Tract-to-Zone Crosswalk — xwalk_tract2zone • cmapgeo

This table contains a set of factors to apportion Census tract-level data among the CMAP travel modeling zones. Separate factors are provided for apportioning housing unit, household, and population attributes. All factors were determined by calculating the percentage of a tract'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 zone (based on the location of the block's centroid point). Zones 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_tract2zone 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_tract2zone_2010.

xwalk_tract2zone

xwalk_tract2zone_2010

Format

xwalk_tract2zone is a tibble with 7499 rows and 6 variables:

geoid_tract: Unique 11-digit tract ID, assigned by the Census Bureau. Corresponds to tract_sf (although that only includes the tracts in the 7-county CMAP region). Character.
zone17: Numeric zone ID. Corresponds to zone_sf. Integer.
hu_pct: Proportion of the tract's housing units (occupied or vacant) located in the specified zone. Multiply this by a tract-level measure of a housing attribute (e.g. vacant homes) to estimate the zone's portion. Double.
hh_pct: Proportion of the tract's households (i.e. occupied housing units) living in the specified zone. Multiply this by a tract-level measure of a household attribute (e.g. car-free households) to estimate the zone's portion. Double.
pop_pct: Proportion of the tract's total population (including group quarters) living in the specified zone. Multiply this by a tract-level measure of a population attribute (e.g. race/ethnicity) to estimate the zone's portion. Double.
emp_pct: Proportion of the tracts's total jobs located in the specified zone. Multiply this by a tract-level measure of an employment attribute (e.g. retail jobs) to estimate the zone's portion. Not available in xwalk_tract2zone_2010. Double.

xwalk_tract2zone_2010 is a tibble with 6910 rows and 5 variables (no emp_pct).

Details

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

To use this crosswalk effectively, Census data should be joined to it (not vice versa, since tract 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 zone ID. If calculating rates, this should only be done after the counts have been summed to zone. The resulting table can then be joined to zone_sf for mapping, if desired.

If your data is also available at the block group level, it is recommended that you use that with xwalk_blockgroup2zone instead of the tract-level allocation. If the zone geography is too coarse for your needs, you can use subzones instead with xwalk_tract2subzone or xwalk_blockgroup2subzone.

Examples

# View the tract allocations for zone17 == 55
dplyr::filter(xwalk_tract2zone, zone17 == 55)
#> # A tibble: 4 × 6
#>   geoid_tract zone17  hu_pct  hh_pct pop_pct emp_pct
#>   <chr>        <int>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 17031081800     55 0.00235 0.00258 0.00431  0.0289
#> 2 17031081900     55 0.408   0.354   0.419    1     
#> 3 17031838300     55 0.320   0.391   0.477    0.216 
#> 4 17031842200     55 0.422   0.463   0.473    0.330 

# Map the zones missing from xwalk_tract2zone (i.e. no HU/HH/pop/emp)
library(ggplot2)
ggplot(dplyr::anti_join(zone_sf, xwalk_tract2zone)) +
  geom_sf(fill = "red", lwd = 0.1) +
  geom_sf(data = zone_sf, fill = NA, lwd = 0.1) +
  theme_void()
#> Joining with `by = join_by(zone17)`