USPS Crosswalk¶

According to (US Department of Housing and Urban Development, n.d.), the difficulty of linking United States Postal Service (USPS) ZIP codes to Census Bureau regions is one of the many obstacles that social science scholars and practitioners face. Only at the ZIP code level is there relevant data that, when paired with demographic data calculated at various Census geographic levels, could open up new paths of investigation.

Furthermore, the (US Department of Housing and Urban Development, n.d.) believes that while there are several appropriate approaches for integrating ZIP codes with Census geographies, they are limited. The HUD-USPS Crosswalk Files were supplied by PD&R to enable more routes for integrating these data. These one-of-a-kind files were created using data from the quarterly USPS Vacancy Data. They come straight from the USPS; they're updated quarterly, so they're always up to date with changes in ZIP code configurations; and they reflect both commercial and residential addresses. Because many of the phenomena that housing researchers study are based on housing unit or address, the latter attribute is of special interest to them. Analysts can take into account not just the spatial distribution of population, but also the spatial distribution of residences, by utilizing an allocation approach based on residential addresses rather than by area or population. This allows for a more sophisticated approach to data allocation across different geographies.

These journal articles describe the problem and proposed solution in more detail:

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S.: Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf
Din, Alexander and Wilson, Ron, 2020. "Crosswalking ZIP Codes to Census: Geographies: Geoprocessing the U.S. Department of Housing & Urban Development’s ZIP Code Crosswalk Files," Cityscape: A Journal of Policy Development and Research, Volume 22, Number 1, https://www.huduser.gov/portal/periodicals/cityscpe/vol22num1/ch12.pdf

Census Geographies¶

This chart provided by the US Census Bureau gives a good example of the relationships among different geographies. The crosswalk files only support a subset of these.

Examples¶

There are 12 main function calls for the crosswalk files: the package also contains an omni function which encapsulates the capabilities of all the main function calls below -- [omni-function] Using the omni function for querying

[zip-tract] Crosswalk zipcode to census tract
[zip-county] Crosswalk zipcode to county fip
[zip-cbsa (Core Base Statistical Areas)] Crosswalk zipcode to core base statistical area (cbsa)
[zip-cbsadiv (Available 4th Quarter 2017 onwards)] Crosswalk zipcode to core based statistical area division (cbsadiv)
[zip-cd (Congressional District)] Crosswalk zipcode to congressional district (cd)
[tract-zip] Crosswalk census tract to zipcode
[county-zip] Crosswalk county fip into zipcode
[cbsa-zip] Crosswalk core based statistical areas (cbsa) to zipcode
[cbsadiv-zip (Available 4th Quarter 2017 onwards)] Crosswalk core based statistical areas division (cbsadiv) to zipcode
[cd-zip] Crosswalk congressional district (cd) to zipcode
[zip-countysub (Available 2nd Quarter 2018 onwards)] Crosswalk zipcode to county subdivision (countysub)
[countysub-zip (Available 2nd Quarter 2018 onwards)] Crosswalk county subdivision (countysub) to zipcode

The first geoid type in the function call is what to query for. For example in 1) above, 'zip' is the first geoid and 'tract' is the second geoid.

The second geoid in the function call describes the geoid which we want to determine 'intersection' with the first geoid where intersection is described as the % of residential, business, other, and total buildings that overlap.

For example, in function call #7, we might have a county called 22031 which has zip codes 71052, 71078, 71049, 71032 ... where the residential % (res_ratio) of each zip is 0.38, 0.21, 0.11, 0.05 ... respectively. Of all these zipcodes' res_ratios, when added up will equal 1, signaling these grouping of zip codes make up 100% of residential address in the county with each zipcode taking up their respective residential percentage.

Disclaimer: Although there exists inverse relationships in the Crosswalk Files, the measurements are NOT COMPLETELY inverse -- for reasons stated within the papers above.

These are basic examples which shows you to query the Crosswalk API. Before looking at the outputted data I RECOMMEND first taking a look at [the parameters] Parameters as well as [return data] Returns located at the bottom of the page.

Crosswalk zipcode to census tract¶

hud_cw_zip_tract(zip = '35213', year = ['2010'], quarter = ['1'])

Crosswalk zipcode to county fip¶

hud_cw_zip_county(zip = 35213, year = ['2020'], quarter = ['2'])

Crosswalk zipcode to core base statistical area (cbsa)¶

hud_cw_zip_cbsa(zip = 35213, year = ['2011'], quarter = ['3'])

Crosswalk zipcode to core based statistical area division (cbsadiv)¶

hud_cw_zip_cbsadiv(zip = '22031', year = ['2019'], quarter = ['4'])

Crosswalk zipcode to congressional district (cd)¶

hud_cw_zip_cd(zip = '35213', year = [2011]), quarter = [1]))

Crosswalk census tract to zipcode¶

hud_cw_tract_zip(tract = 48201223100, year = ['2017'], quarter = ['1'])

Crosswalk county fip into zipcode¶

hud_cw_county_zip(county = '22031', year = ['2010'], quarter = ['1'])

Crosswalk core based statistical areas (cbsa) to zipcode¶

hud_cw_cbsa_zip(cbsa = '10140', year = ['2017'], quarter = ['2'])

Crosswalk core based statistical areas division (cbsadiv) to zipcode¶

hud_cw_cbsadiv_zip(cbsadiv = 10380, year = ['2017'], quarter = ['4'])

Crosswalk congressional district (cd) to zipcode¶

hud_cw_cd_zip(cd = '2202', year = ['2010'], quarter = ['4'])

Crosswalk zipcode to county subdivision (countysub)¶

hud_cw_zip_countysub(zip = '35213', year = ['2019'], quarter = ['2'])

Crosswalk county subdivision (countysub) to zipcode¶

hud_cw_countysub_zip(countysub = '4606720300 ', year = ['2019', '2019', '2019'], quarter = ['4','4'])

Querying for only the crosswalked geoids¶

If you just want the crosswalked geoids, you can set the minimal argument to TRUE. This will return a vector containing the crosswalked geoids without the extra metadata.

hud_cw_county_zip(county = '22031', year = ['2010'], quarter = ['1'], minimal = TRUE)

Using the omni function for querying¶

The omni function is a redundant implementation of the functions shown above that requires specifying the type which can be from 1-12. The type argument follows the number scheme described at within the [input arguments][Input Arguments]. You also must use the 'query' argument (i.e query = 22031) for inputting geoids instead of the specific geoid names (i.e county = 22031, cd = 7200) used by the above functions.

hud_cw(type = 7, query = '22031', year = ['2010'], quarter = ['1'])

Crosswalking a dataset¶

For those who need to apply an allocation method (residential, business, other, total) to individual items in a data set, the crosswalk() function is available. Lets say we wanted to know the population at a zip code level (there is likely already a data set for this) for the counties of Washington, Wicomico, and Worchester in Maryland in the year 2019.

NOTE: The use of the crosswalk() function is likely best suited for datasets that are not described in the geographic identifier we want to crosswalk to. In this case population might not be the best example for this.

sample = data.frame(pop = [151049, 103609, 52276),
                    county = ["24043", "24045", "24047"))

head(sample)

In the crosswalked data set below each zip code associated with a county is assigned the same population value.

crosswalk(data = sample, geoid = "county", geoid_col = "county",
          cw_geoid = "zip", cw_geoid_col = NA, method = NA,
          year = 2019, quarter = 1)

To utilize an allocation method provided by the crosswalk files and apply it to columns of the data set, specify the method and cw_geoid_col arguments. In this case we want to allocate the county population levels to a zip code level using the method based on the ratio of residential addresses.

crosswalk(data = sample, geoid = "county", geoid_col = "county",
          cw_geoid = "zip", cw_geoid_col = "pop", method = "res",
          year = 2019, quarter = 1)

Geo-in-geo functions: does one geography overlap another?¶

This library also allows the user to determine if one geography overlaps another.

Disclaimer: Overlap is not determined by whether the boundaries intersect, but rather if any addresses lie in both.

This allows you to specify the year, quarter, and key arguments.

z_in_cbsa(zip = 71052, cbsa = 43340, year = 2019, quarter = 2)

Most recent USPS Crosswalk files¶

To get the most recent crosswalk files by year and quarter:

hud_rec_cw_yr()

Parameters¶

Returns¶

Returns	Description
zip, county, tract, cbsa, cbsadiv, cd, countysub	The geoid that is queried for. The name of the column will be named the geoid type
res_ratio	The ratio of residential addresses in the ZIP – Tract, County, or CBSA part to the total number of residential addresses in the entire ZIP. (for type 1-5 and 11) The ratio of residential addresses in the Zip, Tract, County, or CBSA - ZIP part to the total number of residential addresses in the entire Zip, Tract, County, or CBSA. (for type 6-10 and 12)
bus_ratio	The quarters in the year to retrieve: defaults to the first The ratio of business addresses in the ZIP – Tract, County, or CBSA part to the total number of business addresses in the entire ZIP. (for type 1-5 and 11) The ratio of business addresses in the Tract, County, or CBSA – ZIP part to the total number of business addresses in the entire Tract, County, or CBSA. (for type 6-10 and 12)
oth_ratio	The ratio of other addresses in the ZIP – Tract to the total number of other addresses in the entire ZIP. (for type 1-5 and 11). The ratio of other addresses in the Tract, County, or CBSA – ZIP part to the total number of other addresses in the entire Tract, County, or CBSA. (for type 6-10 and 12)
tot_ratio	The ratio of all addresses in the ZIP – Tract to the total number of all types of addresses in the entire ZIP. (for type 1-5 and 11) The ratio of all addresses in the Tract, County, or CBSA-ZIP part to the total number of all types of addresses in the entire Tract, County, or CBSA. (for type 6-10 and 12)
zip, county, tract, cbsa, cbsadiv, cd, countysub	The intersecting geoids depending on function call
year	Year the measurement was taken.
quarter	Quarter of year when measurement was taken.

References¶

Din, Alexander and Wilson, Ron, 2020. "Crosswalking ZIP Codes to Census: Geographies: Geoprocessing the U.S. Department of Housing & Urban Development’s ZIP Code Crosswalk Files," Cityscape: A Journal of Policy Development and Research, Volume 22, Number 1, https://www.huduser.gov/portal/periodicals/cityscpe/vol22num1/ch12.pdf
Katy Rossiter, K. R. (2014, July 31). Standard Hierarchy of Census Bereau: Geographies [Photograph]. Understanding Geographic Relationships: Counties, Places, Tracts and More. https://www.census.gov/newsroom/blogs/random-samplings/2014/07/understanding-geographic-relation ships-counties-places-tracts-and-more.html
U.S Department of Housing and Urban Development. (n.d.). HUD USPS ZIP: Code Crosswalk Files | HUD USER. HUD USPS ZIP CODE CROSSWALK FILES. Retrieved February 17, 2022, from https://www.huduser.gov/portal/datasets/usps_crosswalk.html
Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S.: Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294.