Calculate Social Vulnerability Index for Communities.

findSVI

The goal of findSVI is to calculate regional CDC/ATSDR Social Vulnerability Index (SVI) at a geographic level of interest using US census data from American Community Survey.

Overview

CDC/ATSDR releases SVI biannually at the counties/census tracts level for US or an individual state (which can be downloaded here). findSVI aims to support more flexible and specific SVI analysis with additional options for years (2012-2021) and geographic 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) and calculates SVI based on CDC/ATSDR SVI documentation 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 customized requests for census data retrieval (e.g., different geography level for each year-state pair, multiple states for one year), you can process individual entry using the following:

• get_census_data(): retrieves US census data (Census API key required);
• get_svi(): calculates SVI from the census data supplied.

Essentially, find_svi() is a wrapper function for get_census_data() and get_svi() that also supports iteration over 1-year-and-1-state pairs at the same geography level.

Installation

Install the findSVI package via CRAN:

install.packages("findSVI")

Alternatively, you can install the development version of findSVI from GitHub with:

# install.packages("devtools")
devtools::install_github("heli-xu/findSVI")

Usage

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.)

data <- get_census_data(2020, "county", "PA")
data[1:10, 1:10]

#> # A tibble: 10 × 10
#>    GEOID NAME    B0600…¹ B0600…² B0900…³ B0900…⁴ B1101…⁵ B1101…⁶ B1101…⁷ B1101…⁸
#>    <chr> <chr>     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1 42001 Adams …    7788     602   20663      NA    1237     215     482     171
#>  2 42003 Allegh…   45708    1713  228296      49   24311    1147    5378     525
#>  3 42005 Armstr…    3973     305   12516       9     912     161     247      85
#>  4 42007 Beaver…    7546     640   31915      NA    3380     380     787     174
#>  5 42009 Bedfor…    3996     317    9386      11     468      99     213      50
#>  6 42011 Berks …   36488    1356   93714      44    8812     662    1695     304
#>  7 42013 Blair …    7292     679   24920      19    2552     363     544     169
#>  8 42015 Bradfo…    4395     362   13358      NA     969     177     428     117
#>  9 42017 Bucks …   25651    1306  128008      53    8222     749    3174     581
#> 10 42019 Butler…    6118     468   37577      NA    2121     337     813     198
#> # … with abbreviated variable names ¹​B06009_002E, ²​B06009_002M, ³​B09001_001E,
#> #   ⁴​B09001_001M, ⁵​B11012_010E, ⁶​B11012_010M, ⁷​B11012_015E, ⁸​B11012_015M

(First 10 rows and columns are shown, with the rest of columns being other census variables for SVI calculation.)

result <- get_svi(2020, data)
glimpse(restult)

#> 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.…