In psrc/psrccensus: Summarize Census Data for use at PSRC

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(magrittr)
library(knitr)
library(psrccensus)

Every Census estimate is linked to a geography. However, not every geography is linked to a Census estimate.

Since the Census Bureau cannot publish data at every conceivable scale, data users often approximate a custom boundary by choosing a set of disaggregate Census geographies (e.g. block, block group, or tract) that closely align with it. This approach is very often adequate to the purpose.

Geographic conversion via a granular proxy variable

For more geographic precision, we in effect disaggregate the estimate to a granular scale--specifically, parcel--by assuming the estimate is in direct proportion either to a variable reported at that scale (housing units), or to one of several population measures we can apportion to that scale (e.g. OFM total population).

With this in mind, PSRC has developed demographic ratios to convert estimates from Census geographies to its own planning geographies. Each planning geography has its own function:

• Regional Growth Strategy classes - census_to_rgs()
• Regional Growth Centers - census_to_rgc()
• Manufacturing-Industrial Centers - census_to_mic()
• Traffic Analysis Zones - census_to_taz()
• High Capacity Transit Station areas - census_to_hct()
• 2020 Jurisdictional boundaries - census_to_juris()

As long as you're using the default proxy variable (total population), you only need a single argument: the name of the dataframe (as returned by get_acs_recs() or get_decennial_recs()) you wish to convert.

library(psrccensus)
library(magrittr)

x <- get_acs_recs(geography = 'block group',
                  table.names = 'B03002',
                  years = 2021,
                  acs.type = 'acs5') %>% 
  dplyr::mutate(label=stringr::str_replace_all(label,"(^Estimate!!|Total:!!)",""))

x %>% dplyr::filter(variable=="B03002_012") %>% .[,c(1,5:8)] %>% head()

rgc_race <- census_to_rgc(x)

rgc_race %>% dplyr::filter(variable=="B03002_012") %>% .[,c(1,3,7:8)] %>% head()

Selecting the correct proxy

The key assumption behind this method is the direct relationship between the granular proxy variable and the Census variable of interest: the stronger this relationship, the more defensible the result. Before splitting a geography, consider which of these metrics is most relevant to your Census estimate of interest:

• total_pop --i.e. total population (the default)
• household_pop
• group_quarters_pop
• housing_units
• occupied_housing_units

You can specify these using the wgt argument:

y <- get_acs_recs(geography = 'tract',
                  table.names = 'B26001',
                  years = 2019,
                  acs.type = 'acs5') %>% 
  dplyr::mutate(label=stringr::str_replace_all(label,"(^Estimate!!|Total:!!)",""))

y %>% .[,c(1,5:8)] %>% head()

rgc_gq_age <- census_to_rgs(y, wgt="group_quarters_pop")

rgc_gq_age %>% .[,c(1,3,4,7,8)]

If your dataframe combines tables that would most appropriately apply two different proxy metrics--for example, one linked to total population, and another linked to housing units--you might divide the dataframe (or alter your code to retrieve separate tables originally) before applying the geographic conversion.

Further geographies

The set of planning geographies is not strictly limited to the five listed above; with approval, the set can be expanded (see the documentation - PSRC VPN required to view).

To convert census estimates to custom geographies that don't have stored splits, use the census_to_customgeo() function. This performs an analogous conversion using the same parcel-level proxy variables, but requires two additional arguments: an sf package object (i.e. your custom geography/geometry), and the variable name in that file that labels the geography. This function is much slower than the PSRC planning geography functions listed above because it must get parcel-level data from Elmer and create the custom splits rather than just read them. Depending on your download speed and the complexity of the geography, it'll take a minute or two to run (still, not shabby for this level of detail).

Error margins & limitations

Currently, split-derived calculations are limited to estimates only; medians cannot be determined via this method. Analysts can calculate shares by converting estimates of both the numerator and denominator to the appropriate geography.

Although it may be harder to assume Margins of Error (MOE) are directly proportional to the split weight, the assumption is applied identically to both estimate and MOE, which seemed the best choice of the available options.

psrc/psrccensus documentation built on June 1, 2025, 1:06 p.m.