Example 5: Geocoding Addresses and Spatial Applications

Vinh Nguyen

2023年10月05日

Introduction: Geocoding

Location data like student addresses are a rich source of information that could be leveraged for institutions to know where students are concentrated in the surrounding areas, the distance to campus for each student, the commute time to campus for each student, and population-level attributes of geographical areas provided by the U.S. Census Bureau (e.g., the American Community Survey or ACS). Geocoding is the act of converting an address to its geographical coordinates (longitude and latitude). It is a critical first step to make addresses more useful for analysis by institutional researchers.

In this vignette, we cover how to geocode using the tidygeocoder package in R. Although many options exist for geocoding, we focus on this particular package in R for several reasons:

  1. Many analysts use R to process or analyze data, so it is convenient to stay in the same environment.
  2. tidygeocoder provides a unified interface for geocoding with many services on the backend. The user specifies the appropriate service depending on their needs as services differ on match rates, costs, limits on the free tier (if available), usage limitations (queries per second or total queries in a time period), and data privacy / retention policies.
  3. tidygeocoder supports the Nominatim service, a geocoding service based on OpenStreetMap (OSM) data, a crowdsourced open data platform. Moreover, the user could specify a custom API URL, which is useful for geocoding with a local Nominatim server instead of the public server. This is especially critical for institutional researchers as address data never leave the local network, maintaining student privacy and limiting potential data security issues.

Besides geocoding, this vignette also illustrates a few spatial applications of the geocoded data.

Installation and Setup

tidygeocoder and other relevant packages in R

The tidygeocoder and other relevant packages could be installed by running the following line in an R console:

 # Installation
 install.packages(c('tidygeocoder' # for geocoding
 , 'maps' # map data for Visualizations
 , 'geodist' # calculating distance
 , 'sf' # interface for spatial data (e.g., shapefiles)
 , 'tigris' # US Census shapefiles
 , 'tidycensus' # query US Census data
 ) 
 , repos='https://cran.r-project.org')

Example: Geocoding Addresses

Let’s load some necessary packages and load some address data provided by the IRexamples package.

 # Load packages
 library(IRexamples)
 library(tidygeocoder)
 library(dplyr)
 
 # Load data
 data(ccc_list)
 data(uc_list)
 
 dim(ccc_list)
 ## [1] 115 5
 
 dim(uc_list)
 ## [1] 10 2
 
 head(ccc_list)
 ## College District
 ## 1 ALLAN HANCOCK COLLEGE Allan Hancock Joint Community College District
 ## 2 AMERICAN RIVER COLLEGE Los Rios Community College District
 ## 3 ANTELOPE VALLEY COLLEGE Antelope Valley Community College District
 ## 4 BAKERSFIELD COLLEGE Kern Community College District
 ## 5 BARSTOW COMMUNITY COLLEGE Barstow Community College District
 ## 6 BERKELEY CITY COLLEGE Peralta Community College District
 ## Address Phone
 ## 1 800 South College Drive, Santa Maria, CA 93454-6368 805.922.6966
 ## 2 4700 College Oak Drive, Sacramento, CA 95841-4286 916.484.8011
 ## 3 3041 West Avenue K, Lancaster, CA 93536-5426 661.722.6300
 ## 4 1801 Panorama Drive, Bakersfield, CA 93305-1299 661.395.4011
 ## 5 2700 Barstow Road, Barstow, CA 92311-6699 760.252.2411
 ## 6 2050 Center Street, Berkeley, CA 94704-1205 510.981.2800
 ## Website
 ## 1 www.hancockcollege.edu
 ## 2 www.arc.losrios.edu
 ## 3 www.avc.edu
 ## 4 www.bakersfieldcollege.edu
 ## 5 www.barstow.edu
 ## 6 www.berkeleycitycollege.edu
 
 head(uc_list)
 ## # A tibble: 6 x 2
 ## University Address 
 ## <chr> <chr> 
 ## 1 UC Berkeley Berkeley, CA 
 ## 2 UC Davis 1 Shields Ave, Davis, CA 95616 
 ## 3 UC Irvine 510 E Peltason Dr. Irvine, California 92697-5700
 ## 4 UC Los Angeles Los Angeles, CA 90095 
 ## 5 UC Merced 5200 Lake Rd, Merced, CA 95343 
 ## 6 UC Riverside 900 University Ave, Riverside, CA 92521

The ccc_list and uc_list data sets contain lists of addresses for the California Community Colleges and the Universities of California, respectively.

Next, we geocode the data with the public Nominatim service. To use the local server, uncomment the api_url and _min_time arguments.

ccc_list_geo <- ccc_list %>%
 geocode(address=Address
 , method='osm' # nominatim
 # , api_url='http://localhost:8080/' # Uncomment this if using a local Nominatim server on the same computer that's running R; default uses the public online service; can also specify the address to a local server running on the network
 # , min_time=0.1 # If using local server, allow 1 query per 0.1 seconds. Otherwise, default is 1 query per second on the public server (see ?min_time_reference)
 )
 dim(ccc_list_geo)
 ## [1] 115 7
 
 names(ccc_list_geo)
 ## [1] "College" "District" "Address" "Phone" "Website" "lat" "long"
 
ccc_list_geo %>%
 select(College, lat, long) %>%
 print(n=120)
 ## # A tibble: 115 x 3
 ## College lat long
 ## <chr> <dbl> <dbl>
 ## 1 ALLAN HANCOCK COLLEGE 34.9 -120.
 ## 2 AMERICAN RIVER COLLEGE 38.6 -121.
 ## 3 ANTELOPE VALLEY COLLEGE 34.7 -118.
 ## 4 BAKERSFIELD COLLEGE 35.4 -119.
 ## 5 BARSTOW COMMUNITY COLLEGE 34.9 -117.
 ## 6 BERKELEY CITY COLLEGE 37.9 -122.
 ## 7 BUTTE COLLEGE 39.6 -122.
 ## 8 CABRILLO COLLEGE 37.0 -122.
 ## 9 CAÑADA COLLEGE 37.4 -122.
 ## 10 CERRITOS COLLEGE 33.9 -118.
 ## 11 CERRO COSO COMMUNITY COLLEGE 35.6 -118.
 ## 12 CHABOT COLLEGE 37.6 -122.
 ## 13 CHAFFEY COLLEGE 34.1 -118.
 ## 14 CITRUS COLLEGE 34.1 -118.
 ## 15 CITY COLLEGE OF SAN FRANCISCO NA NA 
 ## 16 CLOVIS COMMUNITY COLLEGE 36.9 -120.
 ## 17 COASTLINE COMMUNITY COLLEGE 33.7 -118.
 ## 18 COLLEGE OF ALAMEDA 37.8 -122.
 ## 19 COLLEGE OF MARIN 38.0 -123.
 ## 20 COLLEGE OF SAN MATEO 37.5 -122.
 ## 21 COLLEGE OF THE CANYONS NA NA 
 ## 22 COLLEGE OF THE DESERT 33.7 -116.
 ## 23 COLLEGE OF THE REDWOODS NA NA 
 ## 24 COLLEGE OF THE SEQUOIAS 36.3 -119.
 ## 25 COLLEGE OF THE SISKIYOUS 41.4 -122.
 ## 26 COLUMBIA COLLEGE NA NA 
 ## 27 COMPTON COLLEGE 33.9 -118.
 ## 28 CONTRA COSTA COLLEGE 38.0 -122.
 ## 29 COPPER MOUNTAIN COLLEGE NA NA 
 ## 30 COSUMNES RIVER COLLEGE 38.4 -121.
 ## 31 CRAFTON HILLS COLLEGE 34.0 -117.
 ## 32 CUESTA COLLEGE NA NA 
 ## 33 CUYAMACA COLLEGE 32.7 -117.
 ## 34 CYPRESS COLLEGE 33.8 -118.
 ## 35 DEANZA COLLEGE 37.3 -122.
 ## 36 DIABLO VALLEY COLLEGE 38.0 -122.
 ## 37 EAST LOS ANGELES COLLEGE NA NA 
 ## 38 EL CAMINO COLLEGE 33.9 -118.
 ## 39 EVERGREEN VALLEY COLLEGE 37.3 -122.
 ## 40 FEATHER RIVER COLLEGE 40.0 -121.
 ## 41 FOLSOM LAKE COLLEGE 38.7 -121.
 ## 42 FOOTHILL COLLEGE 37.4 -122.
 ## 43 FRESNO CITY COLLEGE 36.8 -120.
 ## 44 FULLERTON COLLEGE 33.9 -118.
 ## 45 GAVILAN COLLEGE 37.0 -122.
 ## 46 GLENDALE COMMUNITY COLLEGE 34.2 -118.
 ## 47 GOLDEN WEST COLLEGE 33.7 -118.
 ## 48 GROSSMONT COLLEGE 32.8 -117.
 ## 49 HARTNELL COLLEGE 36.7 -122.
 ## 50 IMPERIAL VALLEY COLLEGE 32.8 -116.
 ## 51 IRVINE VALLEY COLLEGE 33.7 -118.
 ## 52 LAKE TAHOE COMMUNITY COLLEGE NA NA 
 ## 53 LANEY COLLEGE 37.8 -122.
 ## 54 LAS POSITAS COLLEGE 37.7 -122.
 ## 55 LASSEN COLLEGE NA NA 
 ## 56 LONG BEACH CITY COLLEGE 33.8 -118.
 ## 57 LOS ANGELES CITY COLLEGE 34.1 -118.
 ## 58 LOS ANGELES HARBOR COLLEGE NA NA 
 ## 59 LOS ANGELES MISSION COLLEGE 34.3 -118.
 ## 60 LOS ANGELES PIERCE COLLEGE 34.2 -119.
 ## 61 LOS ANGELES SOUTHWEST COLLEGE 33.9 -118.
 ## 62 LOS ANGELES TRADE-TECH COLLEGE 34.0 -118.
 ## 63 LOS ANGELES VALLEY COLLEGE 34.2 -118.
 ## 64 LOS MEDANOS COLLEGE 38.0 -122.
 ## 65 MADERA COLLEGE 36.9 -120.
 ## 66 MENDOCINO COLLEGE 39.2 -123.
 ## 67 MERCED COLLEGE 37.3 -120.
 ## 68 MERRITT COLLEGE 37.8 -122.
 ## 69 MIRACOSTA COLLEGE 33.2 -117.
 ## 70 MISSION COLLEGE 37.4 -122.
 ## 71 MODESTO JUNIOR COLLEGE 37.7 -121.
 ## 72 MONTEREY PENINSULA COLLEGE 36.6 -122.
 ## 73 MOORPARK COLLEGE 34.3 -119.
 ## 74 MORENO VALLEY COLLEGE 33.9 -117.
 ## 75 MT. SAN ANTONIO COLLEGE 34.0 -118.
 ## 76 MT. SAN JACINTO COLLEGE 33.8 -117.
 ## 77 NAPA VALLEY COLLEGE 38.2 -122.
 ## 78 NORCO COLLEGE NA NA 
 ## 79 OHLONE COLLEGE 37.5 -122.
 ## 80 ORANGE COAST COLLEGE NA NA 
 ## 81 OXNARD COLLEGE 34.2 -119.
 ## 82 PALO VERDE COLLEGE NA NA 
 ## 83 PALOMAR COLLEGE 33.2 -117.
 ## 84 PASADENA CITY COLLEGE 34.1 -118.
 ## 85 PORTERVILLE COLLEGE 36.0 -119.
 ## 86 REEDLEY COLLEGE 36.6 -119.
 ## 87 RIO HONDO COLLEGE 34.0 -118.
 ## 88 RIVERSIDE CITY COLLEGE 34.0 -117.
 ## 89 SACRAMENTO CITY COLLEGE 38.5 -121.
 ## 90 SADDLEBACK COLLEGE 33.6 -118.
 ## 91 SAN BERNARDINO VALLEY COLLEGE 34.1 -117.
 ## 92 SAN DIEGO CITY COLLEGE 32.7 -117.
 ## 93 SAN DIEGO MESA COLLEGE 32.8 -117.
 ## 94 SAN DIEGO MIRAMAR COLLEGE 32.9 -117.
 ## 95 SAN JOAQUIN DELTA COLLEGE 38.0 -121.
 ## 96 SAN JOSE CITY COLLEGE 37.3 -122.
 ## 97 SANTA ANA COLLEGE 33.8 -118.
 ## 98 SANTA BARBARA CITY COLLEGE 34.4 -120.
 ## 99 SANTA MONICA COLLEGE NA NA 
 ## 100 SANTA ROSA JUNIOR COLLEGE 38.5 -123.
 ## 101 SANTIAGO CANYON COLLEGE 33.8 -118.
 ## 102 SHASTA COLLEGE NA NA 
 ## 103 SIERRA COLLEGE 38.8 -121.
 ## 104 SKYLINE COLLEGE 37.6 -122.
 ## 105 SOLANO COMMUNITY COLLEGE 38.2 -122.
 ## 106 SOUTHWESTERN COLLEGE 32.6 -117.
 ## 107 TAFT COLLEGE NA NA 
 ## 108 VENTURA COLLEGE 34.3 -119.
 ## 109 VICTOR VALLEY COLLEGE 34.5 -117.
 ## 110 WEST HILLS COLLEGE COALINGA NA NA 
 ## 111 WEST HILLS COLLEGE LEMOORE 36.3 -120.
 ## 112 WEST LOS ANGELES COLLEGE 34.0 -118.
 ## 113 WEST VALLEY COLLEGE 37.3 -122.
 ## 114 WOODLAND COMMUNITY COLLEGE 38.7 -122.
 ## 115 YUBA COLLEGE NA NA 
 
 # Match rate
ccc_list_geo %>%
 summarize(N_Missing=sum(is.na(lat)), Match_Rate=mean(!is.na(lat)))
 ## # A tibble: 1 x 2
 ## N_Missing Match_Rate
 ## <int> <dbl>
 ## 1 18 0.843
 
 # Non-matches
ccc_list_geo %>%
 filter(is.na(long)) %>%
 select(College, Address) %>% 
 as.data.frame
 ## College
 ## 1 CITY COLLEGE OF SAN FRANCISCO
 ## 2 COLLEGE OF THE CANYONS
 ## 3 COLLEGE OF THE REDWOODS
 ## 4 COLUMBIA COLLEGE
 ## 5 COPPER MOUNTAIN COLLEGE
 ## 6 CUESTA COLLEGE
 ## 7 EAST LOS ANGELES COLLEGE
 ## 8 LAKE TAHOE COMMUNITY COLLEGE
 ## 9 LASSEN COLLEGE
 ## 10 LOS ANGELES HARBOR COLLEGE
 ## 11 NORCO COLLEGE
 ## 12 ORANGE COAST COLLEGE
 ## 13 PALO VERDE COLLEGE
 ## 14 SANTA MONICA COLLEGE
 ## 15 SHASTA COLLEGE
 ## 16 TAFT COLLEGE
 ## 17 WEST HILLS COLLEGE COALINGA
 ## 18 YUBA COLLEGE
 ## Address
 ## 1 50 Phelan Avenue E200, San Francisco, CA 94112-1898
 ## 2 26455 N. Rockwell Canyon Road, Santa Clarita, CA 91355-1899
 ## 3 7351 Tompkins Hill Road, Eureka, CA 95501-9301
 ## 4 11600 Columbia College Drive, Sonora, CA 95370-8518
 ## 5 6162 Rotary Way, Joshua Tree, CA 92252-6100
 ## 6 PO Box 8106, San Luis Obispo, CA 93403-8106
 ## 7 1301 Avenida Cesar Chavez, Monterey Park, CA 91754-6099
 ## 8 1 College Drive, So. Lake Tahoe, CA 96150-4524
 ## 9 PO Box 3000, Susanville, CA 96130-3000
 ## 10 1111 Figueroa Place, Wilmington, CA 90744-2397
 ## 11 2001 Third Street, Norco, CA 92860-2600
 ## 12 PO Box 5005, Costa Mesa, CA 92628-5005
 ## 13 One College Drive, Blythe, CA 92225-1118
 ## 14 1900 Pico Boulevard, Santa Monica, CA 90405-1628
 ## 15 PO Box 496006, Redding, CA 96049-6006
 ## 16 29 Cougar Ct., Taft, CA 93268-4217
 ## 17 300 Cherry Lane, Coalinga, CA 93210-1301
 ## 18 2088 North Beale Road, Marysville, CA 95901-7699

There is an 84% match rate for the ccc_list data using OSM.

The geocode function provides a cascade method that one could use to leverage multiple geocoding services: when the first service does not return coordinates for an address, a second service is used as a second attempt, with additional services used subsequently should more be specified. The following code illustrates this feature with the census method as backup:

ccc_list_geo2 <- ccc_list %>%
 geocode(address=Address
 , method='cascade'
 , cascade_order=c('osm', 'census')
 )
 
ccc_list_geo2 %>%
 summarize(N_Missing=sum(is.na(lat)), Match_Rate=mean(!is.na(lat)))
 ## # A tibble: 1 x 2
 ## N_Missing Match_Rate
 ## <int> <dbl>
 ## 1 7 0.939
 
ccc_list_geo2 %>%
 filter(is.na(long)) %>%
 select(College, Address, lat, long) %>% 
 as.data.frame
 ## College Address
 ## 1 CUESTA COLLEGE PO Box 8106, San Luis Obispo, CA 93403-8106
 ## 2 LAKE TAHOE COMMUNITY COLLEGE 1 College Drive, So. Lake Tahoe, CA 96150-4524
 ## 3 LASSEN COLLEGE PO Box 3000, Susanville, CA 96130-3000
 ## 4 ORANGE COAST COLLEGE PO Box 5005, Costa Mesa, CA 92628-5005
 ## 5 PALO VERDE COLLEGE One College Drive, Blythe, CA 92225-1118
 ## 6 SHASTA COLLEGE PO Box 496006, Redding, CA 96049-6006
 ## 7 TAFT COLLEGE 29 Cougar Ct., Taft, CA 93268-4217
 ## lat long
 ## 1 NA NA
 ## 2 NA NA
 ## 3 NA NA
 ## 4 NA NA
 ## 5 NA NA
 ## 6 NA NA
 ## 7 NA NA

With the Census as the second service, the match rate increased to 94%. One thing to note is that cascade method would not work with a local Nominatim server. To use a local Nominatim server, the user should geocode using the local server first, then identify the non-matched addresses for geocoding using a secondary service. This is illustrated as follows:

ccc_list_geo2_manual <- ccc_list_geo %>%
 filter(is.na(lat)) %>% # identify non-matches
 select(-lat, -long) %>% # remove these columns
 geocode(address=Address
 , method='census' # geocode non-matches with census method
 ) %>%
 rbind(ccc_list_geo %>% filter(!is.na(lat))) # Append matches from 1st run
 dim(ccc_list_geo2_manual)
 ## [1] 115 7
 
ccc_list_geo2_manual %>%
 summarize(N_Missing=sum(is.na(lat)), Match_Rate=mean(!is.na(lat)))
 ## # A tibble: 1 x 2
 ## N_Missing Match_Rate
 ## <int> <dbl>
 ## 1 7 0.939

The manual approach yields the same results as the cascade method, but is preferred when a local Nominatim server is used.

Example: Visualizing Locations

With the geocoded results, we can visualize the locations of the California community colleges on a map:

 library(ggplot2)
 library(maps)
 library(ggrepel)
 
 ggplot(ccc_list_geo2, aes(long, lat), color = "grey99") +
 borders(database='state', region='california') + # See ?map
 geom_point() +
 # geom_label_repel(aes(label = College)) +
 theme_void()
 #dev.copy(png, filename='./images/ccc_map.png', width=3, height=5, units='in', res=150)
 #dev.off()
 
 # Concentration
 # https://stackoverflow.com/questions/13316185/r-convert-zipcode-or-lat-long-to-county

The following shows how to visualize the map of the US:

 ggplot(ccc_list_geo2, aes(long, lat), color = "grey99") +
 borders(database='county', region='california') + # See ?map
 geom_point() +
 # geom_label_repel(aes(label = College)) +
 theme_void()
 #dev.copy(png, filename='./images/ccc_map_us.png', width=9, height=5, units='in', res=150)
 #dev.off()

With the geographical coordinates, the geocoded data set could also be exported and then imported in other platforms for visualization such as Tableau, ArcGIS, or QGIS.

Example: Calculating Distance

With geocoded data, distance can be calculated using the geodist in R. In the following example, we first geocode the location of all the UC’s, then calculate the distance to each UC for each community college.

 # Geocode UC's
uc_list_geo <- uc_list %>%
 geocode(address=Address
 , method='cascade'
 , cascade_order=c('osm', 'census')
 )
uc_list_geo %>%
 as.data.frame
 ## University Address lat
 ## 1 UC Berkeley Berkeley, CA 37.87535
 ## 2 UC Davis 1 Shields Ave, Davis, CA 95616 NA
 ## 3 UC Irvine 510 E Peltason Dr. Irvine, California 92697-5700 33.64163
 ## 4 UC Los Angeles Los Angeles, CA 90095 34.07088
 ## 5 UC Merced 5200 Lake Rd, Merced, CA 95343 37.34685
 ## 6 UC Riverside 900 University Ave, Riverside, CA 92521 33.96371
 ## 7 UC San Diego 9500 Gilman Dr, La Jolla, CA 92093 32.87615
 ## 8 UC San Francisco 505 Parnassus Ave, San Francisco, CA 94143 37.76307
 ## 9 UC Santa Barbara Santa Barbara, CA 93106 34.42213
 ## 10 UC Santa Cruz 1156 High St, Santa Cruz, CA 95064 36.97738
 ## long geo_method
 ## 1 -122.2396 osm
 ## 2 NA census
 ## 3 -117.8456 osm
 ## 4 -118.4468 osm
 ## 5 -120.4326 osm
 ## 6 -117.3398 osm
 ## 7 -117.2432 osm
 ## 8 -122.4574 osm
 ## 9 -119.7027 osm
 ## 10 -122.0549 osm
 
 # Load packages
 library(geodist)
 library(stringr)
 
 # Calculate distance between each CC to each UC
dist_mat <- geodist(x=ccc_list_geo, y=uc_list_geo, measure='haversine') / 1609.34 # results are in meters; convert to miles
 dim(dist_mat)
 ## [1] 115 10
 
 # Append results as new columns in original data frame
ccc_list_geo[paste0('Dist: ', uc_list$University)] <- dist_mat
 dim(ccc_list_geo)
 ## [1] 115 17
 
 # Print first few rows
 head(ccc_list_geo) %>% as.data.frame
 ## College District
 ## 1 ALLAN HANCOCK COLLEGE Allan Hancock Joint Community College District
 ## 2 AMERICAN RIVER COLLEGE Los Rios Community College District
 ## 3 ANTELOPE VALLEY COLLEGE Antelope Valley Community College District
 ## 4 BAKERSFIELD COLLEGE Kern Community College District
 ## 5 BARSTOW COMMUNITY COLLEGE Barstow Community College District
 ## 6 BERKELEY CITY COLLEGE Peralta Community College District
 ## Address Phone
 ## 1 800 South College Drive, Santa Maria, CA 93454-6368 805.922.6966
 ## 2 4700 College Oak Drive, Sacramento, CA 95841-4286 916.484.8011
 ## 3 3041 West Avenue K, Lancaster, CA 93536-5426 661.722.6300
 ## 4 1801 Panorama Drive, Bakersfield, CA 93305-1299 661.395.4011
 ## 5 2700 Barstow Road, Barstow, CA 92311-6699 760.252.2411
 ## 6 2050 Center Street, Berkeley, CA 94704-1205 510.981.2800
 ## Website lat long Dist: UC Berkeley
 ## 1 www.hancockcollege.edu 34.94327 -120.4231 226.612872
 ## 2 www.arc.losrios.edu 38.64884 -121.3465 72.218347
 ## 3 www.avc.edu 34.67492 -118.1874 316.272955
 ## 4 www.bakersfieldcollege.edu 35.40877 -118.9720 248.964004
 ## 5 www.barstow.edu 34.87101 -117.0258 356.981425
 ## 6 www.berkeleycitycollege.edu 37.86980 -122.2697 1.685213
 ## Dist: UC Davis Dist: UC Irvine Dist: UC Los Angeles Dist: UC Merced
 ## 1 NA 172.62547 127.78918 166.2585
 ## 2 NA 397.65959 355.42338 102.9175
 ## 3 NA 74.10235 44.33047 223.4554
 ## 4 NA 138.06228 97.23803 156.8008
 ## 5 NA 97.09591 98.12937 256.0385
 ## 6 NA 383.57060 338.82270 106.9657
 ## Dist: UC Riverside Dist: UC San Diego Dist: UC San Francisco
 ## 1 188.45515 231.8548 225.56507
 ## 2 393.48090 460.8291 86.02155
 ## 3 69.02799 135.7479 319.94485
 ## 4 136.41495 201.1988 252.93543
 ## 5 65.26556 138.5493 362.74946
 ## 6 386.23756 446.7000 12.63799
 ## Dist: UC Santa Barbara Dist: UC Santa Cruz
 ## 1 54.57738 167.75483
 ## 2 306.29097 121.92436
 ## 3 88.08124 269.05504
 ## 4 79.84264 203.42795
 ## 5 155.45306 317.08630
 ## 6 278.23339 62.84623
 
 # Distance to UCI from colleges with Orange in it's district's name
ccc_list_geo %>%
 filter(str_detect(District, 'Orange')) %>%
 select(College, `Dist: UC Irvine`)
 ## # A tibble: 4 x 2
 ## College `Dist: UC Irvine`
 ## <chr> <dbl>
 ## 1 CYPRESS COLLEGE 15.7 
 ## 2 FULLERTON COLLEGE 16.7 
 ## 3 IRVINE VALLEY COLLEGE 4.71
 ## 4 SADDLEBACK COLLEGE 12.0

Example: Data Augmentation with Census Data

The US Census Bureau offers a lot of useful geographical-level information based on data collected from the Decennial Census (entire population, every 10 years) and the American Community Survey (ACS; sample of population, every month). For example, for a given geographical unit, one could obtain the population size, age group sizes, income levels, and education attainment of the area. Such information could be used to describe the service areas of a college or impute the information about students if such information is unavailable to the institution (e.g., income or socioeconomic level). The augmented data could be used to describe the student population or service areas in grant applications, or could be used as explanatory variables in a statistical model.

Before obtaining the needed information from the US Census, we must first determine the geographical area that an address corresponds, namely the census tract. Census tracts are granular geographical units determined by the US Census Bureau for which statistical information are summarized on (e.g., population size). Previously, we illustrated how to geocode an address. From these geographical coordinates, we can determine the corresponding census tracts these addresses belong to. To do so, we first download the California census tract shapefiles via the tigris R package. From there, we could do a spatial join between the geographical coordinates (as spatial points) and the census tracts (areas) via the sf R package. If a location or point is in a particular tract, a join is returned. Once an address have been linked to a census tract, information from the US Census could be queried using the tidycensus R package.

The following code illustrates how to link geographical coordinates to census tracts using the addresses found in the uc_list data as sample input:

 # Load packages
 # https://stackoverflow.com/a/52260988/199140
 library(tigris) # Download shapefiles from Census
 library(sf) # Handle shapefiles; load after tigris
 library(tidycensus)
 
 # Get CA shapefiles
tract_CA <- tracts(state='CA', class='sf')
 ## Using FIPS code '06' for state 'CA'
 ## |======================================================================| 100%
 
 # Print
 head(tract_CA)
 ## Simple feature collection with 6 features and 12 fields
 ## Geometry type: MULTIPOLYGON
 ## Dimension: XY
 ## Bounding box: xmin: -118.5715 ymin: 34.14174 xmax: -118.4836 ymax: 34.18716
 ## Geodetic CRS: NAD83
 ## STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC
 ## 1 06 037 139301 06037139301 1393.01 Census Tract 1393.01 G5020
 ## 2 06 037 139302 06037139302 1393.02 Census Tract 1393.02 G5020
 ## 3 06 037 139502 06037139502 1395.02 Census Tract 1395.02 G5020
 ## 4 06 037 139600 06037139600 1396 Census Tract 1396 G5020
 ## 5 06 037 139701 06037139701 1397.01 Census Tract 1397.01 G5020
 ## 6 06 037 139801 06037139801 1398.01 Census Tract 1398.01 G5020
 ## FUNCSTAT ALAND AWATER INTPTLAT INTPTLON
 ## 1 S 2865657 0 +34.1781538 -118.5581265
 ## 2 S 338289 0 +34.1767230 -118.5383655
 ## 3 S 1047548 0 +34.1628402 -118.5263110
 ## 4 S 2477482 0 +34.1640599 -118.5101001
 ## 5 S 3396396 2411 +34.1574290 -118.4954117
 ## 6 S 3665744 0 +34.1527206 -118.5455805
 ## geometry
 ## 1 MULTIPOLYGON (((-118.5715 3...
 ## 2 MULTIPOLYGON (((-118.5407 3...
 ## 3 MULTIPOLYGON (((-118.5322 3...
 ## 4 MULTIPOLYGON (((-118.5186 3...
 ## 5 MULTIPOLYGON (((-118.5098 3...
 ## 6 MULTIPOLYGON (((-118.5567 3...
 
 # Convert coordinates into geospatial data type (sf)
uc_points <- uc_list_geo %>%
 filter(!is.na(lat)) %>% # remove missing
 st_as_sf(coords=c('long', 'lat'), crs=st_crs(tract_CA))
 
 # Determine which census tract each point is located in
uc_points_tract <- st_join(uc_points, tract_CA)
 dim(uc_points_tract)
 ## [1] 9 16
 
 names(uc_points_tract)
 ## [1] "University" "Address" "geo_method" "geometry" "STATEFP" 
 ## [6] "COUNTYFP" "TRACTCE" "GEOID" "NAME" "NAMELSAD" 
 ## [11] "MTFCC" "FUNCSTAT" "ALAND" "AWATER" "INTPTLAT" 
 ## [16] "INTPTLON"

GEOID is the unique identifier of a census tract, and as you might infer from reviewing some the values, it is a concatenation of STATEFP (state identifier), COUNTYFP (county identifier), and TRACTCE (tract identifier). Once GEOID is known, we could leverage the get_decennial and get_acs functions from the tidycensus package to query the data elements of interest, as described in the package’s basic usage vignette and the spatial data vignette.

To query data from the Census API, one must obtain a free API key here. This key could be set in R with the following code from the tidycensus package:

 # US Census API Key
 #census_api_key(Sys.getenv('census_api_key'))
 census_api_key('YOUR API KEY')

As noted in tidycensus’s basic usage vignette, getting information from the Census or ACS requires knowing the variable ID’s. As tehre are many, one suggested workflow is to download the variable descriptions via the load_variables function, and search for the needed variables from these descriptions (type ?load_variables in the R console for help).

We illustrate how to find the variable ID’s for the following information, how to query the data, and how to append them to our sample addresses in the previously created uc_points_tract data set:

  1. Median income
  2. Educational attainment
  3. Race and Ethnicity

1. Median Income

 # Download variables
v18 <- load_variables(2018, "acs5", cache = TRUE)
 # ACS time estimates: https://www.census.gov/programs-surveys/acs/guidance/estimates.html
 
 dim(v18)
 ## [1] 26997 3
 
 names(v18)
 ## [1] "name" "label" "concept"
 
 # View and filter variables in RStudio
 # Concept: median income
 #View(v18)
 
 # Manually view and filter
v18 %>%
 filter(str_detect(tolower(concept), 'median')
 , str_detect(tolower(concept), 'income')
 ) %>%
 as.data.frame
 ## Long, so will not print here in vignette

What we want is MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2018 INFLATION-ADJUSTED DOLLARS), which corresponds to variable B19013_001. We can query this information at the tract level, then join it to our list of addresses in uc_points_tract with GEOID as the join key:

lu_median_income <- get_acs(geography='tract'
 , variables=c(median_income='B19013_001')
 , state='CA'
 , year=2018
 , geometry=FALSE
 )
 dim(lu_median_income)
 ## [1] 8057 5
 
 names(lu_median_income)
 ## [1] "GEOID" "NAME" "variable" "estimate" "moe"
 
 head(lu_median_income) %>% as.data.frame
 ## GEOID NAME variable
 ## 1 06001400100 Census Tract 4001, Alameda County, California median_income
 ## 2 06001400200 Census Tract 4002, Alameda County, California median_income
 ## 3 06001400300 Census Tract 4003, Alameda County, California median_income
 ## 4 06001400400 Census Tract 4004, Alameda County, California median_income
 ## 5 06001400500 Census Tract 4005, Alameda County, California median_income
 ## 6 06001400600 Census Tract 4006, Alameda County, California median_income
 ## estimate moe
 ## 1 200893 49177
 ## 2 160536 29320
 ## 3 94732 23862
 ## 4 113036 16872
 ## 5 103846 18727
 ## 6 127232 25110
 
 # Join
uc_income <- uc_points_tract %>%
 select(University, Address, GEOID) %>%
 left_join(lu_median_income %>%
 select(GEOID, estimate) %>%
 rename(median_income=estimate)
 )
uc_income %>%
 as.data.frame
 ## University Address GEOID
 ## 1 UC Berkeley Berkeley, CA 06001400100
 ## 2 UC Irvine 510 E Peltason Dr. Irvine, California 92697-5700 06059062614
 ## 3 UC Los Angeles Los Angeles, CA 90095 06037265301
 ## 4 UC Merced 5200 Lake Rd, Merced, CA 95343 06047001801
 ## 5 UC Riverside 900 University Ave, Riverside, CA 92521 06065046500
 ## 6 UC San Diego 9500 Gilman Dr, La Jolla, CA 92093 06073008305
 ## 7 UC San Francisco 505 Parnassus Ave, San Francisco, CA 94143 06075030102
 ## 8 UC Santa Barbara Santa Barbara, CA 93106 06083000900
 ## 9 UC Santa Cruz 1156 High St, Santa Cruz, CA 95064 06087100400
 ## geometry median_income
 ## 1 POINT (-122.2396 37.87535) 200893
 ## 2 POINT (-117.8456 33.64163) 39135
 ## 3 POINT (-118.4468 34.07088) NA
 ## 4 POINT (-120.4326 37.34685) 96875
 ## 5 POINT (-117.3398 33.96371) 22102
 ## 6 POINT (-117.2432 32.87615) 39306
 ## 7 POINT (-122.4574 37.76307) 147549
 ## 8 POINT (-119.7027 34.42213) 46275
 ## 9 POINT (-122.0549 36.97738) 46375

2. Educational Attainment

 # View and search in RStudio
 # Concept: educational attainment
 #v18 %>%
 # filter(str_detect(tolower(concept), 'educational attainment')) %>%
 # as.data.frame

After searching for "educational attainment" and reviewing the results, we will want to focus on EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER as described by variables with the prefix B15003.

v18 %>% filter(str_detect(name, 'B15003')) %>% as.data.frame
 ## name label
 ## 1 B15003_001 Estimate!!Total
 ## 2 B15003_002 Estimate!!Total!!No schooling completed
 ## 3 B15003_003 Estimate!!Total!!Nursery school
 ## 4 B15003_004 Estimate!!Total!!Kindergarten
 ## 5 B15003_005 Estimate!!Total!!1st grade
 ## 6 B15003_006 Estimate!!Total!!2nd grade
 ## 7 B15003_007 Estimate!!Total!!3rd grade
 ## 8 B15003_008 Estimate!!Total!!4th grade
 ## 9 B15003_009 Estimate!!Total!!5th grade
 ## 10 B15003_010 Estimate!!Total!!6th grade
 ## 11 B15003_011 Estimate!!Total!!7th grade
 ## 12 B15003_012 Estimate!!Total!!8th grade
 ## 13 B15003_013 Estimate!!Total!!9th grade
 ## 14 B15003_014 Estimate!!Total!!10th grade
 ## 15 B15003_015 Estimate!!Total!!11th grade
 ## 16 B15003_016 Estimate!!Total!!12th grade, no diploma
 ## 17 B15003_017 Estimate!!Total!!Regular high school diploma
 ## 18 B15003_018 Estimate!!Total!!GED or alternative credential
 ## 19 B15003_019 Estimate!!Total!!Some college, less than 1 year
 ## 20 B15003_020 Estimate!!Total!!Some college, 1 or more years, no degree
 ## 21 B15003_021 Estimate!!Total!!Associate's degree
 ## 22 B15003_022 Estimate!!Total!!Bachelor's degree
 ## 23 B15003_023 Estimate!!Total!!Master's degree
 ## 24 B15003_024 Estimate!!Total!!Professional school degree
 ## 25 B15003_025 Estimate!!Total!!Doctorate degree
 ## concept
 ## 1 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 2 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 3 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 4 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 5 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 6 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 7 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 8 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 9 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 10 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 11 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 12 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 13 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 14 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 15 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 16 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 17 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 18 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 19 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 20 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 21 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 22 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 23 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 24 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 ## 25 EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER
 
 # Query all of these variables at the tract level
lu_edu_long <- get_acs(geography='tract'
 , variables=v18 %>%
 filter(str_detect(name, 'B15003')) %>%
 pull(name)
 , state='CA'
 , year=2018
 , geometry=FALSE
 )
 
 dim(lu_edu_long)
 ## [1] 201425 5
 
 names(lu_edu_long)
 ## [1] "GEOID" "NAME" "variable" "estimate" "moe" 
 
 unique(lu_edu_long$variable)
 
 # Calculate proportions at the tract level
lu_edu <- lu_edu_long %>%
 group_by(GEOID) %>%
 summarize(Edu_25_Total=sum(estimate[variable=='B15003_001'])
 , Pct_Edu_25_Less_HS=sum(estimate[variable %in% c('B15003_002', 'B15003_003', 'B15003_004', 'B15003_005', 'B15003_006', 'B15003_007', 'B15003_008', 'B15003_009', 'B15003_010', 'B15003_011', 'B15003_012', 'B15003_013', 'B15003_014', 'B15003_015', 'B15003_016')]) / Edu_25_Total
 , Pct_Edu_25_HS_GED=sum(estimate[variable %in% c('B15003_017', 'B15003_018')]) / Edu_25_Total 
 
 , Pct_Edu_25_Some_HE=sum(estimate[variable %in% c('B15003_019', 'B15003_020', 'B15003_021')]) / Edu_25_Total 
 , Pct_Edu_25_BA=sum(estimate[variable %in% c('B15003_022')]) / Edu_25_Total
 , Pct_Edu_25_MA_plus=sum(estimate[variable %in% c('B15003_023', 'B15003_024', 'B15003_025')]) / Edu_25_Total
 ) %>%
 ungroup
 
 dim(lu_edu)
 ## [1] 8057 7
 
 # Join
uc_edu <- uc_points_tract %>% 
 select(University, Address, GEOID) %>%
 left_join(lu_edu)
 
 # Print
uc_edu %>%
 as.data.frame
 ## University Address GEOID
 ## 1 UC Berkeley Berkeley, CA 06001400100
 ## 2 UC Irvine 510 E Peltason Dr. Irvine, California 92697-5700 06059062614
 ## 3 UC Los Angeles Los Angeles, CA 90095 06037265301
 ## 4 UC Merced 5200 Lake Rd, Merced, CA 95343 06047001801
 ## 5 UC Riverside 900 University Ave, Riverside, CA 92521 06065046500
 ## 6 UC San Diego 9500 Gilman Dr, La Jolla, CA 92093 06073008305
 ## 7 UC San Francisco 505 Parnassus Ave, San Francisco, CA 94143 06075030102
 ## 8 UC Santa Barbara Santa Barbara, CA 93106 06083000900
 ## 9 UC Santa Cruz 1156 High St, Santa Cruz, CA 95064 06087100400
 ## geometry Edu_25_Total Pct_Edu_25_Less_HS Pct_Edu_25_HS_GED
 ## 1 POINT (-122.2396 37.87535) 2455 0.024439919 0.03095723
 ## 2 POINT (-117.8456 33.64163) 5296 0.003587613 0.04040785
 ## 3 POINT (-118.4468 34.07088) 89 0.000000000 0.00000000
 ## 4 POINT (-120.4326 37.34685) 1839 0.038064165 0.19738989
 ## 5 POINT (-117.3398 33.96371) 1577 0.163601776 0.12999366
 ## 6 POINT (-117.2432 32.87615) 1256 0.027070064 0.06130573
 ## 7 POINT (-122.4574 37.76307) 4200 0.009047619 0.04833333
 ## 8 POINT (-119.7027 34.42213) 2633 0.200151918 0.12229396
 ## 9 POINT (-122.0549 36.97738) 906 0.006622517 0.04525386
 ## Pct_Edu_25_Some_HE Pct_Edu_25_BA Pct_Edu_25_MA_plus
 ## 1 0.12545825 0.3213849 0.4977597
 ## 2 0.07817221 0.2471677 0.6306647
 ## 3 0.83146067 0.1685393 0.0000000
 ## 4 0.36052202 0.2300163 0.1740076
 ## 5 0.38173748 0.1521877 0.1724794
 ## 6 0.16401274 0.3542994 0.3933121
 ## 7 0.08238095 0.4159524 0.4442857
 ## 8 0.28142803 0.2054690 0.1906570
 ## 9 0.26158940 0.2207506 0.4657837

3. Race and Ethnicity

Working with race and ethnicity data from the Census is not as straightforward as one would expect. It’s important to understand how the information is gathered. This sheet describes how race and ethnicity are asked in two questions by the Census. Based on how these questions are asked and how the options are presented, it’s important to recognize that respondents could:

  1. Check more than 1 race categories in the race question.
  2. Respond if they are of Hispanic origins or not in the ethnicity question.

Because of these points, respondents could fall under more than 1 racial and ethnic categorizations based on what is checked in the two questions. It’s also important to note that Census data are summarized with counts of individuals for each geographical area. That is, data are not reported at the individual level. Because of this, one could summarize each racial or ethnic group by counts and proportions, but these counts and proportions will most almost certainly not sum up to the whole.

We illustrate two ways of summarizing race and ethnicity data at the tract level. In the first case, we include in each race group all respondents that checked a particular group, even if the respondent checks more than one group. In the second case, we include in each race group all respondents that only check one particular group and not multiple. In both cases, the Hispanic ethnicity is summarized separately.

Here is sample code for the first case that includes all respondents that identify with a racial group:

 # Major racial groups
v18 %>%
 filter(str_detect(tolower(concept), 'in combination')) %>%
 as.data.frame
 ## name label
 ## 1 B02008_001 Estimate!!Total
 ## 2 B02009_001 Estimate!!Total
 ## 3 B02010_001 Estimate!!Total
 ## 4 B02011_001 Estimate!!Total
 ## 5 B02012_001 Estimate!!Total
 ## 6 B02013_001 Estimate!!Total
 ## concept
 ## 1 WHITE ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES
 ## 2 BLACK OR AFRICAN AMERICAN ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES
 ## 3 AMERICAN INDIAN AND ALASKA NATIVE ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES
 ## 4 ASIAN ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES
 ## 5 NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES
 ## 6 SOME OTHER RACE ALONE OR IN COMBINATION WITH ONE OR MORE OTHER RACES
 
 # Ethnicity
v18 %>%
 filter(concept=='HISPANIC OR LATINO ORIGIN') %>%
 as.data.frame
 ## name label concept
 ## 1 B03003_001 Estimate!!Total HISPANIC OR LATINO ORIGIN
 ## 2 B03003_002 Estimate!!Total!!Not Hispanic or Latino HISPANIC OR LATINO ORIGIN
 ## 3 B03003_003 Estimate!!Total!!Hispanic or Latino HISPANIC OR LATINO ORIGIN
 
 # Total
v18 %>%
 filter(concept=='TOTAL POPULATION') %>%
 as.data.frame
 ## name label concept
 ## 1 B01003_001 Estimate!!Total TOTAL POPULATION
 
 # Query data elements
lu_race_long <- get_acs(geography='tract'
 , variables=c('B01003_001', 'B03003_003', 'B02008_001', 'B02009_001', 'B02010_001', 'B02011_001', 'B02012_001', 'B02013_001')
 , state='CA'
 , year=2018
 , geometry=FALSE
 )
 
 dim(lu_race_long)
 ## [1] 64456 5
 
 names(lu_race_long)
 ## [1] "GEOID" "NAME" "variable" "estimate" "moe" 
 
 unique(lu_race_long$variable)
 ## [1] "B01003_001" "B02008_001" "B02009_001" "B02010_001" "B02011_001"
 ## [6] "B02012_001" "B02013_001" "B03003_003"
 
lu_race <- lu_race_long %>%
 group_by(GEOID) %>%
 summarize(Pop_Total=sum(estimate[variable=='B01003_001'])
 , Pct_Pop_White=sum(estimate[variable=='B02008_001']) / Pop_Total
 , Pct_Pop_Black=sum(estimate[variable=='B02009_001']) / Pop_Total
 , Pct_Pop_American_Indian=sum(estimate[variable=='B02010_001']) / Pop_Total 
 , Pct_Pop_Asian=sum(estimate[variable=='B02011_001']) / Pop_Total
 , Pct_Pop_Pacific_Islander=sum(estimate[variable=='B02012_001']) / Pop_Total
 , Pct_Pop_Other=sum(estimate[variable=='B02013_001']) / Pop_Total
 , Pct_Pop_Hispanic=sum(estimate[variable=='B03003_003']) / Pop_Total
 )
 
 dim(lu_race)
 ## [1] 8057 9
 
 # Join
uc_race <- uc_points_tract %>% 
 select(University, Address, GEOID) %>%
 left_join(lu_race)
 
 # Print
uc_race %>%
 as.data.frame
 ## University Address GEOID
 ## 1 UC Berkeley Berkeley, CA 06001400100
 ## 2 UC Irvine 510 E Peltason Dr. Irvine, California 92697-5700 06059062614
 ## 3 UC Los Angeles Los Angeles, CA 90095 06037265301
 ## 4 UC Merced 5200 Lake Rd, Merced, CA 95343 06047001801
 ## 5 UC Riverside 900 University Ave, Riverside, CA 92521 06065046500
 ## 6 UC San Diego 9500 Gilman Dr, La Jolla, CA 92093 06073008305
 ## 7 UC San Francisco 505 Parnassus Ave, San Francisco, CA 94143 06075030102
 ## 8 UC Santa Barbara Santa Barbara, CA 93106 06083000900
 ## 9 UC Santa Cruz 1156 High St, Santa Cruz, CA 95064 06087100400
 ## geometry Pop_Total Pct_Pop_White Pct_Pop_Black
 ## 1 POINT (-122.2396 37.87535) 3115 0.7248796 0.05232745
 ## 2 POINT (-117.8456 33.64163) 17086 0.5653752 0.02680557
 ## 3 POINT (-118.4468 34.07088) 11235 0.5556742 0.04779706
 ## 4 POINT (-120.4326 37.34685) 5432 0.5427099 0.03589838
 ## 5 POINT (-117.3398 33.96371) 8754 0.4152387 0.14016450
 ## 6 POINT (-117.2432 32.87615) 1877 0.6105487 0.07032499
 ## 7 POINT (-122.4574 37.76307) 5255 0.7590866 0.03139867
 ## 8 POINT (-119.7027 34.42213) 3686 0.6706457 0.02414542
 ## 9 POINT (-122.0549 36.97738) 10299 0.6078260 0.05058744
 ## Pct_Pop_American_Indian Pct_Pop_Asian Pct_Pop_Pacific_Islander Pct_Pop_Other
 ## 1 0.009951846 0.23210273 0.000000000 0.034349920
 ## 2 0.005677163 0.38622264 0.003921339 0.074154278
 ## 3 0.014508233 0.46995995 0.004361371 0.002225189
 ## 4 0.017304860 0.17746686 0.003129602 0.265095729
 ## 5 0.013022618 0.35435230 0.024560201 0.162668494
 ## 6 0.014917421 0.35109217 0.010122536 0.030367608
 ## 7 0.006089439 0.20761180 0.002664129 0.022264510
 ## 8 0.032013022 0.03635377 0.000000000 0.250135648
 ## 9 0.012525488 0.32119623 0.015438392 0.073502282
 ## Pct_Pop_Hispanic
 ## 1 0.03338684
 ## 2 0.24007960
 ## 3 0.23230975
 ## 4 0.38494109
 ## 5 0.35857894
 ## 6 0.20618007
 ## 7 0.06698382
 ## 8 0.43271839
 ## 9 0.28546461

Here is sample code for the second case that includes respondents in a racial group only if they checked a single racial category:

v18 %>%
 filter(concept=='RACE') %>%
 as.data.frame
 ## name
 ## 1 B02001_001
 ## 2 B02001_002
 ## 3 B02001_003
 ## 4 B02001_004
 ## 5 B02001_005
 ## 6 B02001_006
 ## 7 B02001_007
 ## 8 B02001_008
 ## 9 B02001_009
 ## 10 B02001_010
 ## label
 ## 1 Estimate!!Total
 ## 2 Estimate!!Total!!White alone
 ## 3 Estimate!!Total!!Black or African American alone
 ## 4 Estimate!!Total!!American Indian and Alaska Native alone
 ## 5 Estimate!!Total!!Asian alone
 ## 6 Estimate!!Total!!Native Hawaiian and Other Pacific Islander alone
 ## 7 Estimate!!Total!!Some other race alone
 ## 8 Estimate!!Total!!Two or more races
 ## 9 Estimate!!Total!!Two or more races!!Two races including Some other race
 ## 10 Estimate!!Total!!Two or more races!!Two races excluding Some other race, and three or more races
 ## concept
 ## 1 RACE
 ## 2 RACE
 ## 3 RACE
 ## 4 RACE
 ## 5 RACE
 ## 6 RACE
 ## 7 RACE
 ## 8 RACE
 ## 9 RACE
 ## 10 RACE
 
 # Query data elements
lu_race_single_long <- get_acs(geography='tract'
 , variables=c('B01003_001', 'B03003_003', 'B02001_001', 'B02001_002', 'B02001_003', 'B02001_004', 'B02001_005', 'B02001_006', 'B02001_007', 'B02001_008', 'B02001_009', 'B02001_010')
 , state='CA'
 , year=2018
 , geometry=FALSE
 )
 
 dim(lu_race_single_long)
 ## [1] 96684 5
 
 names(lu_race_single_long)
 ## [1] "GEOID" "NAME" "variable" "estimate" "moe" 
 
 unique(lu_race_single_long$variable)
 ## [1] "B01003_001" "B02001_001" "B02001_002" "B02001_003" "B02001_004"
 ## [6] "B02001_005" "B02001_006" "B02001_007" "B02001_008" "B02001_009"
 ## [11] "B02001_010" "B03003_003"
 
lu_race_single <- lu_race_single_long %>%
 group_by(GEOID) %>%
 summarize(Pop_Total=sum(estimate[variable=='B01003_001'])
 # , Pop_Total2=sum(estimate[variable=='B02001_001'])
 , Pct_Pop_White_Only=sum(estimate[variable=='B02001_002']) / Pop_Total
 , Pct_Pop_Black_Only=sum(estimate[variable=='B02001_003']) / Pop_Total
 , Pct_Pop_American_Indian_Only=sum(estimate[variable=='B02001_004']) / Pop_Total 
 , Pct_Pop_Asian_Only=sum(estimate[variable=='B02001_005']) / Pop_Total
 , Pct_Pop_Pacific_Islander_Only=sum(estimate[variable=='B02001_006']) / Pop_Total
 , Pct_Pop_Other=sum(estimate[variable=='B02001_007']) / Pop_Total
 , Pct_Pop_Two_Or_More=sum(estimate[variable=='B02001_008']) / Pop_Total
 , Pct_Pop_Hispanic=sum(estimate[variable=='B03003_003']) / Pop_Total
 )
 
 dim(lu_race_single)
 ## [1] 8057 10
 
 # Join
uc_race_single <- uc_points_tract %>% 
 select(University, Address, GEOID) %>%
 left_join(lu_race_single)
 
 # Print
uc_race_single %>%
 as.data.frame
 ## University Address GEOID
 ## 1 UC Berkeley Berkeley, CA 06001400100
 ## 2 UC Irvine 510 E Peltason Dr. Irvine, California 92697-5700 06059062614
 ## 3 UC Los Angeles Los Angeles, CA 90095 06037265301
 ## 4 UC Merced 5200 Lake Rd, Merced, CA 95343 06047001801
 ## 5 UC Riverside 900 University Ave, Riverside, CA 92521 06065046500
 ## 6 UC San Diego 9500 Gilman Dr, La Jolla, CA 92093 06073008305
 ## 7 UC San Francisco 505 Parnassus Ave, San Francisco, CA 94143 06075030102
 ## 8 UC Santa Barbara Santa Barbara, CA 93106 06083000900
 ## 9 UC Santa Cruz 1156 High St, Santa Cruz, CA 95064 06087100400
 ## geometry Pop_Total Pct_Pop_White_Only Pct_Pop_Black_Only
 ## 1 POINT (-122.2396 37.87535) 3115 0.6812199 0.04109149
 ## 2 POINT (-117.8456 33.64163) 17086 0.5149245 0.01984081
 ## 3 POINT (-118.4468 34.07088) 11235 0.4753004 0.02999555
 ## 4 POINT (-120.4326 37.34685) 5432 0.5154639 0.02172312
 ## 5 POINT (-117.3398 33.96371) 8754 0.3516107 0.09355723
 ## 6 POINT (-117.2432 32.87615) 1877 0.5524774 0.03356420
 ## 7 POINT (-122.4574 37.76307) 5255 0.7326356 0.02188392
 ## 8 POINT (-119.7027 34.42213) 3686 0.6573521 0.02414542
 ## 9 POINT (-122.0549 36.97738) 10299 0.5383047 0.03349840
 ## Pct_Pop_American_Indian_Only Pct_Pop_Asian_Only Pct_Pop_Pacific_Islander_Only
 ## 1 0.0000000000 0.19229535 0.0000000000
 ## 2 0.0007608568 0.34226852 0.0013461313
 ## 3 0.0023141967 0.40338229 0.0007120605
 ## 4 0.0060751105 0.16678940 0.0031296024
 ## 5 0.0060543751 0.30877313 0.0124514508
 ## 6 0.0117208311 0.30687267 0.0000000000
 ## 7 0.0000000000 0.20057088 0.0000000000
 ## 8 0.0320130222 0.02306023 0.0000000000
 ## 9 0.0025245169 0.28255170 0.0059229051
 ## Pct_Pop_Other Pct_Pop_Two_Or_More Pct_Pop_Hispanic
 ## 1 0.031781701 0.05361156 0.03338684
 ## 2 0.063151118 0.05770807 0.24007960
 ## 3 0.002225189 0.08607032 0.23230975
 ## 4 0.251840943 0.03497791 0.38494109
 ## 5 0.130340416 0.09721270 0.35857894
 ## 6 0.014384656 0.08098029 0.20618007
 ## 7 0.018458611 0.02645100 0.06698382
 ## 8 0.250135648 0.01329354 0.43271839
 ## 9 0.059131955 0.07806583 0.28546461

Example: CCC District Service Areas

The Foundation for California Community Colleges maintains a district service area map. They also make the shapefiles available here. One could download the shapefiles and be able to load them into R, Tableau, or other visualization or GIS platforms.

One use of this data is to quantify the number of students that a district serves outside of their service area.

Appendix: R and R Package Versions

This vignette was generated using an R session with the following packages. There may be some discrepancies when the reader replicates the code caused by version mismatch.

 sessionInfo()
 ## R version 4.0.2 (2020年06月22日)
 ## Platform: x86_64-w64-mingw32/x64 (64-bit)
 ## Running under: Windows 10 x64 (build 17763)
 ## 
 ## Matrix products: default
 ## 
 ## locale:
 ## [1] LC_COLLATE=English_United States.1252 
 ## [2] LC_CTYPE=English_United States.1252 
 ## [3] LC_MONETARY=English_United States.1252
 ## [4] LC_NUMERIC=C 
 ## [5] LC_TIME=English_United States.1252 
 ## 
 ## attached base packages:
 ## [1] stats graphics grDevices utils datasets methods base 
 ## 
 ## other attached packages:
 ## [1] tigris_1.4.1 sf_1.0-2 stringr_1.4.0 
 ## [4] geodist_0.0.7 ggrepel_0.9.1 maps_3.3.0 
 ## [7] ggplot2_3.3.3 dplyr_1.0.6 tidygeocoder_1.0.3 
 ## [10] IRexamples_0.0.2 RevoUtils_11.0.2 RevoUtilsMath_11.0.0
 ## 
 ## loaded via a namespace (and not attached):
 ## [1] uuid_0.1-4 plyr_1.8.6 igraph_1.2.6 
 ## [4] sp_1.4-5 splines_4.0.2 crosstalk_1.1.1 
 ## [7] rstantools_2.1.1 inline_0.3.19 digest_0.6.27 
 ## [10] htmltools_0.5.1.1 rsconnect_0.8.18 fansi_0.5.0 
 ## [13] RSelenium_1.7.7 magrittr_2.0.1 readr_1.4.0 
 ## [16] RcppParallel_5.1.4 matrixStats_0.59.0 xts_0.12.1 
 ## [19] askpass_1.1 prettyunits_1.1.1 jpeg_0.1-8.1 
 ## [22] colorspace_2.0-1 rvest_1.0.1 rappdirs_0.3.3 
 ## [25] mitools_2.4 rgdal_1.5-23 callr_3.7.0 
 ## [28] crayon_1.4.1 jsonlite_1.7.2 lme4_1.1-27 
 ## [31] survival_3.2-11 zoo_1.8-9 glue_1.4.2 
 ## [34] gtable_0.3.0 MatrixModels_0.5-0 V8_3.4.2 
 ## [37] DisImpact_0.0.15 pkgbuild_1.2.0 rstan_2.21.2 
 ## [40] semver_0.2.0 scales_1.1.1 DBI_1.1.1 
 ## [43] ggthemes_4.2.4 miniUI_0.1.1.1 Rcpp_1.0.7 
 ## [46] xtable_1.8-4 units_0.7-2 foreign_0.8-81 
 ## [49] proxy_0.4-26 stats4_4.0.2 StanHeaders_2.21.0-7
 ## [52] survey_4.0 DT_0.18 htmlwidgets_1.5.3 
 ## [55] httr_1.4.2 threejs_0.3.3 RColorBrewer_1.1-2 
 ## [58] ellipsis_0.3.2 farver_2.1.0 pkgconfig_2.0.3 
 ## [61] loo_2.4.1 XML_3.99-0.6 utf8_1.2.1 
 ## [64] labeling_0.4.2 tidyselect_1.1.1 rlang_0.4.11 
 ## [67] reshape2_1.4.4 later_1.2.0 munsell_0.5.0 
 ## [70] tools_4.0.2 xgboost_1.4.1.1 cli_2.5.0 
 ## [73] generics_0.1.0 ggridges_0.5.3 fastmap_1.1.0 
 ## [76] binman_0.1.2 processx_3.5.2 caTools_1.18.2 
 ## [79] purrr_0.3.4 twang_2.3 nlme_3.1-152 
 ## [82] mime_0.10 rstanarm_2.21.1 xml2_1.3.2 
 ## [85] tidycensus_1.0 rstudioapi_0.13 compiler_4.0.2 
 ## [88] bayesplot_1.8.1 shinythemes_1.2.0 curl_4.3.1 
 ## [91] png_0.1-7 e1071_1.7-8 tibble_3.1.2 
 ## [94] stringi_1.4.6 ps_1.6.0 forcats_0.5.1 
 ## [97] lattice_0.20-44 Matrix_1.3-4 classInt_0.4-3 
 ## [100] nloptr_1.2.2.2 markdown_1.1 shinyjs_2.0.0 
 ## [103] gbm_2.1.8 vctrs_0.3.8 pillar_1.6.1 
 ## [106] lifecycle_1.0.0 data.table_1.14.0 bitops_1.0-7 
 ## [109] maptools_1.1-1 httpuv_1.6.1 wdman_0.2.5 
 ## [112] R6_2.5.0 latticeExtra_0.6-29 promises_1.2.0.1 
 ## [115] KernSmooth_2.23-20 gridExtra_2.3 codetools_0.2-18 
 ## [118] boot_1.3-28 colourpicker_1.1.0 MASS_7.3-54 
 ## [121] gtools_3.9.2 assertthat_0.2.1 openssl_1.4.4 
 ## [124] withr_2.4.2 shinystan_2.5.0 parallel_4.0.2 
 ## [127] hms_1.1.0 grid_4.0.2 tidyr_1.1.3 
 ## [130] class_7.3-19 minqa_1.2.4 shiny_1.6.0 
 ## [133] base64enc_0.1-3 dygraphs_1.1.1.6

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