The goal of findSVI is to calculate regional CDC/ATSDR SocialVulnerability Index (SVI) (former site:www.atsdr.cdc.gov/placeandhealth/svi/index.html) at a geographic levelof interest using US census data from American Community Survey.
CDC/ATSDR releases SVI biannually at the counties/census tracts levelfor US or an individual state. findSVI aims to support more flexible andspecific SVI analysis with additional options for years (2012-2022) andgeographic levels (e.g., ZCTA/places, combining multiple states).
To find SVI for one or multiple year-state pair(s):
find_svi(): retrieves US census data (Census API keyrequired) and calculates SVI based on CDC/ATSDR SVI documentation(www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html)for each year-state pair at the same geography level.In most cases,find_svi() would be the easiest option.If you’d like to include simple feature geometry or have more customizedrequests for census data retrieval (e.g., different geography level foreach year-state pair, multiple states for one year), you can processindividual entry using the following:
get_census_data(): retrieves US census data (Census APIkey required);get_svi(): calculates SVI from the census datasupplied.Essentially,find_svi() is a wrapper function forget_census_data() andget_svi() that alsosupports iteration over 1-year-and-1-state pairs at the same geographylevel.
Install the findSVI package via CRAN:
install.packages("findSVI")Alternatively, you can install the development version of findSVIfrom GitHub with:
# install.packages("devtools")devtools::install_github("heli-xu/findSVI")To find county-level SVI for New Jersey (NJ) for 2017, andfor Pennsylvania (PA) for 2018:
library(findSVI)library(dplyr)summarise_results<-find_svi(year =c(2017,2018),state =c("NJ","PA"),geography ="county")summarise_results%>%group_by(year, state)%>%slice_head(n =5)#> # A tibble: 10 × 8#> # Groups: year, state [2]#> GEOID RPL_theme1 RPL_theme2 RPL_theme3 RPL_theme4 RPL_themes year state#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>#> 1 34001 0.95 0.8 0.65 1 0.95 2017 NJ #> 2 34003 0.2 0.3 0.55 0.45 0.25 2017 NJ #> 3 34005 0.3 0.5 0.35 0.4 0.3 2017 NJ #> 4 34007 0.7 0.9 0.55 0.6 0.75 2017 NJ #> 5 34009 0.65 0.6 0.1 0.55 0.45 2017 NJ #> 6 42001 0.212 0.242 0.697 0.227 0.182 2018 PA #> 7 42003 0.136 0.0758 0.742 0.576 0.212 2018 PA #> 8 42005 0.621 0.530 0.0152 0.167 0.227 2018 PA #> 9 42007 0.182 0.409 0.530 0.348 0.197 2018 PA #> 10 42009 0.712 0.606 0.0758 0.288 0.394 2018 PA(First 5 rows of results for 2017-NJ and 2018-PA are shown.‘RPL_themes` indicates overall SVI, and ’RPL_theme1’ to ‘RPL_theme4’indicate theme-specific SVIs.)
To retrieve county-level census dataand then getSVI for PA for 2020:
data<-get_census_data(2020,"county","PA")data[1:10,1:10]#> # A tibble: 10 × 10#> GEOID NAME B06009_002E B06009_002M B09001_001E B09001_001M B11012_010E#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 42001 Adams Coun… 7788 602 20663 NA 1237#> 2 42003 Allegheny … 45708 1713 228296 49 24311#> 3 42005 Armstrong … 3973 305 12516 9 912#> 4 42007 Beaver Cou… 7546 640 31915 NA 3380#> 5 42009 Bedford Co… 3996 317 9386 11 468#> 6 42011 Berks Coun… 36488 1356 93714 44 8812#> 7 42013 Blair Coun… 7292 679 24920 19 2552#> 8 42015 Bradford C… 4395 362 13358 NA 969#> 9 42017 Bucks Coun… 25651 1306 128008 53 8222#> 10 42019 Butler Cou… 6118 468 37577 NA 2121#> # ℹ 3 more variables: B11012_010M <dbl>, B11012_015E <dbl>, B11012_015M <dbl>(First 10 rows and columns are shown, with the rest of columns beingother census variables for SVI calculation.)
result<-get_svi(2020, data)glimpse(result)#> Rows: 67#> Columns: 63#> $ GEOID <chr> "42001", "42003", "42005", "42007", "42009", "42011", "420…#> $ NAME <chr> "Adams County, Pennsylvania", "Allegheny County, Pennsylva…#> $ E_TOTPOP <dbl> 102627, 1218380, 65356, 164781, 48154, 419062, 122495, 607…#> $ E_HU <dbl> 42525, 602416, 32852, 79587, 24405, 167514, 56960, 30691, …#> $ E_HH <dbl> 39628, 545695, 28035, 72086, 19930, 156389, 51647, 25084, …#> $ E_POV150 <dbl> 13573, 212117, 13566, 28766, 10130, 77317, 27397, 13731, 5…#> $ E_UNEMP <dbl> 2049, 32041, 1735, 4249, 1033, 12196, 2765, 1331, 14477, 4…#> $ E_HBURD <dbl> 9088, 133524, 5719, 15764, 3952, 40982, 12146, 5520, 57197…#> $ E_NOHSDP <dbl> 7788, 45708, 3973, 7546, 3996, 36488, 7292, 4395, 25651, 6…#> $ E_UNINSUR <dbl> 5656, 46333, 2632, 6242, 3310, 25627, 6155, 3992, 25208, 6…#> $ E_AGE65 <dbl> 20884, 230745, 14496, 35351, 10950, 72293, 25372, 12948, 1…#> $ E_AGE17 <dbl> 20663, 228296, 12516, 31915, 9386, 93714, 24920, 13358, 12…#> $ E_DISABL <dbl> 13860, 163671, 11431, 25878, 7797, 57961, 20278, 8731, 653…#> $ E_SNGPNT <dbl> 1719, 29689, 1159, 4167, 681, 10507, 3096, 1397, 11396, 29…#> $ E_LIMENG <dbl> 1318, 9553, 130, 606, 64, 16570, 388, 172, 11502, 449, 185…#> $ E_MINRTY <dbl> 11624, 269795, 2096, 18205, 1672, 123611, 7120, 2733, 1089…#> $ E_MUNIT <dbl> 821, 82729, 1180, 4563, 635, 11010, 3629, 1011, 25508, 660…#> $ E_MOBILE <dbl> 2882, 4147, 3289, 3012, 3491, 4628, 4094, 4419, 4764, 6464…#> $ E_CROWD <dbl> 468, 4697, 238, 693, 217, 1878, 451, 472, 2916, 489, 446, …#> $ E_NOVEH <dbl> 1726, 72338, 2058, 5824, 961, 13331, 4216, 2086, 11711, 49…#> $ E_GROUPQ <dbl> 4140, 33976, 795, 2933, 481, 13171, 3289, 736, 9462, 5592,…#> $ EP_POV150 <dbl> 13.8, 17.9, 21.0, 17.7, 21.4, 19.0, 22.9, 22.9, 9.7, 13.2,…#> $ EP_UNEMP <dbl> 3.9, 4.9, 5.5, 5.1, 4.5, 5.6, 4.7, 4.7, 4.2, 4.6, 5.2, 10.…#> $ EP_HBURD <dbl> 22.9, 24.5, 20.4, 21.9, 19.8, 26.2, 23.5, 22.0, 23.8, 19.4…#> $ EP_NOHSDP <dbl> 10.8, 5.2, 8.2, 6.2, 11.3, 12.8, 8.3, 10.2, 5.7, 4.6, 8.0,…#> $ EP_UNINSUR <dbl> 5.6, 3.8, 4.1, 3.8, 6.9, 6.2, 5.1, 6.6, 4.1, 3.3, 4.1, 3.2…#> $ EP_AGE65 <dbl> 20.3, 18.9, 22.2, 21.5, 22.7, 17.3, 20.7, 21.3, 18.7, 18.8…#> $ EP_AGE17 <dbl> 20.1, 18.7, 19.2, 19.4, 19.5, 22.4, 20.3, 22.0, 20.4, 20.0…#> $ EP_DISABL <dbl> 13.7, 13.6, 17.6, 15.8, 16.3, 14.0, 16.8, 14.5, 10.5, 12.8…#> $ EP_SNGPNT <dbl> 4.3, 5.4, 4.1, 5.8, 3.4, 6.7, 6.0, 5.6, 4.7, 3.8, 5.3, 8.1…#> $ EP_LIMENG <dbl> 1.4, 0.8, 0.2, 0.4, 0.1, 4.2, 0.3, 0.3, 1.9, 0.3, 0.1, 0.0…#> $ EP_MINRTY <dbl> 11.3, 22.1, 3.2, 11.0, 3.5, 29.5, 5.8, 4.5, 17.4, 5.6, 7.6…#> $ EP_MUNIT <dbl> 1.9, 13.7, 3.6, 5.7, 2.6, 6.6, 6.4, 3.3, 10.1, 7.9, 5.7, 2…#> $ EP_MOBILE <dbl> 6.8, 0.7, 10.0, 3.8, 14.3, 2.8, 7.2, 14.4, 1.9, 7.7, 4.7, …#> $ EP_CROWD <dbl> 1.2, 0.9, 0.8, 1.0, 1.1, 1.2, 0.9, 1.9, 1.2, 0.6, 0.8, 1.2…#> $ EP_NOVEH <dbl> 4.4, 13.3, 7.3, 8.1, 4.8, 8.5, 8.2, 8.3, 4.9, 6.4, 11.0, 9…#> $ EP_GROUPQ <dbl> 4.0, 2.8, 1.2, 1.8, 1.0, 3.1, 2.7, 1.2, 1.5, 3.0, 5.1, 1.7…#> $ EPL_POV150 <dbl> 0.0758, 0.2727, 0.5303, 0.2424, 0.5606, 0.3788, 0.6818, 0.…#> $ EPL_UNEMP <dbl> 0.1212, 0.4242, 0.6818, 0.5000, 0.2576, 0.6970, 0.3636, 0.…#> $ EPL_HBURD <dbl> 0.5303, 0.6970, 0.2424, 0.4394, 0.1970, 0.8636, 0.5909, 0.…#> $ EPL_NOHSDP <dbl> 0.7273, 0.0152, 0.2424, 0.1061, 0.8182, 0.9091, 0.2727, 0.…#> $ EPL_UNINSUR <dbl> 0.5152, 0.1061, 0.1364, 0.1061, 0.7424, 0.6667, 0.3939, 0.…#> $ EPL_AGE65 <dbl> 0.4848, 0.2727, 0.7879, 0.7121, 0.8788, 0.0909, 0.5606, 0.…#> $ EPL_AGE17 <dbl> 0.5909, 0.1970, 0.2576, 0.3333, 0.3939, 0.9091, 0.6212, 0.…#> $ EPL_DISABL <dbl> 0.2576, 0.2273, 0.7727, 0.5000, 0.5909, 0.3333, 0.6667, 0.…#> $ EPL_SNGPNT <dbl> 0.2273, 0.6364, 0.1515, 0.7424, 0.0455, 0.8636, 0.7879, 0.…#> $ EPL_LIMENG <dbl> 0.7576, 0.6515, 0.0909, 0.2879, 0.0303, 0.9697, 0.1667, 0.…#> $ EPL_MINRTY <dbl> 0.6515, 0.8636, 0.0303, 0.6364, 0.0455, 0.9242, 0.2879, 0.…#> $ EPL_MUNIT <dbl> 0.1515, 0.9545, 0.4242, 0.6970, 0.1970, 0.7727, 0.7576, 0.…#> $ EPL_MOBILE <dbl> 0.4394, 0.0303, 0.6818, 0.2121, 0.9091, 0.1515, 0.5000, 0.…#> $ EPL_CROWD <dbl> 0.4091, 0.1818, 0.0909, 0.2576, 0.3333, 0.4091, 0.1818, 0.…#> $ EPL_NOVEH <dbl> 0.0000, 0.9848, 0.4545, 0.5909, 0.0455, 0.6818, 0.6061, 0.…#> $ EPL_GROUPQ <dbl> 0.6667, 0.4697, 0.0758, 0.2879, 0.0455, 0.5455, 0.4394, 0.…#> $ SPL_theme1 <dbl> 1.9698, 1.5152, 1.8333, 1.3940, 2.5758, 3.5152, 2.3029, 2.…#> $ SPL_theme2 <dbl> 2.3182, 1.9849, 2.0606, 2.5757, 1.9394, 3.1666, 2.8031, 2.…#> $ SPL_theme3 <dbl> 0.6515, 0.8636, 0.0303, 0.6364, 0.0455, 0.9242, 0.2879, 0.…#> $ SPL_theme4 <dbl> 1.6667, 2.6211, 1.7272, 2.0455, 1.5304, 2.5606, 2.4849, 2.…#> $ RPL_theme1 <dbl> 0.2424, 0.1667, 0.1970, 0.1364, 0.5455, 0.9242, 0.3636, 0.…#> $ RPL_theme2 <dbl> 0.3788, 0.2121, 0.2273, 0.5758, 0.1667, 0.9091, 0.6970, 0.…#> $ RPL_theme3 <dbl> 0.6515, 0.8636, 0.0303, 0.6364, 0.0455, 0.9242, 0.2879, 0.…#> $ RPL_theme4 <dbl> 0.1212, 0.5606, 0.1515, 0.2576, 0.0455, 0.5152, 0.4848, 0.…#> $ SPL_themes <dbl> 6.6062, 6.9848, 5.6514, 6.6516, 6.0911, 10.1666, 7.8788, 8…#> $ RPL_themes <dbl> 0.2273, 0.2879, 0.0909, 0.2424, 0.1667, 0.9545, 0.5152, 0.…To find SVI for custom geographic boundaries:
cz_svi<-find_svi_x(year =2020,geography ="county",xwalk = cty_cz_2020_xwalk#county-commuting zone crosswalk)…wherexwalk is supplied by users to define therelationship between a Census geography (‘GEOID’) and the customgeographic level (‘GEOID2’). The Census geography should be fully nestedin the custom geographic level of interest. As an example, first 10 rowsof the county-commuting zone crosswalk are shown below:
cty_cz_2020_xwalk%>%head(10)#> GEOID GEOID2#> 1 01069 3#> 2 01023 9#> 3 01005 3#> 4 01107 4#> 5 01033 10#> 6 04012 37#> 7 04001 32#> 8 05081 55#> 9 05121 46#> 10 06037 37With the crosswalk, county-level census data are aggregated to thecommuting zone-level, and SVI is calculated for each commuting zone.Below shows the overall and theme-specific SVI of the first 10 rows,with GEOIDs representing the commuting zone IDs.
cz_svi%>%select(GEOID,contains("RPL"))%>%head(10)#> # A tibble: 10 × 6#> GEOID RPL_theme1 RPL_theme2 RPL_theme3 RPL_theme4 RPL_themes#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>#> 1 1 0.778 0.833 0.885 0.730 0.826#> 2 2 0.734 0.436 0.698 0.388 0.625#> 3 3 0.871 0.892 0.703 0.570 0.833#> 4 4 0.881 0.498 0.838 0.947 0.876#> 5 5 0.560 0.675 0.684 0.333 0.606#> 6 6 0.799 0.813 0.605 0.302 0.720#> 7 7 0.821 0.680 0.802 0.875 0.842#> 8 8 0.694 0.888 0.438 0.0842 0.570#> 9 9 0.899 0.969 0.838 0.918 0.962#> 10 10 0.357 0.507 0.589 0.134 0.335