census-data-aggregator 0.0.4

Combine U.S. census data responsibly

Combine U.S. census data responsibly

Features

• Approximating sums

• Approximating medians

• Approximating percent change

• Approximating products

• Approximating proportions

• Approximating ratios

Installation

$ pipenv install census-data-aggregator

Usage

Import the library.

>>> import census_data_aggregator

Approximating sums

Total together estimates from the U.S. Census Bureau and approximate the combined margin of error. Follows the bureau’s official guidelines for how to calculate a new margin of error when totaling multiple values. Useful for aggregating census categories and geographies.

Accepts an open-ended set of paired lists, each expected to provide an estimate followed by its margin of error.

>>> males_under_5, males_under_5_moe = 10154024, 3778
>>> females_under_5, females_under_5_moe = 9712936, 3911
>>> census_data_aggregator.approximate_sum(
    (males_under_5, males_under_5_moe),
    (females_under_5, females_under_5_moe)
)
19866960, 5437.757350231803

Approximating medians

Estimate a median and approximate the margin of error. Follows the U.S. Census Bureau’s official guidelines for estimation using a design factor. Useful for generating medians for measures like household income and age when aggregating census geographies.

Expects a list of dictionaries that divide the full range of data values into continuous categories. Each dictionary should have three keys:

key	value
min	The minimum value of the range
max	The maximum value of the range
n	The number of people, households or other units in the range

The minimum value in the first range and the maximum value in the last range can be tailored to the dataset by using the “jam values” provided in the American Community Survey’s technical documentation.

>>> income = [
    dict(min=2499, max=9999, n=186),
    dict(min=10000, max=14999, n=78),
    dict(min=15000, max=19999, n=98),
    dict(min=20000, max=24999, n=287),
    dict(min=25000, max=29999, n=142),
    dict(min=30000, max=34999, n=90),
    dict(min=35000, max=39999, n=107),
    dict(min=40000, max=44999, n=104),
    dict(min=45000, max=49999, n=178),
    dict(min=50000, max=59999, n=106),
    dict(min=60000, max=74999, n=177),
    dict(min=75000, max=99999, n=262),
    dict(min=100000, max=124999, n=77),
    dict(min=125000, max=149999, n=100),
    dict(min=150000, max=199999, n=58),
    dict(min=200000, max=250001, n=18)
]

For a margin of error to be returned, a “design factor” must be provided to calculate the standard error. The statistical input is used to tailor the estimate to the variance of the dataset. The Census Bureau publishes design factors as part of its PUMS Accuracy statement. Find the value for the dataset you are estimating by referring to the bureau’s reference material.

>>> census_data_aggregator.approximate_median(income, design_factor=1.5)
42211.096153846156, 27260.315546093672

If a design factor is not provided, no margin of error will be returned.

>>> census_data_aggregator.approximate_median(income)
42211.096153846156, None

Approximating percent change

Calculates the percent change between two estimates and approximates its margin of error. Follows the bureau’s ACS handbook.

Accepts two paired lists, each expected to provide an estimate followed by its margin of error. The first input should be the earlier estimate in the comparison. The second input should be the later estimate.

Returns both values as percentages multiplied by 100.

>>> single_women_in_fairfax_before = 135173, 3860
>>> single_women_in_fairfax_after = 139301, 4047
>>> census_data_aggregator.approximate_percentchange(
  single_women_in_fairfax_before,
  single_women_in_fairfax_after
)
3.0538643072211165, 4.198069852261231

Approximating products

Calculates the product of two estimates and approximates its margin of error. Follows the bureau’s ACS handbook.

Accepts two paired lists, each expected to provide an estimate followed by its margin of error.

>>> owner_occupied_units = 74506512, 228238
>>> single_family_percent = 0.824, 0.001
>>> census_data_aggregator.approximate_product(
    owner_occupied_units,
    single_family_percent
)
61393366, 202289

Approximating proportions

Calculate an estimate’s proportion of another estimate and approximate the margin of error. Follows the bureau’s ACS handbook. Simply multiply the result by 100 for a percentage. Recommended when the first value is smaller than the second.

Accepts two paired lists, each expected to provide an estimate followed by its margin of error. The numerator goes in first. The denominator goes in second. In cases where the numerator is not a subset of the denominator, the bureau recommends using the approximate_ratio method instead.

>>> single_women_in_virginia = 203119, 5070
>>> total_women_in_virginia = 690746, 831
>>> census_data_aggregator.approximate_proportion(
    single_women_in_virginia,
    total_women_in_virginia
)
0.322, 0.008

Approximating ratios

Calculate the ratio between two estimates and approximate its margin of error. Follows the bureau’s ACS handbook.

Accepts two paired lists, each expected to provide an estimate followed by its margin of error. The numerator goes in first. The denominator goes in second. In cases where the numerator is a subset of the denominator, the bureau recommends uses the approximate_proportion method.

>>> single_men_in_virginia = 226840, 5556
>>> single_women_in_virginia = 203119, 5070
>>> census_data_aggregator.approximate_ratio(
    single_men_in_virginia,
    single_women_in_virginia
)
1.117, 0.039

A note from the experts

The California State Data Center’s Demographic Research Unit notes:

The user should be aware that the formulas are actually approximations that overstate the MOE compared to the more precise methods based on the actual survey returns that the Census Bureau uses. Therefore, the calculated MOEs will be higher, or more conservative, than those found in published tabulations for similarly-sized areas. This knowledge may affect the level of error you are willing to accept.

The American Community Survey’s handbook adds:

As the number of estimates involved in a sum or difference increases, the results of the approximation formula become increasingly different from the [standard error] derived directly from the ACS microdata. Users are encouraged to work with the fewest number of estimates possible.

References

This module was designed to conform with the Census Bureau’s April 18, 2018, presentation “Using American Community Survey Estimates and Margin of Error”, the bureau’s PUMS Accuracy statement and the California State Data Center’s 2016 edition of “Recalculating medians and their margins of error for aggregated ACS data.”, and the Census Bureau’s ACS 2018 General Handbook Chapter 8, “Calculating Measures of Error for Derived Estimates”