- Description
- Codes
- Comparability
- Universe
- Availability
- Questionnaire Text
- Flags
- Source Variables
- Editing Procedure

## Description

METPOP10 reports the average 2010 population of metro/micro areas in each Public Use Microdata Area (PUMA). Where a PUMA lies entirely within a single metro area, this "average" is simply the metro area's population. Elsewhere, METPOP10 gives an approximation of the typical population size of the commuting systems where PUMA residents live.

Specifically, METPOP10 provides the population-weighted geometric mean of the 2010 populations of core-based (metropolitan/micropolitan) statistical areas (CBSAs), using the 2013 CBSA delineations of the Office and Management and Budget (OMB). For PUMA residents who live outside of any CBSA, METPOP10 uses county populations to approximate the commuting system population. (For Virginia "independent cities" that lie outside of CBSAs, we combine the populations of the independent cities with the populations of their neighboring counties.)

Using a geometric mean corresponds to measuring the average population on a logarithmic scale, which is suitable because CBSA and county populations generally have a log-normal distribution (highly concentrated at the lower end of the distribution with a long positive tail). For such distributions, the geometric mean is appropriately less sensitive to large outliers, more sensitive to variations among small values, and generally closer to the median than is the arithmetic mean. In practical terms, a logarithmic scaling makes sense because a difference between populations of 100,000 and 500,000 is about as significant for the character of a commuting system as any other factor-of-5 difference (e.g., 1 million and 5 million), and it is clearly more significant than an equal absolute difference of 400,000 in large commuting systems (e.g., 10.1 million and 10.5 million).

The specific steps to compute METPOP10 are 1) compute the populations of all spatial intersections (i.e., overlaps) between PUMAs and counties, 2) multiply each intersection's population by the logarithm of the population of the encompassing CBSA or noncore county, 3) sum these products for all intersections in each PUMA, 4) divide the sum for each PUMA by the total PUMA population, and 4) exponentiate the results to return to a linear scaling of populations.

For a detailed explanation and demonstration of the METPOP10 measure (as well as the DENSITY variable), see:

- Schroeder, J. and J. Pacas. (2019). Across the rural-urban universe: Two continuous indices of urbanization for U.S. census microdata (No. 2019-5). Minnesota Population Center Working Paper Series.

METPOP10 is an 8-digit numeric variable.

## Comparability

METPOP10 is generally comparable across years because it consistently reports the average 2010 populations of 2013 CBSAs (or noncore counties) in all samples. Changes in PUMA delineations, however, affect the correspondence between some PUMAs and CBSAs (or counties), such that for a given location, the METPOP10 value could change significantly between samples due only to changes in the extent of the encompassing PUMA.

Users should be aware that the PUMA boundaries used to compute METPOP10 are consistent only within these two sets of samples: 1) 2005-2011 ACS, and 2) 2010 census and 2012-onward ACS.

For a comparable variable that reports average 2000 populations of 2003 CBSAs (for 2000 census samples and 2005-2011 ACS samples), see METPOP00.

## Universe

- All households and group quarters.

## Availability

- 2019: All samples
- 2018: All samples
- 2017: All samples
- 2016: All samples
- 2015: All samples
- 2014: All samples
- 2013: All samples
- 2012: All samples
- 2011: All samples
- 2010: All samples
- 2009: All samples
- 2008: All samples
- 2007: All samples
- 2006: All samples
- 2005: All samples
- 2004: --
- 2003: --
- 2002: --
- 2001: --
- 2000: 5%; 1% unwt
- 1990: --
- 1980: --
- 1970: --
- 1960: --
- 1950: --
- 1940: --
- 1930: --
- 1920: --
- 1910: --
- 1900: --
- 1880: --
- 1870: --
- 1860: --
- 1850: --

- 2019: All samples
- 2018: All samples
- 2017: All samples
- 2016: All samples
- 2015: All samples
- 2014: All samples
- 2013: All samples
- 2012: All samples
- 2011: All samples
- 2010: All samples
- 2009: All samples
- 2008: All samples
- 2007: All samples
- 2006: All samples
- 2005: All samples
- 2000: PR 5%
- 1990: --
- 1980: --
- 1970: --
- 1930: --
- 1920: --
- 1910: --

## Flags

This variable has no flags.## Editing Procedure

There is no editing procedure available for this variable.