- Notifications
You must be signed in to change notification settings - Fork3
Calculate CDC/ATSDR Social Vulnerability Index
License
Unknown, MIT licenses found
Licenses found
heli-xu/findSVI
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
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 key required) andcalculates 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 liketo include simple feature geometry or have more customized requests forcensus data retrieval (e.g., different geography level for eachyear-state pair, multiple states for one year), you can processindividual entry using the following:
get_census_data(): retrieves US census data (Census API keyrequired);get_svi(): calculates SVI from the census data supplied.
Essentially,find_svi() is a wrapper function forget_census_data()andget_svi() that also supports iteration over 1-year-and-1-statepairs at the same geography level.
Install the findSVI package via CRAN:
install.packages("findSVI")Alternatively, you can install the development version of findSVI fromGitHub with:
# install.packages("devtools")devtools::install_github("heli-xu/findSVI")
To find county-level SVI for New Jersey (NJ) for 2017, and forPennsylvania (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 get SVI for PA for2020:
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 the relationship between aCensus geography (‘GEOID’) and the custom geographic level (‘GEOID2’).The Census geography should be fully nested in the custom geographiclevel of interest. As an example, first 10 rows of the county-commutingzone 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 37
With 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 0.335About
Calculate CDC/ATSDR Social Vulnerability Index
Resources
License
Unknown, MIT licenses found
Licenses found
Uh oh!
There was an error while loading.Please reload this page.
Stars
Watchers
Forks
Packages0
Uh oh!
There was an error while loading.Please reload this page.