choosing between one-, three-, and five-year data, but the margins of error provided with ACS data are helpful.

Comparing Data Over Time

Consistency in time periods

No Overlap

Comparison Tool

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• The Census Bureau recommends that only periods with similar lengths be compared. For example, 1-year to 1-year, 3-year to 3-year, and 5-year to 5-year. Conversely, do not compare estimates from different periods with different lengths (e.g., 1-year to 5-year).

• Also, make sure the periods do not overlap. For example, compare the 2008-2010 ACS 3-Year estimates with the 2011-2013 ACS 3-Year estimates. Comparisons of 5-Year estimates are not possible yet because the years included still overlap. In December, the 2010-2014 ACS 5-year file will be released and can be compared to the 2005-2009 ACS 5-year file.

• While, in general, it is okay to compare estimates to information gathered through the long form during the 2000 Census, it is important to verify that the information is comparable. There may be differences in the universe, question wording, residence rules, reference periods, and the way in which the data are tabulated. For example, income in the ACS is based on the one year before the survey was administered and this period varies across respondents depending on which month they get the survey. For the 2000 Census, respondents were asked to report their income for the same 12 month period (that is, the 1999 calendar year).

• The Census Bureau provides information about comparability through the Table Comparison Lookup Tool.

Table Comparison Lookup Tool

www.census.gov

About the Bureau

Our Surveys and Programs

American Community Survey

Guidance for Data Users

Comparing ACS data

Table specific Guidance

Table Comparison Lookup Tool

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• To access the Table Comparison Lookup Tool, follow these steps.

Customizing Estimates

• Search Existing Tables

– Profile Tables (DP02 – DP05)

– Subject Tables (Sxxx)

– Collapsed detailed tables (Cxxxxx)

• Combine Fewest Cells

• Calculate New Margin of Error

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• Sometimes you may need to aggregate estimates because the tabulations do not exactly show what you need. For example, you may want information for a custom geographic area. The new estimate is easy to compute, simply sum the published numbers. For the margin of error, however, you cannot simply add them together. You must compute new ones.

• The first step is to verify that the data are not already available in another table, such as the profile tables, subject tables, or collapsed detail tables. The numbering scheme is shared here because it can be helpful when searching through the lists in American FactFinder.

• If you determine that the information is not available on any other tables, find a table requiring the fewest cells to be combined and then compute a new margin of error.

Combine Fewest Cells

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Age Percent MOE

Under 5 years 6.5% +/-0.1

5 to 9 years 6.3% +/-0.2

10 to 14 years 6.1% +/-0.2

15 to 19 years 6.7% +/-0.1

20 to 24 years 8.5% +/-0.1

25 to 29 years 8.3% +/-0.1

30 to 34 years 7.6% +/-0.1

etc.

Source: 2013 ACS 1-year estimates for San Diego County

What proportion of the population is 5 to 14 years of age?

Age Categories Percent MOE

5 to 14 years 12.4% +/-0.1

15 to 17 years 3.8% +/-0.1

18 to 24 years 11.4% +/-0.1

15 to 44 years 44.3% +/-0.1

16 years and over 79.9% +/-0.1

18 years and over 77.4%

60 years and over 17.6% +/-0.1

etc.

• Here is a simple example of combining the fewest cells possible. • If you want to know the proportion of people between the ages of 5 and 14, you might find

the answer using the table on the left by simply adding the two categories and computing a new margin of error, which will be explained in a moment. But the number is already computed on the table on the right.

• This simple example doesn’t show dramatic differences in the result, but it does illustrate how the data included in the tables in American FactFinder vary.

• With respect to geographic areas, a census designated place is better than adding multiple census tracts.

• Also, it is sometimes helpful to subtract a smaller area from a larger one. For example, subtract one block group from a tract rather than adding up four block groups.

• Not only is it less work, it is also critical for computing the margin of error, which is the next topic.

MOE for Customized Estimates

• Formula for estimates:

• Formula for percentages:

• See Appendix 3 of ACS User Handbooks

www.census.gov/acs/www/guidance_for_data_users/handbooks/

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222 )]([...)]ˆ([)]ˆ([ nMOEYMOEXMOEsumMOE

][

)]([*][)]([/ 222

den

denMOEproportionnumMOEsumMOE

• As previously mentioned, while you can simply add the estimates together, this process is not appropriate for the margin of error. Here are the formulas. “Num” in the formula for percentages stands for numerator and “den” for denominator. The margin of error for any estimates that are zero should be excluded from these calculations because the Census Bureau uses the same margin of error for these estimates (e.g., 12 for San Diego County).

• FYI… The calculated margin of error will not be as accurate as the ones produced based on the actual surveys because it does not account for covariance.

• There are more details provided in the appendices of the ACS user handbooks located at this link, which was included in the list of resources attached to the reminder email for this workshop. There are different versions of the handbook based on the type of person using the data (e.g., researchers, local governments) and they are extremely helpful.

Universe

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• While this picture might make you think about outer space, the point is that every table in American FactFinder has a universe, reflecting what the data in the table represent.

• It is important to know the table universe to correctly interpret the data in the table. In each table, the total is the count of the universe not the total population.

Variations in the Universe

• Poverty Status

– Population for whom poverty status is determined

• Employment Status

– Population 16 years and over

• Unemployment

– Civilian labor force 16 years and over

• Employment Status by Disability Status

– Civilian non-institutional population 18-64 years

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• Here are a few examples of when the tables are based on a subset and not the entire population.

• Poverty Status is based on the population for whom poverty status is determined. • Employment status includes the population 16 years old and older. • Unemployment is restricted to the Civilian Labor Force. • Employment Status by Disability Status is based on Civilian non-institutional population 18-64

years.

Why are geographies important?

• Choosing an appropriate scale

• Valid comparison of data over time and across space

– Over time: Comparing changes in same area from one survey to the next

– Across space: Comparisons between defined geographic areas that are similar in size and population

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• Selecting an appropriate level of geography allows us to examine the variable (or variables) of interest at a relevant scale (household income at the county level may not be representative of incomes in particular city, or vice versa) as well as focusing directly on the area of interest.

• It also controls which ACS estimates are available (1, 3, or 5 year). • When making comparisons over time, it is useful to select a unit of geography with

boundaries that were unchanged for the period in question. • The examination of the spatial variation of certain variables can be more insightful if the

geographies used have similar population thresholds.

Legal Geographic Units

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• State– County (58 statewide)– Incorporated Cities (18 in SD County)– American Indian Reservation or

Rancheria (108 in CA, 18 in SD County)– California Senate (40) and State

Assembly districts (80)– School Districts– Congressional Districts

• Legal Geographic units have boundaries defined by law and cannot change without legal steps– Electoral district boundaries change

frequently

• The boundaries of legal geographies are delineated for the purpose of governance (i.e. states, counties, and cities), voting (electoral districts), or educational services (school districts).

• Although the boundaries are not designed for data collection/tabulation, they are informed by it through the redistricting process.

Statistical Geographic Units

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• Statistical areas are created to identify an area for collection and tabulation of census data

• Census BureauGeographic Units– Block– Block Group– Census Tract– County

• “Nested” Geographies– Boundaries do not cross– Data can be easily

summed to next larger area

• Statistical geographies are designed by the Census Bureau for data collection/tabulation using standardized criteria for boundaries and population thresholds.

• Most nest to larger statistical geographies, with some exceptions. • They tend to be more abstract, and therefore lack the familiarity that is often associated with

legal geographies.

County

• Legal and statistical geography

• Includes all areas of county

• Referred to as “San Diego Region” in SANDAG publications

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• The County of San Diego falls into both the legal and statistical category since it is a unit of governance and Census Tracts nest to its boundaries. It is inclusive of all areas within San Diego County (both incorporated and unincorporated).

• SANDAG publications refer to the county as a whole as the San Diego Region in order to make a distinction between unincorporated areas.

• With over 3.1 million people, ACS data is available at 1, 3, and 5 year estimates.

Incorporated Cities

• Legal geography: 18 cities in the San Diego Region

• Vary greatly in size and population

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• Incorporated cities are legal geographies defined for the purposes of governance, law enforcement, and voting.

• The ACS does not tabulate data for the unincorporated county as a stand-alone entity. • Incorporated cities vary in population from around 4,000 (Del Mar) to over 1.3 million (San

Diego), so direct comparisons between cities may not always be advisable for certain variables.

• The variation in population also means that 1 and 3 year ACS estimates may not be available for all cities.

• Note: cities are found under the “Places” geography in American Fact Finder.

Census Designated Places (CDPs)

• Statistical geography in unincorporated areas

• Areas with concentrations of population identifiable by name

• Do not nest to other geographies

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• A statistical geography defined by the Census Bureau with input from local agencies, CDPs capture data in communities in unincorporated areas of the county.

• Although they often share the same names as County Community Planning Areas, the boundaries are not the same and they do not provide continuous coverage of the unincorporated county (no nesting).

• Note: CDPs are found under the “Places” geography in American Fact Finder.

Census Tracts

• Small, relatively permanent statistical subdivisions of a county (nest within county boundaries)

• Average population ~ 4,000– 1,200 minimum– 8,000 maximum

• Boundaries are “visible and identifiable”

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• Census Tracts are a statistical geography that nests to county boundaries. • With over 600 covering the entire county, they offer a high level of geographic detail.

However, their smaller population levels mean that only 5 year estimates are available. • Tract boundaries must be visible and identifiable to aid canvassers in determining which tract

to assign Census and ACS responses to. As such, tract boundaries often do not align with city or other boundaries that fall along property lines or other divisions not marked by a visible feature.

• Census tracts are highly abstract; very few people are likely to be able to identify the tract they live in.

ZIP Code Tabulation Areas (ZCTAs)

• Approximations of USPS ZIP codes

• Composed of census blocks

• Coverage not continuous (some gaps)

• Biased toward residential areas

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• ZCTAs are a unique statistical geography that attempts to approximate USPS ZIP codes, since no official ZIP code boundaries exist (ZIP codes are used by the USPS for routing rather than geographic delineation.)

• ZCTAs are created by taking the return addresses from ACS responses by census block. A census block then is assigned to a ZCTA based on the ZIP listed on the majority of responses from that particular block.

• This biases ZCTAs toward residential areas and results in some gaps in coverage (ZCTAs also do not nest to other geographies).

• Like the ZIP codes they approximate, ZCTA numbers are familiar to most people and offer a fair level of geographic detail with over 100 covering at least some portion of the county.

Filling in the Gaps

• Local units of geography are not captured by the Census/ACS

– Community Planning Areas

– Neighborhoods

• Use Census Tracts to approximate

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• There are many smaller local units of geography (planning areas, neighborhoods, etc…) and user defined study areas for which ACS data is not tabulated by the Census Bureau.

• In many cases Census Tracts can be used to approximate these areas. • Select the tracts that provide the best coverage of the area of interest, and then combine the

data (using the Margin of Error formulas from the earlier slide to test for reliability). • The SANDAG Geographic Boundary Viewer (http://gis1.sandag.org/GeoBoundaryViewer/)can

be used to assist in the process by overlaying Census Tracts with a variety of boundary layers and base maps.

Useful Resources

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• “Census & ACS Resources” (sent with the reminder email for this class) provides a list of helpful resources.

Understanding Census/American Community Survey Data & Geographies

Darlanne Hoctor MulmatDarlanne.Mulmat@sandag.org619-699-7326

Andy GordonAndrew.Gordon@sandag.org619-699-6965

• Again, feel free to use this contact information as questions arise.