Block Group-to-Subzone Crosswalk — xwalk_blockgroup2subzone • cmapgeo

This table contains a set of factors to apportion Census block group-level data among the CMAP travel modeling subzones. Separate factors are provided for apportioning housing unit, household, and population attributes. All factors were determined by calculating the percentage of a block group's housing units, households and population that were located in each of its component blocks, according to the 2020 Decennial Census, and then assigning each block to a subzone (based on the location of the block's centroid point). Subzones that do not contain the centroid of any blocks with at least one housing unit, household, person or job are not present in this table. Use xwalk_blockgroup2subzone for data from the 2020 decennial census or the American Community Survey (ACS) from 2020 onward. For data from the 2010 decennial census or ACS from 2010 through 2019, use xwalk_blockgroup2subzone_2010.

xwalk_blockgroup2subzone

xwalk_blockgroup2subzone_2010

Format

xwalk_blockgroup2subzone is a tibble with 22781 rows and 6 variables:

geoid_blkgrp: Unique 12-digit block group ID, assigned by the Census Bureau. Corresponds to blockgroup_sf (although that only includes the block groups in the 7-county CMAP region). Character.
subzone17: Numeric subzone ID. Corresponds to subzone_sf. Integer.
hu_pct: Proportion of the block group's housing units (occupied or vacant) located in the specified subzone. Multiply this by a block group-level measure of a housing attribute (e.g. vacant homes) to estimate the subzone's portion. Double.
hh_pct: Proportion of the block group's households (i.e. occupied housing units) living in the specified subzone. Multiply this by a block group-level measure of a household attribute (e.g. car-free households) to estimate the subzone's portion. Double.
pop_pct: Proportion of the block group's total population (including group quarters) living in the specified subzone. Multiply this by a block group-level measure of a population attribute (e.g. race/ethnicity) to estimate the subzone's portion. Double.
emp_pct: Proportion of the block group's total jobs located in the specified subzone. Multiply this by a block group-level measure of an employment attribute (e.g. retail jobs) to estimate the subzone's portion. Not available in xwalk_blockgroup2subzone_2010. Double.

xwalk_blockgroup2subzone_2010 is a tibble with 21411 rows and 5 variables (no emp_pct).

Details

Other than in certain areas of Chicago, block groups tend to be significantly larger than subzones and have highly irregular boundaries, so in most cases the jobs, population, households and/or housing units in a block group are split across multiple subzones. For that reason, it is not appropriate to use a one-to-one block group-to-subzone assignment to apportion Census data among subzones, and this crosswalk should be used instead.

To use this crosswalk effectively, Census data should be joined to it (not vice versa, since block group IDs appear multiple times in this table). Once the data is joined, it should be multiplied by the appropriate factor (depending whether the data of interest is measured at the housing unit, household, person or job level), and then the result should be summed by subzone ID. If calculating rates, this should only be done after the counts have been summed to subzone. The resulting table can then be joined to subzone_sf for mapping, if desired.

If your data is only available at the tract level, you can use xwalk_tract2subzone for a tract-level allocation instead. If the subzone geography is too granular for your needs, you can use zones instead with xwalk_blockgroup2zone or xwalk_tract2zone.

Examples

# View the block group allocations for subzone17 == 1
dplyr::filter(xwalk_blockgroup2subzone, subzone17 == 1)
#> # A tibble: 4 × 6
#>   geoid_blkgrp subzone17 hu_pct hh_pct pop_pct emp_pct
#>   <chr>            <int>  <dbl>  <dbl>   <dbl>   <dbl>
#> 1 170310816001         1  0.285  0.290   0.324  0.0895
#> 2 170310816002         1  1      1       1      1     
#> 3 170310817001         1  0.877  0.882   0.853  0.233 
#> 4 170310817002         1  0.161  0.165   0.167  0.0517

# Map the subzones missing from xwalk_blockgroup2subzone (i.e. no HU/HH/pop/emp)
library(ggplot2)
ggplot(dplyr::anti_join(subzone_sf, xwalk_blockgroup2subzone)) +
  geom_sf(fill = "red", lwd = 0.1) +
  geom_sf(data = subzone_sf, fill = NA, lwd = 0.1) +
  theme_void()
#> Joining with `by = join_by(subzone17)`