Crosswalk • rhud

Program Overview

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

zip-tract
zip-county
zip-cbsa (Core Base Statistical Areas)
zip-cbsadiv (Available 4th Quarter 2017 onwards)
zip-cd (Congressional District)
tract-zip
county-zip
cbsa-zip
cbsadiv-zip (Available 4th Quarter 2017 onwards)
cd-zip
zip-countysub (Available 2nd Quarter 2018 onwards)
countysub-zip (Available 2nd Quarter 2018 onwards)

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 as well as return data located at the bottom of the page.

Crosswalk zipcode to census tract

library(rhud)
#> 
#> rhud:
#> -------------------------------------------------------------------------------------
#> * To begin using this package, you must obtain a key from HUD User website.
#> * To enable caching of rhud data, use `options(rhud_use_cache = TRUE)`
#> * To get tibbles instead of dataframes, use `options(rhud_use_tibble = TRUE)`
#> * Set these in your R script for single session or in .Rprofile for persistence.
#> -------------------------------------------------------------------------------------
options (digits=4)

hud_cw_zip_tract(zip = '35213', year = c('2010'), quarter = c('1'))
#> Downloading: [==================================================] 100% 1/1
#> 
#>         tract res_ratio bus_ratio oth_ratio tot_ratio   zip year quarter
#> 1 01073010801  0.338469  0.404558  0.697872  0.354414 35213 2010       1
#> 2 01073010803  0.230782  0.028490  0.008511  0.212530 35213 2010       1
#> 3 01073005600  0.185858  0.039886  0.004255  0.171949 35213 2010       1
#> 4 01073010802  0.114337  0.011396  0.000000  0.105006 35213 2010       1
#> 5 01073002306  0.061953  0.509971  0.144681  0.088122 35213 2010       1
#> 6 01073004702  0.037626  0.000000  0.144681  0.039396 35213 2010       1
#> 7 01073004800  0.018975  0.000000  0.000000  0.017328 35213 2010       1
#> 8 01073010805  0.009244  0.005698  0.000000  0.008738 35213 2010       1
#> 9 01073010804  0.002757  0.000000  0.000000  0.002518 35213 2010       1

Crosswalk zipcode to county fip

hud_cw_zip_county(zip = 35213, year = c('2020'), quarter = c('2'))
#> 
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#>   county res_ratio bus_ratio oth_ratio tot_ratio   zip year quarter
#> 1  01073         1         1         1         1 35213 2020       2

Crosswalk zipcode to core base statistical area (cbsa)

hud_cw_zip_cbsa(zip = 35213, year = c('2011'), quarter = c('3'))
#> 
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#>    cbsa res_ratio bus_ratio oth_ratio tot_ratio   zip year quarter
#> 1 13820         1         1         1         1 35213 2011       3

Crosswalk zipcode to core based statistical area division (cbsadiv)

hud_cw_zip_cbsadiv(zip = '22031', year = c('2019'), quarter = c('4'))
#> 
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#>   cbsadiv res_ratio bus_ratio oth_ratio tot_ratio   zip year quarter
#> 1   47894         1         1         1         1 22031 2019       4

Crosswalk zipcode to congressional district (cd)

hud_cw_zip_cd(zip = '35213', year = c(2011), quarter = c(1))
#> 
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#>     cd res_ratio bus_ratio oth_ratio tot_ratio   zip year quarter
#> 1 0106  0.952728    0.9831  0.991701  0.955706 35213 2011       1
#> 2 0107  0.043711    0.0169  0.008299  0.041045 35213 2011       1
#> 3 0103  0.001781    0.0000  0.000000  0.001624 35213 2011       1
#> 4 0104  0.001781    0.0000  0.000000  0.001624 35213 2011       1

Crosswalk census tract to zipcode

hud_cw_tract_zip(tract = 48201223100, year = c('2017'), quarter = c('1'))
#> 
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#>     zip res_ratio bus_ratio oth_ratio tot_ratio       tract year quarter
#> 1 77032         1   0.95425         1  0.991617 48201223100 2017       1
#> 2 77396         0   0.04575         0  0.008383 48201223100 2017       1

Crosswalk county fip into zipcode

hud_cw_county_zip(county = '22031', year = c('2010'), quarter = c('1'))
#> 
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#>      zip res_ratio bus_ratio oth_ratio tot_ratio county year quarter
#> 1  71052 0.4322130  0.699634   0.56338 0.4455100  22031 2010       1
#> 2  71078 0.1816700  0.086081   0.15493 0.1770400  22031 2010       1
#> 3  71049 0.1103580  0.065934   0.15493 0.1085520  22031 2010       1
#> 4  71032 0.0813449  0.040293   0.01408 0.0790172  22031 2010       1
#> 5  71027 0.0699566  0.047619   0.04225 0.0687441  22031 2010       1
#> 6  71030 0.0435647  0.020147   0.04225 0.0424621  22031 2010       1
#> 7  71046 0.0425705  0.021978   0.01408 0.0414348  22031 2010       1
#> 8  71063 0.0275669  0.014652   0.01408 0.0268813  22031 2010       1
#> 9  71419 0.0105748  0.003663   0.00000 0.0101875  22031 2010       1
#> 10 71065 0.0001808  0.000000   0.00000 0.0001712  22031 2010       1

Crosswalk core based statistical areas (cbsa) to zipcode

hud_cw_cbsa_zip(cbsa = '10140', year = c('2017'), quarter = c('2'))
#> 
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#>      zip res_ratio bus_ratio oth_ratio tot_ratio  cbsa year quarter
#> 1  98520 3.384e-01 0.5520178 0.4216084 3.573e-01 10140 2017       2
#> 2  98550 1.823e-01 0.1780822 0.2258327 1.835e-01 10140 2017       2
#> 3  98569 1.268e-01 0.0744169 0.1283509 1.230e-01 10140 2017       2
#> 4  98563 1.166e-01 0.0995927 0.1064175 1.150e-01 10140 2017       2
#> 5  98541 1.001e-01 0.0377638 0.0609261 9.411e-02 10140 2017       2
#> 6  98557 4.417e-02 0.0148093 0.0138099 4.093e-02 10140 2017       2
#> 7  98537 3.694e-02 0.0159200 0.0138099 3.457e-02 10140 2017       2
#> 8  98568 2.151e-02 0.0011107 0.0040617 1.938e-02 10140 2017       2
#> 9  98595 1.415e-02 0.0103665 0.0154346 1.391e-02 10140 2017       2
#> 10 98547 5.923e-03 0.0029619 0.0024370 5.581e-03 10140 2017       2
#> 11 98535 2.993e-03 0.0011107 0.0016247 2.805e-03 10140 2017       2
#> 12 98575 2.899e-03 0.0014809 0.0008123 2.721e-03 10140 2017       2
#> 13 98526 2.307e-03 0.0051833 0.0000000 2.444e-03 10140 2017       2
#> 14 98552 1.964e-03 0.0025916 0.0000000 1.944e-03 10140 2017       2
#> 15 98536 1.964e-03 0.0007405 0.0000000 1.805e-03 10140 2017       2
#> 16 98571 6.546e-04 0.0007405 0.0016247 6.942e-04 10140 2017       2
#> 17 98579 2.494e-04 0.0000000 0.0000000 2.221e-04 10140 2017       2
#> 18 98559 3.117e-05 0.0007405 0.0000000 8.331e-05 10140 2017       2
#> 19 98562 0.000e+00 0.0003702 0.0008123 5.554e-05 10140 2017       2
#> 20 98583 0.000e+00 0.0000000 0.0008123 2.777e-05 10140 2017       2
#> 21 98587 0.000e+00 0.0000000 0.0008123 2.777e-05 10140 2017       2
#> 22 98566 0.000e+00 0.0000000 0.0008123 2.777e-05 10140 2017       2

Crosswalk core based statistical areas division (cbsadiv) to zipcode

hud_cw_cbsadiv_zip(cbsadiv = 10380, year = c('2017'), quarter = c('4'))
#> 
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#>      zip res_ratio bus_ratio oth_ratio tot_ratio cbsadiv year quarter
#> 1  00662 0.1640649  0.185800  0.147385 1.652e-01   10380 2017       4
#> 2  00603 0.1564245  0.142274  0.407290 1.582e-01   10380 2017       4
#> 3  00685 0.1352663  0.138602  0.076070 1.349e-01   10380 2017       4
#> 4  00602 0.1003411  0.102149  0.061807 1.000e-01   10380 2017       4
#> 5  00610 0.0840450  0.075490  0.037242 8.304e-02   10380 2017       4
#> 6  00641 0.0810440  0.090996  0.081616 8.166e-02   10380 2017       4
#> 7  00676 0.0817030  0.066785  0.045959 8.042e-02   10380 2017       4
#> 8  00669 0.0679181  0.054815  0.025357 6.668e-02   10380 2017       4
#> 9  00677 0.0574370  0.055359  0.102219 5.778e-02   10380 2017       4
#> 10 00605 0.0337231  0.048014  0.001585 3.426e-02   10380 2017       4
#> 11 00690 0.0264032  0.018906  0.007132 2.575e-02   10380 2017       4
#> 12 00604 0.0057615  0.017546  0.001585 6.434e-03   10380 2017       4
#> 13 00611 0.0056725  0.003128  0.001585 5.475e-03   10380 2017       4
#> 14 00612 0.0001959  0.000136  0.001585 2.068e-04   10380 2017       4
#> 15 00631 0.0000000  0.000000  0.001585 1.654e-05   10380 2017       4

Crosswalk congressional district (cd) to zipcode

hud_cw_cd_zip(cd = '2202', year = c('2010'), quarter = c('4'))
#> 
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#>      zip res_ratio bus_ratio oth_ratio tot_ratio   cd year quarter
#> 1  70072 0.0873171 6.011e-02 0.0383332 8.356e-02 2202 2010       4
#> 2  70115 0.0700244 6.151e-02 0.0831270 6.984e-02 2202 2010       4
#> 3  70119 0.0689893 7.905e-02 0.0521159 6.916e-02 2202 2010       4
#> 4  70058 0.0621786 5.956e-02 0.1148918 6.382e-02 2202 2010       4
#> 5  70094 0.0559088 2.953e-02 0.0506084 5.374e-02 2202 2010       4
#> 6  70117 0.0563989 3.658e-02 0.0189512 5.360e-02 2202 2010       4
#> 7  70122 0.0560060 2.443e-02 0.0243351 5.252e-02 2202 2010       4
#> 8  70130 0.0438001 1.326e-01 0.1001400 5.245e-02 2202 2010       4
#> 9  70114 0.0511557 3.982e-02 0.0505007 5.028e-02 2202 2010       4
#> 10 70118 0.0520726 2.443e-02 0.0325186 4.931e-02 2202 2010       4
#> 11 70131 0.0484644 1.244e-02 0.0328416 4.521e-02 2202 2010       4
#> 12 70126 0.0421946 2.998e-02 0.0360719 4.106e-02 2202 2010       4
#> 13 70127 0.0378133 2.358e-02 0.0369333 3.671e-02 2202 2010       4
#> 14 70056 0.0353248 2.828e-02 0.0390869 3.493e-02 2202 2010       4
#> 15 70116 0.0319406 4.517e-02 0.0675137 3.418e-02 2202 2010       4
#> 16 70065 0.0333898 1.114e-02 0.0648218 3.281e-02 2202 2010       4
#> 17 70062 0.0249526 8.814e-02 0.0412404 3.028e-02 2202 2010       4
#> 18 70125 0.0298282 2.718e-02 0.0187359 2.924e-02 2202 2010       4
#> 19 70128 0.0290761 5.197e-03 0.0043071 2.641e-02 2202 2010       4
#> 20 70113 0.0189405 3.168e-02 0.0330570 2.039e-02 2202 2010       4
#> 21 70053 0.0186194 4.232e-02 0.0179821 2.038e-02 2202 2010       4
#> 22 70112 0.0134058 5.072e-02 0.0194896 1.643e-02 2202 2010       4
#> 23 70129 0.0139466 1.404e-02 0.0059223 1.367e-02 2202 2010       4
#> 24 70003 0.0064473 1.054e-02 0.0052762 6.715e-03 2202 2010       4
#> 25 70121 0.0040264 6.396e-03 0.0055992 4.260e-03 2202 2010       4
#> 26 70124 0.0045376 1.049e-03 0.0015075 4.169e-03 2202 2010       4
#> 27 70123 0.0016308 7.995e-04 0.0023689 1.594e-03 2202 2010       4
#> 28 70001 0.0004352 8.944e-03 0.0007537 1.087e-03 2202 2010       4
#> 29 70067 0.0011745 3.498e-04 0.0000000 1.071e-03 2202 2010       4
#> 30 70170 0.0000000 5.496e-03 0.0003230 4.248e-04 2202 2010       4
#> 31 70163 0.0000000 4.597e-03 0.0002154 3.534e-04 2202 2010       4
#> 32 70139 0.0000000 3.698e-03 0.0002154 2.857e-04 2202 2010       4
#> 33 70146 0.0000000 4.997e-04 0.0002154 4.511e-05 2202 2010       4
#> 34 70143 0.0000000 4.997e-05 0.0000000 3.760e-06 2202 2010       4
#> 35 70165 0.0000000 4.997e-05 0.0000000 3.760e-06 2202 2010       4
#> 36 70141 0.0000000 4.997e-05 0.0000000 3.760e-06 2202 2010       4

Crosswalk zipcode to county subdivision (countysub)

hud_cw_zip_countysub(zip = '35213', year = c('2019'), quarter = c('2'))
#> 
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#>    countysub res_ratio bus_ratio oth_ratio tot_ratio   zip year quarter
#> 1 0107390324         1         1         1         1 35213 2019       2

Crosswalk county subdivision (countysub) to zipcode

hud_cw_countysub_zip(countysub = '4606720300 ', year = c('2019', '2019', '2019'),
                     quarter = c('4','4'))
#> 
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#>     zip res_ratio bus_ratio oth_ratio tot_ratio  countysub year quarter
#> 1 57376         1         1         0         1 4606720300 2019       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 = c('2010'), quarter = c('1'), minimal = TRUE)
#> 
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#>  [1] "71052" "71078" "71049" "71032" "71027" "71030" "71046" "71063" "71419"
#> [10] "71065"

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 = c('2010'), quarter = c('1'))
#> 
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#>      zip res_ratio bus_ratio oth_ratio tot_ratio county year quarter
#> 1  71052 0.4322130  0.699634   0.56338 0.4455100  22031 2010       1
#> 2  71078 0.1816700  0.086081   0.15493 0.1770400  22031 2010       1
#> 3  71049 0.1103580  0.065934   0.15493 0.1085520  22031 2010       1
#> 4  71032 0.0813449  0.040293   0.01408 0.0790172  22031 2010       1
#> 5  71027 0.0699566  0.047619   0.04225 0.0687441  22031 2010       1
#> 6  71030 0.0435647  0.020147   0.04225 0.0424621  22031 2010       1
#> 7  71046 0.0425705  0.021978   0.01408 0.0414348  22031 2010       1
#> 8  71063 0.0275669  0.014652   0.01408 0.0268813  22031 2010       1
#> 9  71419 0.0105748  0.003663   0.00000 0.0101875  22031 2010       1
#> 10 71065 0.0001808  0.000000   0.00000 0.0001712  22031 2010       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 = c(151049, 103609, 52276),
                     county = c("24043", "24045", "24047"))

head(sample)
#>      pop county
#> 1 151049  24043
#> 2 103609  24045
#> 3  52276  24047

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)
#> 
Downloading:    [=================---------------------------------]    33% 1/3
Downloading:    [=================================-----------------]    67% 2/3
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#> 
#> 
#> * No method or cw_geoid_col specified: will just merge the datasets.
#>    county   zip res_ratio bus_ratio oth_ratio tot_ratio year quarter    pop
#> 1   24043 21740 4.154e-01 0.5855329 0.6520012 4.388e-01 2019       1 151049
#> 2   24043 21742 2.269e-01 0.2051453 0.2022609 2.242e-01 2019       1 151049
#> 3   24043 21795 7.331e-02 0.0689943 0.0498014 7.197e-02 2019       1 151049
#> 4   24043 21713 5.965e-02 0.0295690 0.0192484 5.558e-02 2019       1 151049
#> 5   24043 21783 4.411e-02 0.0126963 0.0033608 3.993e-02 2019       1 151049
#> 6   24043 21722 3.437e-02 0.0075175 0.0030553 3.094e-02 2019       1 151049
#> 7   24043 21750 2.974e-02 0.0414300 0.0262756 3.051e-02 2019       1 151049
#> 8   24043 21782 2.497e-02 0.0046776 0.0009166 2.237e-02 2019       1 151049
#> 9   24043 21756 2.179e-02 0.0018376 0.0009166 1.934e-02 2019       1 151049
#> 10  24043 21767 1.567e-02 0.0145339 0.0137489 1.550e-02 2019       1 151049
#> 11  24043 21719 1.093e-02 0.0030070 0.0265811 1.098e-02 2019       1 151049
#> 12  24043 21734 1.081e-02 0.0150351 0.0012221 1.073e-02 2019       1 151049
#> 13  24043 21758 1.118e-02 0.0030070 0.0006111 1.010e-02 2019       1 151049
#> 14  24043 21733 6.992e-03 0.0021717 0.0000000 6.318e-03 2019       1 151049
#> 15  24043 21711 6.903e-03 0.0015035 0.0000000 6.187e-03 2019       1 151049
#> 16  24043 21779 5.866e-03 0.0016706 0.0000000 5.289e-03 2019       1 151049
#> 17  24043 21715 5.481e-04 0.0001671 0.0000000 4.950e-04 2019       1 151049
#> 18  24043 21780 4.740e-04 0.0000000 0.0000000 4.168e-04 2019       1 151049
#> 19  24043 21769 2.370e-04 0.0003341 0.0000000 2.345e-04 2019       1 151049
#> 20  24043 21720 2.963e-05 0.0001671 0.0000000 3.908e-05 2019       1 151049
#> 21  24043 21746 0.000e+00 0.0005012 0.0000000 3.908e-05 2019       1 151049
#> 22  24043 21755 1.481e-05 0.0000000 0.0000000 1.303e-05 2019       1 151049
#> 23  24043 21781 0.000e+00 0.0001671 0.0000000 1.303e-05 2019       1 151049
#> 24  24043 21721 0.000e+00 0.0001671 0.0000000 1.303e-05 2019       1 151049
#> 25  24043 21741 0.000e+00 0.0001671 0.0000000 1.303e-05 2019       1 151049
#> 26  24045 21804 3.617e-01 0.2666006 0.4497751 3.574e-01 2019       1 103609
#> 27  24045 21801 2.563e-01 0.4428147 0.4677661 2.830e-01 2019       1 103609
#> 28  24045 21826 6.512e-02 0.0471754 0.0228636 6.152e-02 2019       1 103609
#> 29  24045 21875 6.282e-02 0.0243806 0.0356072 5.809e-02 2019       1 103609
#> 30  24045 21802 4.247e-02 0.1117939 0.0007496 4.670e-02 2019       1 103609
#> 31  24045 21830 3.543e-02 0.0059465 0.0018741 3.121e-02 2019       1 103609
#> 32  24045 21849 3.141e-02 0.0053518 0.0052474 2.784e-02 2019       1 103609
#> 33  24045 21850 2.981e-02 0.0114965 0.0089955 2.719e-02 2019       1 103609
#> 34  24045 21837 2.548e-02 0.0105055 0.0026237 2.306e-02 2019       1 103609
#> 35  24045 21874 2.180e-02 0.0073340 0.0022489 1.958e-02 2019       1 103609
#> 36  24045 21803 1.634e-02 0.0537166 0.0003748 1.893e-02 2019       1 103609
#> 37  24045 21822 1.714e-02 0.0043608 0.0007496 1.522e-02 2019       1 103609
#> 38  24045 21856 9.956e-03 0.0033697 0.0000000 8.897e-03 2019       1 103609
#> 39  24045 21861 8.827e-03 0.0001982 0.0011244 7.692e-03 2019       1 103609
#> 40  24045 21865 5.789e-03 0.0025768 0.0000000 5.228e-03 2019       1 103609
#> 41  24045 21840 5.112e-03 0.0011893 0.0000000 4.519e-03 2019       1 103609
#> 42  24045 21814 4.434e-03 0.0005946 0.0000000 3.881e-03 2019       1 103609
#> 43  24045 21852 0.000e+00 0.0003964 0.0000000 3.545e-05 2019       1 103609
#> 44  24045 21810 0.000e+00 0.0001982 0.0000000 1.772e-05 2019       1 103609
#> 45  24047 21842 5.302e-01 0.4559211 0.8379953 5.443e-01 2019       1  52276
#> 46  24047 21811 2.832e-01 0.2535088 0.0927739 2.696e-01 2019       1  52276
#> 47  24047 21851 5.434e-02 0.0881579 0.0417249 5.577e-02 2019       1  52276
#> 48  24047 21843 4.340e-02 0.1065789 0.0009324 4.491e-02 2019       1  52276
#> 49  24047 21863 3.859e-02 0.0660088 0.0205128 3.927e-02 2019       1  52276
#> 50  24047 21813 2.390e-02 0.0160088 0.0048951 2.221e-02 2019       1  52276
#> 51  24047 21841 7.577e-03 0.0041667 0.0004662 6.918e-03 2019       1  52276
#> 52  24047 21872 6.003e-03 0.0039474 0.0004662 5.528e-03 2019       1  52276
#> 53  24047 21864 4.920e-03 0.0017544 0.0000000 4.411e-03 2019       1  52276
#> 54  24047 21829 3.411e-03 0.0008772 0.0000000 3.036e-03 2019       1  52276
#> 55  24047 21804 1.804e-03 0.0004386 0.0000000 1.604e-03 2019       1  52276
#> 56  24047 21862 1.263e-03 0.0024123 0.0002331 1.275e-03 2019       1  52276
#> 57  24047 21822 1.361e-03 0.0000000 0.0000000 1.189e-03 2019       1  52276
#> 58  24047 21792 0.000e+00 0.0002193 0.0000000 1.432e-05 2019       1  52276

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)
#> 
Downloading:    [=================---------------------------------]    33% 1/3
Downloading:    [=================================-----------------]    67% 2/3
Downloading:    [==================================================]    100%    3/3
#> 
#> 
#> * Applying allocation method based on residential address percentage.
#>    county   zip res_ratio bus_ratio oth_ratio tot_ratio year quarter       pop
#> 1   24043 21740 4.154e-01 0.5855329 0.6520012 4.388e-01 2019       1 62751.933
#> 2   24043 21742 2.269e-01 0.2051453 0.2022609 2.242e-01 2019       1 34280.196
#> 3   24043 21795 7.331e-02 0.0689943 0.0498014 7.197e-02 2019       1 11073.212
#> 4   24043 21713 5.965e-02 0.0295690 0.0192484 5.558e-02 2019       1  9010.270
#> 5   24043 21783 4.411e-02 0.0126963 0.0033608 3.993e-02 2019       1  6663.170
#> 6   24043 21722 3.437e-02 0.0075175 0.0030553 3.094e-02 2019       1  5190.918
#> 7   24043 21750 2.974e-02 0.0414300 0.0262756 3.051e-02 2019       1  4492.829
#> 8   24043 21782 2.497e-02 0.0046776 0.0009166 2.237e-02 2019       1  3772.365
#> 9   24043 21756 2.179e-02 0.0018376 0.0009166 1.934e-02 2019       1  3291.310
#> 10  24043 21767 1.567e-02 0.0145339 0.0137489 1.550e-02 2019       1  2367.238
#> 11  24043 21719 1.093e-02 0.0030070 0.0265811 1.098e-02 2019       1  1651.249
#> 12  24043 21734 1.081e-02 0.0150351 0.0012221 1.073e-02 2019       1  1633.349
#> 13  24043 21758 1.118e-02 0.0030070 0.0006111 1.010e-02 2019       1  1689.286
#> 14  24043 21733 6.992e-03 0.0021717 0.0000000 6.318e-03 2019       1  1056.083
#> 15  24043 21711 6.903e-03 0.0015035 0.0000000 6.187e-03 2019       1  1042.659
#> 16  24043 21779 5.866e-03 0.0016706 0.0000000 5.289e-03 2019       1   886.036
#> 17  24043 21715 5.481e-04 0.0001671 0.0000000 4.950e-04 2019       1    82.786
#> 18  24043 21780 4.740e-04 0.0000000 0.0000000 4.168e-04 2019       1    71.599
#> 19  24043 21769 2.370e-04 0.0003341 0.0000000 2.345e-04 2019       1    35.799
#> 20  24043 21720 2.963e-05 0.0001671 0.0000000 3.908e-05 2019       1     4.475
#> 21  24043 21746 0.000e+00 0.0005012 0.0000000 3.908e-05 2019       1     0.000
#> 22  24043 21755 1.481e-05 0.0000000 0.0000000 1.303e-05 2019       1     2.237
#> 23  24043 21781 0.000e+00 0.0001671 0.0000000 1.303e-05 2019       1     0.000
#> 24  24043 21721 0.000e+00 0.0001671 0.0000000 1.303e-05 2019       1     0.000
#> 25  24043 21741 0.000e+00 0.0001671 0.0000000 1.303e-05 2019       1     0.000
#> 26  24045 21804 3.617e-01 0.2666006 0.4497751 3.574e-01 2019       1 37479.352
#> 27  24045 21801 2.563e-01 0.4428147 0.4677661 2.830e-01 2019       1 26557.357
#> 28  24045 21826 6.512e-02 0.0471754 0.0228636 6.152e-02 2019       1  6746.751
#> 29  24045 21875 6.282e-02 0.0243806 0.0356072 5.809e-02 2019       1  6508.531
#> 30  24045 21802 4.247e-02 0.1117939 0.0007496 4.670e-02 2019       1  4400.703
#> 31  24045 21830 3.543e-02 0.0059465 0.0018741 3.121e-02 2019       1  3671.152
#> 32  24045 21849 3.141e-02 0.0053518 0.0052474 2.784e-02 2019       1  3254.265
#> 33  24045 21850 2.981e-02 0.0114965 0.0089955 2.719e-02 2019       1  3088.362
#> 34  24045 21837 2.548e-02 0.0105055 0.0026237 2.306e-02 2019       1  2639.571
#> 35  24045 21874 2.180e-02 0.0073340 0.0022489 1.958e-02 2019       1  2258.843
#> 36  24045 21803 1.634e-02 0.0537166 0.0003748 1.893e-02 2019       1  1693.069
#> 37  24045 21822 1.714e-02 0.0043608 0.0007496 1.522e-02 2019       1  1776.021
#> 38  24045 21856 9.956e-03 0.0033697 0.0000000 8.897e-03 2019       1  1031.581
#> 39  24045 21861 8.827e-03 0.0001982 0.0011244 7.692e-03 2019       1   914.597
#> 40  24045 21865 5.789e-03 0.0025768 0.0000000 5.228e-03 2019       1   599.806
#> 41  24045 21840 5.112e-03 0.0011893 0.0000000 4.519e-03 2019       1   529.616
#> 42  24045 21814 4.434e-03 0.0005946 0.0000000 3.881e-03 2019       1   459.426
#> 43  24045 21852 0.000e+00 0.0003964 0.0000000 3.545e-05 2019       1     0.000
#> 44  24045 21810 0.000e+00 0.0001982 0.0000000 1.772e-05 2019       1     0.000
#> 45  24047 21842 5.302e-01 0.4559211 0.8379953 5.443e-01 2019       1 27719.003
#> 46  24047 21811 2.832e-01 0.2535088 0.0927739 2.696e-01 2019       1 14804.331
#> 47  24047 21851 5.434e-02 0.0881579 0.0417249 5.577e-02 2019       1  2840.491
#> 48  24047 21843 4.340e-02 0.1065789 0.0009324 4.491e-02 2019       1  2268.620
#> 49  24047 21863 3.859e-02 0.0660088 0.0205128 3.927e-02 2019       1  2017.408
#> 50  24047 21813 2.390e-02 0.0160088 0.0048951 2.221e-02 2019       1  1249.199
#> 51  24047 21841 7.577e-03 0.0041667 0.0004662 6.918e-03 2019       1   396.108
#> 52  24047 21872 6.003e-03 0.0039474 0.0004662 5.528e-03 2019       1   313.800
#> 53  24047 21864 4.920e-03 0.0017544 0.0000000 4.411e-03 2019       1   257.213
#> 54  24047 21829 3.411e-03 0.0008772 0.0000000 3.036e-03 2019       1   178.334
#> 55  24047 21804 1.804e-03 0.0004386 0.0000000 1.604e-03 2019       1    94.311
#> 56  24047 21862 1.263e-03 0.0024123 0.0002331 1.275e-03 2019       1    66.018
#> 57  24047 21822 1.361e-03 0.0000000 0.0000000 1.189e-03 2019       1    71.162
#> 58  24047 21792 0.000e+00 0.0002193 0.0000000 1.432e-05 2019       1     0.000

Parameters

query: character double integer or numeric

         The query argument name is the first geoid in the function call.
         For example, zip-county would have the ‘zip’ argument name as the query.

         Functions #1-5 and #11 must specify a 5 digit zip code in the zip function
         argument.

         * zip = 22031

         Function #6 must specify an 11 digit number consisting of state FIPS + county
         FIPS + tract code tract in the tract function argument.

         * tract = 51059461700

         Function #7 must specify a 5 digit county fips code in the county function
         argument.

         * county = 51600

         Function #8 must specify a micropolitan or metropolitan CBSA code in the
         cbsa function argument.

         * cbsa = 10380

         Function #9 must specify a metropolitan CBSA division code in the
         cbsadiv function argument.

         * cbsadiv = 35614

         I recommend typing hud_metropolitan(“MD”) to see a list of metropolitan CBSA in
         Maryland. For example, a code of METRO22900N40079 needs 22900 inside as the input
         argument.

         Function #10 must be a 2 digit state fips code + 2 digit congressional district
         code in the cd function argument.

         * cd = 7200

         Function #12 must be a 10 digit number consisting of 5 digit county fips code + 5 digit
         county sub district code in the countysub argument.

         * countysub = 4606720300

year: character double integer or numeric

         Year range of the data to retrieve: defaults to the current year.

         * year = c(2019, 2018, 2021)
         * year = c(2016)
         * year = 2021

quarter: character double integer or numeric

        The quarters in the year to retrieve: defaults to the first
        quarter.

        * quarter = c(1,2,3,4)
        * quarter = c(1)
        * quarter = 4

minimal: logical

If TRUE, returns just the intersecting geoids that are
crosswalked with the queried geoid.

key: character

The API key provided by HUD USER.

* key = “wqokqo2138jdi13wfwwfwcytjyr”

Returns

Data Field	Description
zip/county/fipstract/cbsa/cbsadiv/cd/countysub	The geoid to query for
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 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/fipstract/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.