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SVI2018-无代写
时间：2023-11-10
CDC SVI 2018 Documentation - 1/31/2020
Please see data dictionary below.
Introduction
What is Social Vulnerability?
Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado
or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a
community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or
crowded households, may affect that community’s ability to prevent human suffering and financial loss in the
event of disaster. These factors describe a community’s social vulnerability.
What is CDC Social Vulnerability Index?
ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and
Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and
emergency response planners identify and map the communities that will most likely need support before,
during, and after a hazardous event.
SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for
which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment,
minority status, and disability, and further groups them into four related themes. Thus, each tract receives a
ranking for each Census variable and for each of the four themes, as well as an overall ranking.
In addition to tract-level rankings, SVI 2010, 2014, 2016, and 2018 also have corresponding rankings at the
county level. Notes below that describe “tract” methods also refer to county methods.
How can CDC SVI help communities be better prepared for hazardous events?
SVI provides specific socially and spatially relevant information to help public health officials and local planners
better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks,
or chemical exposure.
CDC SVI can be used to:
• Allocate emergency preparedness funding by community need.
• Estimate the type and amount of needed supplies such as food, water, medicine, and bedding.
• Decide how many emergency personnel are required to assist people.
• Identify areas in need of emergency shelters.
• Create a plan to evacuate people, accounting for those who have special needs, such as those without
vehicles, the elderly, or people who do not speak English well.
• Identify communities that will need continued support to recover following an emergency or natural
disaster.
Important Notes on CDC SVI Databases
SVI 2014, 2016, and 2018 are available for download in shapefile format from
https://svi.cdc.gov/SVIDataToolsDownload.html. SVI 2014 and 2016 are also available via ArcGIS Online.
Search on “CDC’s Social Vulnerability Index.”
For SVI 2000 and 2010, keep the data in geodatabase format when downloading from
https://svi.cdc.gov/SVIDataToolsDownload.html. Converting to shapefile changes the field names.
ACS field names have changed between SVI 2016 and 2018. Name changes are noted in the Data
Dictionary below.
2 For US-wide or multi-state mapping and analysis, use the US database, in which all tracts are ranked
against one another. For individual state mapping and analysis, use the state-specific database, in which
tracts are ranked only against other tracts in the specified state.
Starting with SVI 2014, we’ve added a stand-alone, state-specific Commonwealth of Puerto Rico
database. Puerto Rico is not included in the US-wide ranking.
Starting with SVI 2014, we’ve added a database of Tribal Census Tracts
(https://www.census.gov/glossary/#term_TribalCensusTract). Tribal tracts are defined independently of,
and in addition to, standard county-based tracts. The tribal tract database contains only estimates,
percentages, and their respective margins of error (MOEs), along with the adjunct variables described in
the data dictionary below. Because of geographic separation and cultural diversity, tribal tracts are not
ranked against each other nor against standard census tracts.
Tracts with zero estimates for total population (N = 645 for the U.S.) were removed during the ranking
process. These tracts were added back to the SVI databases after ranking. The TOTPOP field value is 0,
but the percentile ranking fields (RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4, and
RPL_THEMES) were set to -999.
For tracts with > 0 TOTPOP, a value of -999 in any field either means the value was unavailable from the
original census data or we could not calculate a derived value because of unavailable census data.
Any cells with a -999 were not used for further calculations. For example, total flags do not include fields
with a -999 value.
Whenever available, we use Census-calculated MOEs. If Census MOEs are unavailable, for instance when
aggregating variables within a table, we use approximation formulas provided by the Census in Appendix
A (pages A-14 through A-17) of A Compass for Understanding and Using American Community Survey
Data here:
https://www.census.gov/content/dam/Census/library/publications/2008/acs/ACSGeneralHandbook.pdf
If more precise MOEs are required, see Census methods and data regarding Variance Replicate Tables
here: https://www.census.gov/programs-surveys/acs/technical-documentation/variance-tables.html. For
selected ACS 5-year Detailed Tables, “Users can calculate margins of error for aggregated data by using
the variance replicates. Unlike available approximation formulas, this method results in an exact margin
of error by using the covariance term.”
The U.S. Census Bureau reports that data collection errors prohibited the inclusion of income and
poverty data from Rio Arriba County, New Mexico. Please see a more detailed explanation provided by
the Census Bureau here: https://www.census.gov/programs-surveys/acs/technical-
documentation/errata/125.html.
FIPS codes are generally defined as text to preserve leading zeros (0s). If you’re working with csv files,
leading 0s are required to properly join or merge tables. ArcGIS maintains leading 0s in the FIPS code
fields of csv files. To preserve leading 0s and create an Excel file in Excel for Office 365, follow these
steps:
o Open a blank worksheet in Excel.
o Click Data in the menu bar and choose the icon From Text/CSV
o Navigate to the csv file and choose to Import
o In the dialog box that opens, choose to Transform Data
o In the Power Query Editor dialog box, for each of the FIPS columns (ST, STCNTY, FIPS for tracts
and ST, FIPS for counties), right click the column name and choose to Change Type to Text.
o As prompted in the Change Column Type dialog box, choose to Replace current. Click Close and
Load.
o Save As an Excel xlsx file.
See the Methods section below for further details.
Questions? Please visit the SVI website at http://svi.cdc.gov for additional information or email the SVI
Coordinator at svi_coordinator@cdc.gov.
3
Methods
Variables Used
American Community Survey (ACS), 2014-2018 (5-year) data for the following estimates:
Text version of overall vulnerability image:
• Socioeconomic Status
o Below Poverty
o Unemployed
o Income
o No High School Diploma
• Household Composition & Disability
o Aged 65 or Older
o Aged 17 or Younger
o Civilian with a Disability
o Single-Parent Households
• Minority Status & Language
o Minority
o Speaks English “Less than Well”
• Housing Type & Transportation
o Multi-Unit Structures
o Mobile Homes
o Crowding
o No Vehicle
o Group Quarters
For SVI 2018, we included two adjunct variables, 1) 2014-2018 ACS estimates for persons without health
insurance, and 2) an estimate of daytime population derived from LandScan 2018 estimates. These adjunct
variables are excluded from SVI rankings.
4
Raw data estimates and percentages for each variable, for each tract, are included in the database. In addition,
the margins of error (MOEs) for each estimate, at the Census Bureau standard of 90%, are also included.
Confidence intervals can be calculated by subtracting the MOE from the estimate (lower limit) and adding the
MOE to the estimate (upper limit). Because of relatively small sample sizes, some of the MOEs are high. It’s
important to identify the amount of error acceptable in any analysis.
Rankings
We ranked Census tracts within each state and the District of Columbia, to enable mapping and analysis of
relative vulnerability in individual states. We also ranked tracts for the entire United States against one another,
for mapping and analysis of relative vulnerability in multiple states, or across the U.S. as a whole. Tract rankings
are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater
vulnerability.
For each tract, we generated its percentile rank among all tracts for 1) the fifteen individual variables, 2) the four
themes, and 3) its overall position.
Theme rankings: For each of the four themes, we summed the percentiles for the variables comprising each
theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings.
The four summary theme ranking variables, detailed in the Data Dictionary below, are:
• Socioeconomic - RPL_THEME1
• Household Composition & Disability - RPL_THEME2
• Minority Status & Language - RPL_THEME3
• Housing Type & Transportation - RPL_THEME4
Overall tract rankings: We summed the sums for each theme, ordered the tracts, and then calculated overall
percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual
variable rankings. The overall tract summary ranking variable is RPL_THEMES.
Flags
Tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability.
Tracts below the 90th percentile are given a value of 0.
For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall
flag value for each tract as the number of all variable flags.
For a detailed description of SVI variable selection rationale and methods, see A Social Vulnerability Index for
Disaster Management
(https://svi.cdc.gov/A%20Social%20Vulnerability%20Index%20for%20Disaster%20Management.pdf).
Reproducibility Caveat
When replicating SVI using Microsoft Excel or similar software, results may differ slightly from databases on the
SVI website or ArcGIS Online. This is due to variation in the number of decimal places used by the different
software programs. For purposes of automation, we developed SVI using SQL programming language. Because
the SQL programming language uses a different level of precision compared to Excel and similar software,
reproducing SVI in Excel may marginally differ from the SVI databases downloaded from the SVI website. For
future iterations of SVI, beginning with SVI 2018, we plan to modify the SQL automation process for constructing
SVI to align with that of Microsoft Excel. If there are any questions, please email the SVI Coordinator at
svi_coordinator@cdc.gov.
5
CDC SVI 2018 Data Dictionary – American Community Survey field names that changed between 2016 and 2018 are noted in RED and marked
‘Yes’ or ‘No’ in the ‘Field Name Changed Since 2016?’ column.
Variables beginning with “E_” are estimates. Variables beginning with “M_” are margins of error for
those estimates. Values of -999 represent “null” or “no data.”
The four summary theme ranking variables, detailed in the Data Dictionary below, are:
Themes
1. Socioeconomic
2. Household Composition/Disability
3. Minority Status/Language
4. Housing Type/Transportation
• Socioeconomic - RPL_THEME1
• Household Composition & Disability - RPL_THEME2
• Minority Status & Language - RPL_THEME3
• Housing Type & Transportation - RPL_THEME4
The overall tract summary ranking variable is RPL_THEMES.
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
ST State-level FIPS code SVI No FIPS
In Excel, from Tract-level FIPS code, LEFT
(FIPS, 2)
STATE State name S0601 Yes NAME In Excel, use DATA|Text to Columns to extract state name GEO.display-label
ST_ABBR State abbreviation N/A No N/A Joined from Esri state boundary shapefile
STCNTY County-level FIPS code SVI No FIPS
In Excel, from Tract-level FIPS code, LEFT
(FIPS, 5)
In the county-level SVI
database, the 5-digit
STCNTY field is the FIPS
field, used for joins.
GEO.id
COUNTY County name S0601 Yes NAME In Excel, use DATA| Text to Columns to extract county name GEO.display-label
FIPS Tract-level FIPS code S0601 Yes GEO_ID In Excel, RIGHT (GEO.id, 11)
LOCATION
Text
description of
tract, county,
state
S0601 Yes NAME GEO.display-label
AREA_SQMI Tract area in square miles
Census
Cartographic
Boundary
File - U.S.
Tracts 2018
500K
No ALAND * 3.86102e-7
Conversion from square meters to square
miles
6
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_TOTPOP
Population
estimate,
2014-2018 ACS
S0601 Yes S0601_C01_001E HC01_EST_VC01
M_TOTPOP
Population
estimate MOE,
2014-2018 ACS
S0601 Yes S0601_C01_001M HC01_MOE_VC01
E_HU
Housing units
estimate,
2014-2018 ACS
DP04 Yes DP04_0001E HC01_VC03
M_HU
Housing units
estimate MOE,
2014-2018 ACS
DP04 Yes DP04_0001M HC02_VC03
E_HH
Households
estimate,
2014-2018 ACS
DP02 Yes DP02_0001E HC01_VC03
M_HH
Households
estimate MOE,
2014-2018 ACS
DP02 Yes DP02_0001M HC02_VC03
E_POV
Persons below
poverty
estimate,
2014-2018 ACS
1 B17001 Yes B17001_002E HD01_VD02
M_POV
Persons below
poverty
estimate MOE,
2014-2018 ACS
1 B17001 Yes B17001_002M HD02_VD02
E_UNEMP
Civilian (age
16+)
unemployed
estimate,
2014-2018 ACS
1 DP03 Yes DP03_0005E HC01_VC07
M_UNEMP
Civilian (age
16+)
unemployed
estimate MOE,
2014-2018 ACS
1 DP03 Yes DP03_0005M HC02_VC07
7
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_PCI
Per capita
income
estimate,
2014-2018 ACS
1 B19301 Yes B19301_001E
Fewer rows than other
variables - joined to Census
2016 tracts. Contains null
cells (i.e. -999).
HD01_VD01
M_PCI
Per capita
income
estimate MOE,
2014-2018 ACS
1 B19301 Yes B19301_001M
Fewer rows than other
variables - joined to Census
2016 tracts
HD02_VD01
E_NOHSDP
Persons (age
25+) with no
high school
diploma
estimate,
2014-2018 ACS
1 B06009 Yes B06009_002E HD01_VD03
M_NOHSDP
Persons (age
25+) with no
high school
diploma
estimate MOE,
2014-2018 ACS
1 B06009 Yes B06009_002M HD02_VD03
E_MINRTY
Minority (all
persons except
white, non-
Hispanic)
estimate,
2014-2018 ACS
3 B01001H Yes E_TOTPOP - B01001H_001E
Estimate total population – white, Non-
Hispanic population
E_TOTPOP -
HD01_VD01
M_MINRTY
Minority (all
persons except
white, non-
Hispanic)
estimate MOE,
2014-2018 ACS
3 B01001H Yes
SQRT(M_TOTPO
P^2 +
B01001H_001M
^2)
SQRT (MOE total population^2 + MOE
white, non-Hispanic^2)
SQRT(M_TOTPOP
^2 +
HD02_VD01^2)
8
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_LIMENG
Persons (age
5+) who speak
English "less
than well"
estimate,
2014-2018 ACS
3 B16005 Yes
B16005_007E +
B16005_008E +
B16005_012E +
B16005_013E +
B16005_017E +
B16005_018E +
B16005_022E +
B16005_023E +
B16005_029E +
B16005_030E +
B16005_034E +
B16005_035E +
B16005_039E +
B16005_040E +
B16005_044E +
B16005_045E +
Estimate; Native: - Speak Spanish: - Speak
English "not well" + Estimate; Native: -
Speak Spanish: - Speak English "not at all"
+ Estimate; Native: - Speak other Indo-
European languages: - Speak English "not
well" + Estimate; Native: - Speak other
Indo-European languages: - Speak English
"not at all" + Estimate; Native: - Speak
Asian and Pacific Island languages: - Speak
English "not well" + Estimate; Native: -
Speak Asian and Pacific Island languages: -
Speak English "not at all" + Estimate;
Native: - Speak other languages: - Speak
English "not well" + Estimate; Native: -
Speak other languages: - Speak English
"not at all" + Estimate; Foreign born: -
Speak Spanish: - Speak English "not well"
+ Estimate; Foreign born: - Speak Spanish:
- Speak English "not at all" + Estimate;
Foreign born: - Speak other Indo-
European languages: - Speak English "not
well" + Estimate; Foreign born: - Speak
other Indo-European languages: - Speak
English "not at all" + Estimate; Foreign
born: - Speak Asian and Pacific Island
languages: - Speak English "not well" +
Estimate; Foreign born: - Speak Asian and
Pacific Island languages: - Speak English
"not at all" + Estimate; Foreign born: -
Speak other languages: - Speak English
"not well" + Estimate; Foreign born: -
Speak other languages: - Speak English
"not at all"
HD01_VD07 +
HD01_VD08 +
HD01_VD12 +
HD01_VD13 +
HD01_VD17 +
HD01_VD18 +
HD01_VD22 +
HD01_VD23 +
HD01_VD29 +
HD01_VD30 +
HD01_VD34 +
HD01_VD35 +
HD01_VD39 +
HD01_VD40 +
HD01_VD44 +
HD01_VD45
9
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
M_LIMENG
Persons (age
5+) who speak
English "less
than well"
estimate MOE,
2014-2018 ACS
3 B16005 Yes
SQRT(B16005_0
07M ^2 +
B16005_008M
^2 +
B16005_012M
^2 +
B16005_013M
^2 +
B16005_017M
^2 +
B16005_018M
^2 +
B16005_022M
^2 +
B16005_023M
^2 +
B16005_029M
^2 +
B16005_030M
^2 +
B16005_034M
^2 +
B16005_035M
^2 +
B16005_039M
^2 +
B16005_040M
^2 +
B16005_044M
^2 +
B16005_045M
^2)
SQRT (MOE Native: - Speak Spanish: -
Speak English "not well"^2 + MOE Native:
- Speak Spanish: - Speak English "not at
all"^2 + MOE Native: - Speak other Indo-
European languages: - Speak English "not
well"^2 + MOE Native: - Speak other Indo-
European languages: - Speak English "not
at all"^2 + MOE Native: - Speak Asian and
Pacific Island languages: - Speak English
"not well"^2 + MOE Native: - Speak Asian
and Pacific Island languages: - Speak
English "not at all"^2 + MOE Native: -
Speak other languages: - Speak English
"not well"^2 + MOE Native: - Speak other
languages: - Speak English "not at all"^2 +
MOE Foreign born: - Speak Spanish: -
Speak English "not well"^2 + MOE Foreign
born: - Speak Spanish: - Speak English "not
at all"^2 + MOE Foreign born: - Speak
other Indo-European languages: - Speak
English "not well"^2 + MOE Foreign born:
- Speak other Indo-European languages: -
Speak English "not at all"^2 + MOE
Foreign born: - Speak Asian and Pacific
Island languages: - Speak English "not
well"^2 + MOE Foreign born: - Speak
Asian and Pacific Island languages: - Speak
English "not at all"^2 + MOE Foreign born:
- Speak other languages: - Speak English
"not well"^2 + MOE Foreign born: - Speak
other languages: - Speak English "not at
all"^2)
SQRT(HD02_VD0
7^2 +
HD02_VD08^2 +
HD02_VD12^2 +
HD02_VD13^2 +
HD02_VD17^2 +
HD02_VD18^2 +
HD02_VD22^2 +
HD02_VD23^2 +
HD02_VD29^2 +
HD02_VD30^2 +
HD02_VD34^2 +
HD02_VD35^2 +
HD02_VD39^2 +
HD02_VD40^2 +
HD02_VD44^2 +
HD02_VD45^2)
E_MUNIT
Housing in
structures with
10 or more
units estimate,
2014-2018 ACS
4 DP04 Yes DP04_0012E + DP04_0013E
Estimate; UNITS IN STRUCTURE - Total
housing units - 10 to 19 units + Estimate;
UNITS IN STRUCTURE - Total housing units
- 20 or more units
HC01_VC19 + HC01_VC20
10
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
M_MUNIT
Housing in
structures with
10 or more
units estimate
MOE, 2014-
2018 ACS
4 DP04 Yes
SQRT(DP04_001
2M^2 +
DP04_0013M
^2)
SQRT (MOE UNITS IN STRUCTURE - Total
housing units - 10 to 19 units^2 + MOE;
UNITS IN STRUCTURE - Total housing units
- 20 or more units^2)
SQRT(HC02_VC19
^2 +
HC02_VC20^2)
E_MOBILE
Mobile homes
estimate,
2014-2018 ACS
4 DP04 Yes DP04_0014E HC01_VC21
M_MOBILE
Mobile homes
estimate MOE,
2014-2018 ACS
4 DP04 Yes DP04_0014M HC02_VC21
E_CROWD
At household
level (occupied
housing units),
more people
than rooms
estimate,
2014-2018 ACS
4 DP04 Yes DP04_0078E + DP04_0079E
Estimate; OCCUPANTS PER ROOM -
Occupied housing units - 1.01 to 1.50 +
Estimate; OCCUPANTS PER ROOM -
Occupied housing units - 1.51 or more
HC01_VC114 + HC01_VC115
M_CROWD
At household
level (occupied
housing units),
more people
than rooms
estimate MOE,
2014-2018 ACS
4 DP04 Yes
SQRT(DP04_007
8M^2 +
DP04_0079M^2
)
SQRT (MOE OCCUPANTS PER ROOM -
Occupied housing units - 1.01 to 1.50^2+
MOE OCCUPANTS PER ROOM - Occupied
housing units - 1.51 or more^2)
SQRT(HC02_VC11
4^2 +
HC02_VC115^2)
E_NOVEH
Households
with no vehicle
available
estimate,
2014-2018 ACS
4 DP04 Yes DP04_0058E HC01_VC85
M_NOVEH
Households
with no vehicle
available
estimate MOE,
2014-2018 ACS
4 DP04 Yes DP04_0058M HC02_VC85
11
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_GROUPQ
Persons in
institutionalize
d group
quarters
estimate,
2014-2018 ACS
4 B26001 Yes B26001_001E HD01_VD01
M_GROUPQ
Persons in
institutionalize
d group
quarters
estimate MOE,
2014-2018 ACS
4 B26001 Yes B26001_001M HD02_VD01
EP_POV
Percentage of
persons below
poverty
estimate
1 S0601 Yes S0601_C01_049E HC01_EST_VC67
MP_POV
Percentage of
persons below
poverty
estimate MOE
1 S0601 Yes S0601_C01_049M HC01_MOE_VC67
EP_UNEMP Unemployment Rate estimate 1 DP03 Yes DP03_0009PE
The ACS calculated
Unemployment Rate =
E_UNEMP/civilian
population age 16+ in the
labor force
HC03_VC12
MP_UNEMP
Unemploymen
t Rate estimate
MOE
1 DP03 Yes DP03_0009PM HC04_VC12
EP_PCI
Per capita
income
estimate,
2014-2018 ACS
1 B19301 Yes B19301_001E Value is the same as E_PCI HD01_VD01
MP_PCI
Per capita
income
estimate MOE,
2014-2018 ACS
1 B19301 Yes B19301_001M Value is the same as M_PCI HD02_VD01
12
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
EP_NOHSDP
Percentage of
persons with
no high school
diploma (age
25+) estimate
1 S0601 Yes S0601_C01_033E HC01_EST_VC46
MP_NOHSDP
Percentage of
persons with
no high school
diploma (25+)
estimate MOE
1 S0601 Yes S0601_C01_033M HC01_MOE_VC46
EP_DISABL
Percentage of
civilian
noninstitutiona
lized
population
with a
disability
estimate,
2014-2018 ACS
2 DP02 Yes DP02_0071PE HC03_VC106
MP_DISABL
Percentage of
civilian
noninstitutiona
lized
population
with a
disability
estimate MOE,
2014-2018 ACS
2 DP02 Yes DP02_0071PM HC04_VC106
EP_SNGPNT
Percentage of
single parent
households
with children
under 18
estimate,
2014-2018 ACS
2 SVI No (E_SNGPNT / E_HH) * 100
(Single parent household with children
under 18 estimate / Households estimate)
* 100
This calculation resulted in
some division by 0 errors in
cases where E_HH equals
0. These rows were revised
with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
13
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_SNGPNT
Percentage of
single parent
households
with children
under 18
estimate MOE,
2014-2018 ACS
2 SVI No
((SQRT(M_SNGP
NT^2-
((EP_SNGPNT/1
00)^2*M_HH^2)
))/E_HH)*100
((SQRT(MOE Single parent households^2 -
(Estimated proportion single parent
households^2 * MOE Households^2))) /
Households estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
EP_MINRTY
Percentage
minority (all
persons except
white, non-
Hispanic)
estimate,
2014-2018 ACS
3 SVI No (E_MINRTY/E_TOTPOP)*100
(Minority estimate / Total population
estimate) * 100
This calculation resulted in
some division by 0 errors in
cases where E_HH equals
0. These rows were revised
with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
MP_MINRTY
Percentage
minority (all
persons except
white, non-
Hispanic)
estimate MOE,
2014-2018 ACS
3 SVI
((SQRT(M_MINR
TY^2-
((EP_MINRTY/10
0)^2*M_TOTPO
P^2)))/E_TOTPO
P)*100
((SQRT(MOE Minority^2 - (Estimated
proportion minority^2 * MOE Total
population^2))) / Total population
estimate) * 100
14
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
EP_LIMENG
Percentage of
persons (age
5+) who speak
English "less
than well"
estimate,
2014-2018 ACS
3 SVI and B16005 Yes
(E_LIMENG/B16
005_001E)*100
(Persons who speak English "less than
well" estimate / Population age 5 and
over estimate) * 100
This calculation resulted in
some division by 0 errors in
cases where total
population age 5 and over
equals 0. These rows were
revised with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
(E_LIMENG/
HD01_VD01)*100
MP_LIMENG
Percentage of
persons (age
5+) who speak
English "less
than well"
estimate MOE,
2014-2018 ACS
3 SVI and B16005 Yes
((SQRT(M_LIME
NG^2-
((EP_LIMENG/1
00)^2*
B16005_001M^
2)))/
B16005_001E)*
100
((SQRT(MOE Persons who speak English
less than well^2 - (Estimated proportion
persons who speak English less than
well^2 * MOE population age 5 and
over^2))) / Population age 5 and over
estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
((SQRT(M_LIMEN
G^2-
((EP_LIMENG/10
0)^2*HD02_VD01
^2)))/
HD01_VD01)*100
EP_MUNIT
Percentage of
housing in
structures with
10 or more
units estimate
4 SVI No (E_MUNIT/E_HU)*100
(Housing in structures with 10 or more
units estimate / Housing units
estimate)*100
This calculation resulted in
some division by 0 errors in
cases where E_HU equals
0. These rows were revised
with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
15
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_MUNIT
Percentage of
housing in
structures with
10 or more
units estimate
MOE
4 SVI No
((SQRT(M_MUN
IT^2-
((EP_MUNIT/10
0)^2*M_HU^2))
)/E_HU)*100
((SQRT(MOE Housing in structures with 10
or more units^2 - (Estimated proportion
housing in structures with 10 or more
units^2 * MOE Housing units^2))) /
Housing units estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
EP_MOBILE
Percentage of
mobile homes
estimate
4 DP04 Yes DP04_0014PE HC03_VC21
MP_MOBILE
Percentage of
mobile homes
estimate MOE
4 DP04 Yes DP04_0014PM HC04_VC21
EP_CROWD
Percentage of
occupied
housing units
with more
people than
rooms
estimate
4 SVI and DP04 Yes
(E_CROWD/
DP04_0002E)*1
00
(Occupied housing units with more people
than rooms estimate / Occupied housing
units estimate)*100
This calculation resulted in
some division by 0 errors in
cases where HC01_VC04
equals 0. These rows were
revised with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
E_CROWD/HC01_
VC04)*100
16
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_CROWD
Percentage of
occupied
housing units
with more
people than
rooms
estimate MOE
4 SVI and DP04 Yes
((SQRT(M_CRO
WD^2-
((EP_CROWD/10
0)^2*
DP04_0002M^2
)))/
DP04_0002E)*1
00
((SQRT(MOE Occupied housing units with
more people than rooms^2 - (Estimated
proportion of occupied housing units with
more people than rooms^2 * MOE
Occupied housing units^2))) /Occupied
housing units estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
((SQRT(M_CROW
D^2-
((EP_CROWD/100
)^2
*HC02_VC04^2)))
/
HC01_VC04)*100
EP_NOVEH
Percentage of
households
with no vehicle
available
estimate
4 DP04 Yes DP04_0058PE HC03_VC85
MP_NOVEH
Percentage of
households
with no vehicle
available
estimate MOE
4 DP04 Yes DP04_0058PM HC04_VC85
EP_GROUPQ
Percentage of
persons in
institutionalize
d group
quarters
estimate,
2014-2018 ACS
4 SVI No (E_GROUPQ/E_TOTPOP)*100
(Persons in group quarters estimate /
Total population estimate) * 100
This calculation resulted in
some division by 0 errors in
cases where E_TOTPOP
equals 0. These rows were
revised with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
17
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_GROUPQ
Percentage of
persons in
institutionalize
d group
quarters
estimate MOE,
2014-2018 ACS
4 SVI No
((SQRT(M_GRO
UPQ^2-
((EP_GROUPQ/1
00)^2*M_TOTP
OP^2)))/E_TOTP
OP)*100
((SQRT(MOE Persons in group quarters^2
- (Estimated proportion persons in group
quarters^2 * MOE Total population^2))) /
Total population estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
EPL_POV
Percentile
Percentage of
persons below
poverty
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on EP_POV
array with 4
significant digits
EPL_UNEMP
Percentile
Percentage of
civilian (age
16+)
unemployed
estimate
1 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_UNEMP
array with 4
significant digits
EPL_PCI
Percentile per
capita income
estimate
1 SVI No
In Excel: 1-
(PERCENTRANK.I
NC on EP_PCI
array with 4
significant
digits)
Per capita income
necessarily reversed as
high income equates with
low vulnerability and vice
versa.
18
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
EPL_NOHSDP
Percentile
Percentage of
persons with
no high school
diploma (age
25+) estimate
1 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_NOHSDP
array with 4
significant digits
SPL_THEME1
Sum of series
for
Socioeconomic
theme
1 SVI No
EPL_POV +
EPL_UNEMP +
EPL_PCI +
EPL_NOHSDP
Null values (-999) removed
before calculating output
sum. Output for sums with
null values in the same row
set to -999.
RPL_THEME1
Percentile
ranking for
Socioeconomic
theme
summary
1 SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEME1
array with 4
significant digits
Null values (-999) removed
from the array before
calculating output
percentile ranks. Output
for -999 input cells set to -
999.
EPL_AGE65
Percentile
percentage of
persons aged
65 and older
estimate
2 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_AGE65 array
with 4
significant digits
EPL_AGE17
Percentile
percentage of
persons aged
17 and
younger
estimate
2 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_AGE17 array
with 4
significant digits
EPL_DISABL
Percentile
percentage of
civilian
noninstitutiona
lized
population
with a
disability
estimate
2 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_DISABL array
with 4
significant digits
19
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
EPL_SNGPNT
Percentile
percentage of
single parent
households
with children
under 18
estimate
2 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_SNGPNT
array with 4
significant digits
SPL_THEME2
Sum of series
for Household
Composition
theme
2 SVI No
EPL_AGE65 +
EPL_AGE17 +
EPL_DISABL +
EPL_SNGPNT
RPL_THEME2
Percentile
ranking for
Household
Composition
theme
summary
2 SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEME2
array with 4
significant digits
EPL_MINRTY
Percentile
percentage
minority (all
persons except
white, non-
Hispanic)
estimate
3 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_MINRTY
array with 4
significant digits
EPL_LIMENG
Percentile
percentage of
persons (age
5+) who speak
English "less
than well"
estimate
3 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_LIMENG
array with 4
significant digits
SPL_THEME3
Sum of series
for Minority
Status/Languag
e theme
3 SVI No EPL_MINRTY + EPL_LIMENG
20
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
RPL_THEME3
Percentile
ranking for
Minority
Status/Languag
e theme
3 SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEME3
array with 4
significant digits
EPL_MUNIT
Percentile
percentage
housing in
structures with
10 or more
units estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_MUNIT array
with 4
significant digits
EPL_MOBILE
Percentile
percentage
mobile homes
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_MOBILE
array with 4
significant digits
EPL_CROWD
Percentile
percentage
households
with more
people than
rooms
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_CROWD
array with 4
significant digits
EPL_NOVEH
Percentile
percentage
households
with no vehicle
available
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_NOVEH
array with 4
significant digits
EPL_GROUPQ
Percentile
percentage of
persons in
institutionalize
d group
quarters
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_GROUPQ
array with 4
significant digits
21
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
SPL_THEME4
Sum of series
for Housing
Type/
Transportation
theme
4 SVI No
EPL_MUNIT +
EPL_MOBIL +
EPL_CROWD +
EPL_NOVEH +
EPL_GROUPQ
RPL_THEME4
Percentile
ranking for
Housing Type/
Transportation
theme
4 SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEME4
array with 4
significant digits
SPL_THEMES Sum of series themes SVI No
SPL_THEME1 +
SPL_THEME2 +
SPL_THEME3 +
SPL_THEME4
Null values (-999) removed
before calculating output
sum. Output for sums with
null values in the same row
set to -999.
RPL_THEMES
Overall
percentile
ranking
SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEMES
array with 4
significant digits
Null values (-999) removed
from the array before
calculating output
percentile ranks. Output
for -999 input cells set to -
999.
F_POV
Flag - the
percentage of
persons in
poverty is in
the 90th
percentile (1 =
yes, 0 = no)
1 SVI No EPL_POV >= 0.90
F_UNEMP
Flag - the
percentage of
civilian
unemployed is
in the 90th
percentile (1 =
yes, 0 = no)
1 SVI No EPL_UNEMP >= 0.90
22
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
F_PCI
Flag - per
capita income
is in the 90th
percentile (1 =
yes, 0 = no)
1 SVI No EPL_PCI >= 0.90 Output for -999 input cells set to -999.
F_NOHSDP
Flag - the
percentage of
persons with
no high school
diploma is in
the 90th
percentile (1 =
yes, 0 = no)
1 SVI No EPL_NOHSDIP >= 0.90
F_THEME1
Sum of flags
for
Socioeconomic
Status theme
1 SVI No
F_POV +
F_UNEMP +
F_PCI +
F_NOHSDP
Null values (-999) removed
before calculating output
sum. Output for sums with
null values in the same row
set to -999.
F_AGE65
Flag - the
percentage of
persons aged
65 and older is
in the 90th
percentile (1 =
yes, 0 = no)
2 SVI No EPL_AGE65 >= 0.90
F_AGE17
Flag - the
percentage of
persons aged
17 and
younger is in
the 90th
percentile (1 =
yes, 0 = no)
2 SVI No EPL_AGE17 >= 0.90
F_DISABL
Flag - the
percentage of
persons with a
disability is in
the 90th
percentile (1 =
yes, 0 = no)
2 SVI No EPL_DISABL >= 0.90
23
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
F_SNGPNT
Flag - the
percentage of
single parent
households is
in the 90th
percentile (1 =
yes, 0 = no)
2 SVI No EPL_SNGPNT >= 0.90
F_THEME2
Sum of flags
for Household
Composition
theme
2 SVI No
F_AGE65 +
F_AGE17 +
F_DISABL +
F_SNGPNT
F_MINRTY
Flag - the
percentage of
minority is in
the 90th
percentile (1 =
yes, 0 = no)
3 SVI No EPL_MINRTY >= 0.90
F_LIMENG
Flag - the
percentage
those with
limited English
is in the 90th
percentile (1 =
yes, 0 = no)
3 SVI No EPL_LIMENG >= 0.90
F_THEME3
Sum of flags
for Minority
Status/Languag
e theme
3 SVI No F_MINRTY + F_LIMENG
F_MUNIT
Flag - the
percentage of
households in
multi-unit
housing is in
the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_MUNIT >= 0.90
24
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
F_MOBILE
Flag - the
percentage of
mobile homes
is in the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_MOBILE >= 0.90
F_CROWD
Flag - the
percentage of
crowded
households is
in the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_CROWD >= 0.90
F_NOVEH
Flag - the
percentage of
households
with no
vehicles is in
the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_NOVEH >= 0.90
F_GROUPQ
Flag - the
percentage of
persons in
institutionalize
d group
quarters is in
the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_GROUPQ >= 0.90
F_THEME4
Sum of flags
for Housing
Type/
Transportation
theme
4 SVI No
F_MUNIT +
F_MOBILE +
F_CROWD +
F_NOVEH +
F_GROUPQ
F_TOTAL
Sum of flags
for the four
themes
4 SVI No
F_THEME1 +
F_THEME2 +
F_THEME3 +
F_THEME4
Null values (-999) removed
before calculating output
sum. Output for sums with
null values in the same row
set to -999.
25
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_UNINSUR
Adjunct
variable -
Uninsured in
the total
civilian
noninstitutiona
lized
population
estimate,
2014-2018 ACS
S2701 Yes S2701_C04_001E HC04_EST_VC01
M_UNINSUR
Adjunct
variable -
Uninsured in
the total
civilian
noninstitutiona
lized
population
estimate MOE,
2014-2018 ACS
S2701 Yes S2701_C04_001M HC04_MOE_VC01
EP_UNINSUR
Adjunct
variable -
Percentage
uninsured in
the total
civilian
noninstitutiona
lized
population
estimate,
2014-2018 ACS
S2701 Yes S2701_C05_001E HC05_EST_VC01
26
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_UNINSUR
Adjunct
variable -
Percentage
uninsured in
the total
civilian
noninstitutiona
lized
population
estimate MOE,
2014-2018 ACS
S2701 Yes S2701_C05_001M HC05_MOE_VC01
E_DAYPOP
Adjunct
variable -
Estimated
daytime
population,
LandScan 2018
N/A No
Derived from LandScan 2018 -
http://web.ornl.gov/sci/landscan/index.sh
tml. We followed ORNL's instructions for
processing in ArcGIS, loading the
LandScan grid first and maintaining
WGS84 projection parameters. Using
Spatial Analyst, we ran the Zonal Statistics
as Table function to sum estimated
daytime population for each LandScan
raster cell to obtain an estimated daytime
population for each SVI 2018 census tract.
Tracts having no LandScan
cells that overlay have
been assigned null values
(i.e. -999).
LandScan daytime
populations are unavailable
for Puerto Rico, therefore
all Puerto Rico tracts and
municipios are assigned -
999.
Please see data dictionary below.
Introduction
What is Social Vulnerability?
Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado
or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a
community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or
crowded households, may affect that community’s ability to prevent human suffering and financial loss in the
event of disaster. These factors describe a community’s social vulnerability.
What is CDC Social Vulnerability Index?
ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and
Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and
emergency response planners identify and map the communities that will most likely need support before,
during, and after a hazardous event.
SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for
which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment,
minority status, and disability, and further groups them into four related themes. Thus, each tract receives a
ranking for each Census variable and for each of the four themes, as well as an overall ranking.
In addition to tract-level rankings, SVI 2010, 2014, 2016, and 2018 also have corresponding rankings at the
county level. Notes below that describe “tract” methods also refer to county methods.
How can CDC SVI help communities be better prepared for hazardous events?
SVI provides specific socially and spatially relevant information to help public health officials and local planners
better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks,
or chemical exposure.
CDC SVI can be used to:
• Allocate emergency preparedness funding by community need.
• Estimate the type and amount of needed supplies such as food, water, medicine, and bedding.
• Decide how many emergency personnel are required to assist people.
• Identify areas in need of emergency shelters.
• Create a plan to evacuate people, accounting for those who have special needs, such as those without
vehicles, the elderly, or people who do not speak English well.
• Identify communities that will need continued support to recover following an emergency or natural
disaster.
Important Notes on CDC SVI Databases
SVI 2014, 2016, and 2018 are available for download in shapefile format from
https://svi.cdc.gov/SVIDataToolsDownload.html. SVI 2014 and 2016 are also available via ArcGIS Online.
Search on “CDC’s Social Vulnerability Index.”
For SVI 2000 and 2010, keep the data in geodatabase format when downloading from
https://svi.cdc.gov/SVIDataToolsDownload.html. Converting to shapefile changes the field names.
ACS field names have changed between SVI 2016 and 2018. Name changes are noted in the Data
Dictionary below.
2 For US-wide or multi-state mapping and analysis, use the US database, in which all tracts are ranked
against one another. For individual state mapping and analysis, use the state-specific database, in which
tracts are ranked only against other tracts in the specified state.
Starting with SVI 2014, we’ve added a stand-alone, state-specific Commonwealth of Puerto Rico
database. Puerto Rico is not included in the US-wide ranking.
Starting with SVI 2014, we’ve added a database of Tribal Census Tracts
(https://www.census.gov/glossary/#term_TribalCensusTract). Tribal tracts are defined independently of,
and in addition to, standard county-based tracts. The tribal tract database contains only estimates,
percentages, and their respective margins of error (MOEs), along with the adjunct variables described in
the data dictionary below. Because of geographic separation and cultural diversity, tribal tracts are not
ranked against each other nor against standard census tracts.
Tracts with zero estimates for total population (N = 645 for the U.S.) were removed during the ranking
process. These tracts were added back to the SVI databases after ranking. The TOTPOP field value is 0,
but the percentile ranking fields (RPL_THEME1, RPL_THEME2, RPL_THEME3, RPL_THEME4, and
RPL_THEMES) were set to -999.
For tracts with > 0 TOTPOP, a value of -999 in any field either means the value was unavailable from the
original census data or we could not calculate a derived value because of unavailable census data.
Any cells with a -999 were not used for further calculations. For example, total flags do not include fields
with a -999 value.
Whenever available, we use Census-calculated MOEs. If Census MOEs are unavailable, for instance when
aggregating variables within a table, we use approximation formulas provided by the Census in Appendix
A (pages A-14 through A-17) of A Compass for Understanding and Using American Community Survey
Data here:
https://www.census.gov/content/dam/Census/library/publications/2008/acs/ACSGeneralHandbook.pdf
If more precise MOEs are required, see Census methods and data regarding Variance Replicate Tables
here: https://www.census.gov/programs-surveys/acs/technical-documentation/variance-tables.html. For
selected ACS 5-year Detailed Tables, “Users can calculate margins of error for aggregated data by using
the variance replicates. Unlike available approximation formulas, this method results in an exact margin
of error by using the covariance term.”
The U.S. Census Bureau reports that data collection errors prohibited the inclusion of income and
poverty data from Rio Arriba County, New Mexico. Please see a more detailed explanation provided by
the Census Bureau here: https://www.census.gov/programs-surveys/acs/technical-
documentation/errata/125.html.
FIPS codes are generally defined as text to preserve leading zeros (0s). If you’re working with csv files,
leading 0s are required to properly join or merge tables. ArcGIS maintains leading 0s in the FIPS code
fields of csv files. To preserve leading 0s and create an Excel file in Excel for Office 365, follow these
steps:
o Open a blank worksheet in Excel.
o Click Data in the menu bar and choose the icon From Text/CSV
o Navigate to the csv file and choose to Import
o In the dialog box that opens, choose to Transform Data
o In the Power Query Editor dialog box, for each of the FIPS columns (ST, STCNTY, FIPS for tracts
and ST, FIPS for counties), right click the column name and choose to Change Type to Text.
o As prompted in the Change Column Type dialog box, choose to Replace current. Click Close and
Load.
o Save As an Excel xlsx file.
See the Methods section below for further details.
Questions? Please visit the SVI website at http://svi.cdc.gov for additional information or email the SVI
Coordinator at svi_coordinator@cdc.gov.
3
Methods
Variables Used
American Community Survey (ACS), 2014-2018 (5-year) data for the following estimates:
Text version of overall vulnerability image:
• Socioeconomic Status
o Below Poverty
o Unemployed
o Income
o No High School Diploma
• Household Composition & Disability
o Aged 65 or Older
o Aged 17 or Younger
o Civilian with a Disability
o Single-Parent Households
• Minority Status & Language
o Minority
o Speaks English “Less than Well”
• Housing Type & Transportation
o Multi-Unit Structures
o Mobile Homes
o Crowding
o No Vehicle
o Group Quarters
For SVI 2018, we included two adjunct variables, 1) 2014-2018 ACS estimates for persons without health
insurance, and 2) an estimate of daytime population derived from LandScan 2018 estimates. These adjunct
variables are excluded from SVI rankings.
4
Raw data estimates and percentages for each variable, for each tract, are included in the database. In addition,
the margins of error (MOEs) for each estimate, at the Census Bureau standard of 90%, are also included.
Confidence intervals can be calculated by subtracting the MOE from the estimate (lower limit) and adding the
MOE to the estimate (upper limit). Because of relatively small sample sizes, some of the MOEs are high. It’s
important to identify the amount of error acceptable in any analysis.
Rankings
We ranked Census tracts within each state and the District of Columbia, to enable mapping and analysis of
relative vulnerability in individual states. We also ranked tracts for the entire United States against one another,
for mapping and analysis of relative vulnerability in multiple states, or across the U.S. as a whole. Tract rankings
are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater
vulnerability.
For each tract, we generated its percentile rank among all tracts for 1) the fifteen individual variables, 2) the four
themes, and 3) its overall position.
Theme rankings: For each of the four themes, we summed the percentiles for the variables comprising each
theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings.
The four summary theme ranking variables, detailed in the Data Dictionary below, are:
• Socioeconomic - RPL_THEME1
• Household Composition & Disability - RPL_THEME2
• Minority Status & Language - RPL_THEME3
• Housing Type & Transportation - RPL_THEME4
Overall tract rankings: We summed the sums for each theme, ordered the tracts, and then calculated overall
percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual
variable rankings. The overall tract summary ranking variable is RPL_THEMES.
Flags
Tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability.
Tracts below the 90th percentile are given a value of 0.
For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall
flag value for each tract as the number of all variable flags.
For a detailed description of SVI variable selection rationale and methods, see A Social Vulnerability Index for
Disaster Management
(https://svi.cdc.gov/A%20Social%20Vulnerability%20Index%20for%20Disaster%20Management.pdf).
Reproducibility Caveat
When replicating SVI using Microsoft Excel or similar software, results may differ slightly from databases on the
SVI website or ArcGIS Online. This is due to variation in the number of decimal places used by the different
software programs. For purposes of automation, we developed SVI using SQL programming language. Because
the SQL programming language uses a different level of precision compared to Excel and similar software,
reproducing SVI in Excel may marginally differ from the SVI databases downloaded from the SVI website. For
future iterations of SVI, beginning with SVI 2018, we plan to modify the SQL automation process for constructing
SVI to align with that of Microsoft Excel. If there are any questions, please email the SVI Coordinator at
svi_coordinator@cdc.gov.
5
CDC SVI 2018 Data Dictionary – American Community Survey field names that changed between 2016 and 2018 are noted in RED and marked
‘Yes’ or ‘No’ in the ‘Field Name Changed Since 2016?’ column.
Variables beginning with “E_” are estimates. Variables beginning with “M_” are margins of error for
those estimates. Values of -999 represent “null” or “no data.”
The four summary theme ranking variables, detailed in the Data Dictionary below, are:
Themes
1. Socioeconomic
2. Household Composition/Disability
3. Minority Status/Language
4. Housing Type/Transportation
• Socioeconomic - RPL_THEME1
• Household Composition & Disability - RPL_THEME2
• Minority Status & Language - RPL_THEME3
• Housing Type & Transportation - RPL_THEME4
The overall tract summary ranking variable is RPL_THEMES.
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
ST State-level FIPS code SVI No FIPS
In Excel, from Tract-level FIPS code, LEFT
(FIPS, 2)
STATE State name S0601 Yes NAME In Excel, use DATA|Text to Columns to extract state name GEO.display-label
ST_ABBR State abbreviation N/A No N/A Joined from Esri state boundary shapefile
STCNTY County-level FIPS code SVI No FIPS
In Excel, from Tract-level FIPS code, LEFT
(FIPS, 5)
In the county-level SVI
database, the 5-digit
STCNTY field is the FIPS
field, used for joins.
GEO.id
COUNTY County name S0601 Yes NAME In Excel, use DATA| Text to Columns to extract county name GEO.display-label
FIPS Tract-level FIPS code S0601 Yes GEO_ID In Excel, RIGHT (GEO.id, 11)
LOCATION
Text
description of
tract, county,
state
S0601 Yes NAME GEO.display-label
AREA_SQMI Tract area in square miles
Census
Cartographic
Boundary
File - U.S.
Tracts 2018
500K
No ALAND * 3.86102e-7
Conversion from square meters to square
miles
6
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_TOTPOP
Population
estimate,
2014-2018 ACS
S0601 Yes S0601_C01_001E HC01_EST_VC01
M_TOTPOP
Population
estimate MOE,
2014-2018 ACS
S0601 Yes S0601_C01_001M HC01_MOE_VC01
E_HU
Housing units
estimate,
2014-2018 ACS
DP04 Yes DP04_0001E HC01_VC03
M_HU
Housing units
estimate MOE,
2014-2018 ACS
DP04 Yes DP04_0001M HC02_VC03
E_HH
Households
estimate,
2014-2018 ACS
DP02 Yes DP02_0001E HC01_VC03
M_HH
Households
estimate MOE,
2014-2018 ACS
DP02 Yes DP02_0001M HC02_VC03
E_POV
Persons below
poverty
estimate,
2014-2018 ACS
1 B17001 Yes B17001_002E HD01_VD02
M_POV
Persons below
poverty
estimate MOE,
2014-2018 ACS
1 B17001 Yes B17001_002M HD02_VD02
E_UNEMP
Civilian (age
16+)
unemployed
estimate,
2014-2018 ACS
1 DP03 Yes DP03_0005E HC01_VC07
M_UNEMP
Civilian (age
16+)
unemployed
estimate MOE,
2014-2018 ACS
1 DP03 Yes DP03_0005M HC02_VC07
7
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_PCI
Per capita
income
estimate,
2014-2018 ACS
1 B19301 Yes B19301_001E
Fewer rows than other
variables - joined to Census
2016 tracts. Contains null
cells (i.e. -999).
HD01_VD01
M_PCI
Per capita
income
estimate MOE,
2014-2018 ACS
1 B19301 Yes B19301_001M
Fewer rows than other
variables - joined to Census
2016 tracts
HD02_VD01
E_NOHSDP
Persons (age
25+) with no
high school
diploma
estimate,
2014-2018 ACS
1 B06009 Yes B06009_002E HD01_VD03
M_NOHSDP
Persons (age
25+) with no
high school
diploma
estimate MOE,
2014-2018 ACS
1 B06009 Yes B06009_002M HD02_VD03
E_MINRTY
Minority (all
persons except
white, non-
Hispanic)
estimate,
2014-2018 ACS
3 B01001H Yes E_TOTPOP - B01001H_001E
Estimate total population – white, Non-
Hispanic population
E_TOTPOP -
HD01_VD01
M_MINRTY
Minority (all
persons except
white, non-
Hispanic)
estimate MOE,
2014-2018 ACS
3 B01001H Yes
SQRT(M_TOTPO
P^2 +
B01001H_001M
^2)
SQRT (MOE total population^2 + MOE
white, non-Hispanic^2)
SQRT(M_TOTPOP
^2 +
HD02_VD01^2)
8
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_LIMENG
Persons (age
5+) who speak
English "less
than well"
estimate,
2014-2018 ACS
3 B16005 Yes
B16005_007E +
B16005_008E +
B16005_012E +
B16005_013E +
B16005_017E +
B16005_018E +
B16005_022E +
B16005_023E +
B16005_029E +
B16005_030E +
B16005_034E +
B16005_035E +
B16005_039E +
B16005_040E +
B16005_044E +
B16005_045E +
Estimate; Native: - Speak Spanish: - Speak
English "not well" + Estimate; Native: -
Speak Spanish: - Speak English "not at all"
+ Estimate; Native: - Speak other Indo-
European languages: - Speak English "not
well" + Estimate; Native: - Speak other
Indo-European languages: - Speak English
"not at all" + Estimate; Native: - Speak
Asian and Pacific Island languages: - Speak
English "not well" + Estimate; Native: -
Speak Asian and Pacific Island languages: -
Speak English "not at all" + Estimate;
Native: - Speak other languages: - Speak
English "not well" + Estimate; Native: -
Speak other languages: - Speak English
"not at all" + Estimate; Foreign born: -
Speak Spanish: - Speak English "not well"
+ Estimate; Foreign born: - Speak Spanish:
- Speak English "not at all" + Estimate;
Foreign born: - Speak other Indo-
European languages: - Speak English "not
well" + Estimate; Foreign born: - Speak
other Indo-European languages: - Speak
English "not at all" + Estimate; Foreign
born: - Speak Asian and Pacific Island
languages: - Speak English "not well" +
Estimate; Foreign born: - Speak Asian and
Pacific Island languages: - Speak English
"not at all" + Estimate; Foreign born: -
Speak other languages: - Speak English
"not well" + Estimate; Foreign born: -
Speak other languages: - Speak English
"not at all"
HD01_VD07 +
HD01_VD08 +
HD01_VD12 +
HD01_VD13 +
HD01_VD17 +
HD01_VD18 +
HD01_VD22 +
HD01_VD23 +
HD01_VD29 +
HD01_VD30 +
HD01_VD34 +
HD01_VD35 +
HD01_VD39 +
HD01_VD40 +
HD01_VD44 +
HD01_VD45
9
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
M_LIMENG
Persons (age
5+) who speak
English "less
than well"
estimate MOE,
2014-2018 ACS
3 B16005 Yes
SQRT(B16005_0
07M ^2 +
B16005_008M
^2 +
B16005_012M
^2 +
B16005_013M
^2 +
B16005_017M
^2 +
B16005_018M
^2 +
B16005_022M
^2 +
B16005_023M
^2 +
B16005_029M
^2 +
B16005_030M
^2 +
B16005_034M
^2 +
B16005_035M
^2 +
B16005_039M
^2 +
B16005_040M
^2 +
B16005_044M
^2 +
B16005_045M
^2)
SQRT (MOE Native: - Speak Spanish: -
Speak English "not well"^2 + MOE Native:
- Speak Spanish: - Speak English "not at
all"^2 + MOE Native: - Speak other Indo-
European languages: - Speak English "not
well"^2 + MOE Native: - Speak other Indo-
European languages: - Speak English "not
at all"^2 + MOE Native: - Speak Asian and
Pacific Island languages: - Speak English
"not well"^2 + MOE Native: - Speak Asian
and Pacific Island languages: - Speak
English "not at all"^2 + MOE Native: -
Speak other languages: - Speak English
"not well"^2 + MOE Native: - Speak other
languages: - Speak English "not at all"^2 +
MOE Foreign born: - Speak Spanish: -
Speak English "not well"^2 + MOE Foreign
born: - Speak Spanish: - Speak English "not
at all"^2 + MOE Foreign born: - Speak
other Indo-European languages: - Speak
English "not well"^2 + MOE Foreign born:
- Speak other Indo-European languages: -
Speak English "not at all"^2 + MOE
Foreign born: - Speak Asian and Pacific
Island languages: - Speak English "not
well"^2 + MOE Foreign born: - Speak
Asian and Pacific Island languages: - Speak
English "not at all"^2 + MOE Foreign born:
- Speak other languages: - Speak English
"not well"^2 + MOE Foreign born: - Speak
other languages: - Speak English "not at
all"^2)
SQRT(HD02_VD0
7^2 +
HD02_VD08^2 +
HD02_VD12^2 +
HD02_VD13^2 +
HD02_VD17^2 +
HD02_VD18^2 +
HD02_VD22^2 +
HD02_VD23^2 +
HD02_VD29^2 +
HD02_VD30^2 +
HD02_VD34^2 +
HD02_VD35^2 +
HD02_VD39^2 +
HD02_VD40^2 +
HD02_VD44^2 +
HD02_VD45^2)
E_MUNIT
Housing in
structures with
10 or more
units estimate,
2014-2018 ACS
4 DP04 Yes DP04_0012E + DP04_0013E
Estimate; UNITS IN STRUCTURE - Total
housing units - 10 to 19 units + Estimate;
UNITS IN STRUCTURE - Total housing units
- 20 or more units
HC01_VC19 + HC01_VC20
10
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
M_MUNIT
Housing in
structures with
10 or more
units estimate
MOE, 2014-
2018 ACS
4 DP04 Yes
SQRT(DP04_001
2M^2 +
DP04_0013M
^2)
SQRT (MOE UNITS IN STRUCTURE - Total
housing units - 10 to 19 units^2 + MOE;
UNITS IN STRUCTURE - Total housing units
- 20 or more units^2)
SQRT(HC02_VC19
^2 +
HC02_VC20^2)
E_MOBILE
Mobile homes
estimate,
2014-2018 ACS
4 DP04 Yes DP04_0014E HC01_VC21
M_MOBILE
Mobile homes
estimate MOE,
2014-2018 ACS
4 DP04 Yes DP04_0014M HC02_VC21
E_CROWD
At household
level (occupied
housing units),
more people
than rooms
estimate,
2014-2018 ACS
4 DP04 Yes DP04_0078E + DP04_0079E
Estimate; OCCUPANTS PER ROOM -
Occupied housing units - 1.01 to 1.50 +
Estimate; OCCUPANTS PER ROOM -
Occupied housing units - 1.51 or more
HC01_VC114 + HC01_VC115
M_CROWD
At household
level (occupied
housing units),
more people
than rooms
estimate MOE,
2014-2018 ACS
4 DP04 Yes
SQRT(DP04_007
8M^2 +
DP04_0079M^2
)
SQRT (MOE OCCUPANTS PER ROOM -
Occupied housing units - 1.01 to 1.50^2+
MOE OCCUPANTS PER ROOM - Occupied
housing units - 1.51 or more^2)
SQRT(HC02_VC11
4^2 +
HC02_VC115^2)
E_NOVEH
Households
with no vehicle
available
estimate,
2014-2018 ACS
4 DP04 Yes DP04_0058E HC01_VC85
M_NOVEH
Households
with no vehicle
available
estimate MOE,
2014-2018 ACS
4 DP04 Yes DP04_0058M HC02_VC85
11
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_GROUPQ
Persons in
institutionalize
d group
quarters
estimate,
2014-2018 ACS
4 B26001 Yes B26001_001E HD01_VD01
M_GROUPQ
Persons in
institutionalize
d group
quarters
estimate MOE,
2014-2018 ACS
4 B26001 Yes B26001_001M HD02_VD01
EP_POV
Percentage of
persons below
poverty
estimate
1 S0601 Yes S0601_C01_049E HC01_EST_VC67
MP_POV
Percentage of
persons below
poverty
estimate MOE
1 S0601 Yes S0601_C01_049M HC01_MOE_VC67
EP_UNEMP Unemployment Rate estimate 1 DP03 Yes DP03_0009PE
The ACS calculated
Unemployment Rate =
E_UNEMP/civilian
population age 16+ in the
labor force
HC03_VC12
MP_UNEMP
Unemploymen
t Rate estimate
MOE
1 DP03 Yes DP03_0009PM HC04_VC12
EP_PCI
Per capita
income
estimate,
2014-2018 ACS
1 B19301 Yes B19301_001E Value is the same as E_PCI HD01_VD01
MP_PCI
Per capita
income
estimate MOE,
2014-2018 ACS
1 B19301 Yes B19301_001M Value is the same as M_PCI HD02_VD01
12
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
EP_NOHSDP
Percentage of
persons with
no high school
diploma (age
25+) estimate
1 S0601 Yes S0601_C01_033E HC01_EST_VC46
MP_NOHSDP
Percentage of
persons with
no high school
diploma (25+)
estimate MOE
1 S0601 Yes S0601_C01_033M HC01_MOE_VC46
EP_DISABL
Percentage of
civilian
noninstitutiona
lized
population
with a
disability
estimate,
2014-2018 ACS
2 DP02 Yes DP02_0071PE HC03_VC106
MP_DISABL
Percentage of
civilian
noninstitutiona
lized
population
with a
disability
estimate MOE,
2014-2018 ACS
2 DP02 Yes DP02_0071PM HC04_VC106
EP_SNGPNT
Percentage of
single parent
households
with children
under 18
estimate,
2014-2018 ACS
2 SVI No (E_SNGPNT / E_HH) * 100
(Single parent household with children
under 18 estimate / Households estimate)
* 100
This calculation resulted in
some division by 0 errors in
cases where E_HH equals
0. These rows were revised
with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
13
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_SNGPNT
Percentage of
single parent
households
with children
under 18
estimate MOE,
2014-2018 ACS
2 SVI No
((SQRT(M_SNGP
NT^2-
((EP_SNGPNT/1
00)^2*M_HH^2)
))/E_HH)*100
((SQRT(MOE Single parent households^2 -
(Estimated proportion single parent
households^2 * MOE Households^2))) /
Households estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
EP_MINRTY
Percentage
minority (all
persons except
white, non-
Hispanic)
estimate,
2014-2018 ACS
3 SVI No (E_MINRTY/E_TOTPOP)*100
(Minority estimate / Total population
estimate) * 100
This calculation resulted in
some division by 0 errors in
cases where E_HH equals
0. These rows were revised
with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
MP_MINRTY
Percentage
minority (all
persons except
white, non-
Hispanic)
estimate MOE,
2014-2018 ACS
3 SVI
((SQRT(M_MINR
TY^2-
((EP_MINRTY/10
0)^2*M_TOTPO
P^2)))/E_TOTPO
P)*100
((SQRT(MOE Minority^2 - (Estimated
proportion minority^2 * MOE Total
population^2))) / Total population
estimate) * 100
14
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
EP_LIMENG
Percentage of
persons (age
5+) who speak
English "less
than well"
estimate,
2014-2018 ACS
3 SVI and B16005 Yes
(E_LIMENG/B16
005_001E)*100
(Persons who speak English "less than
well" estimate / Population age 5 and
over estimate) * 100
This calculation resulted in
some division by 0 errors in
cases where total
population age 5 and over
equals 0. These rows were
revised with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
(E_LIMENG/
HD01_VD01)*100
MP_LIMENG
Percentage of
persons (age
5+) who speak
English "less
than well"
estimate MOE,
2014-2018 ACS
3 SVI and B16005 Yes
((SQRT(M_LIME
NG^2-
((EP_LIMENG/1
00)^2*
B16005_001M^
2)))/
B16005_001E)*
100
((SQRT(MOE Persons who speak English
less than well^2 - (Estimated proportion
persons who speak English less than
well^2 * MOE population age 5 and
over^2))) / Population age 5 and over
estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
((SQRT(M_LIMEN
G^2-
((EP_LIMENG/10
0)^2*HD02_VD01
^2)))/
HD01_VD01)*100
EP_MUNIT
Percentage of
housing in
structures with
10 or more
units estimate
4 SVI No (E_MUNIT/E_HU)*100
(Housing in structures with 10 or more
units estimate / Housing units
estimate)*100
This calculation resulted in
some division by 0 errors in
cases where E_HU equals
0. These rows were revised
with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
15
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_MUNIT
Percentage of
housing in
structures with
10 or more
units estimate
MOE
4 SVI No
((SQRT(M_MUN
IT^2-
((EP_MUNIT/10
0)^2*M_HU^2))
)/E_HU)*100
((SQRT(MOE Housing in structures with 10
or more units^2 - (Estimated proportion
housing in structures with 10 or more
units^2 * MOE Housing units^2))) /
Housing units estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
EP_MOBILE
Percentage of
mobile homes
estimate
4 DP04 Yes DP04_0014PE HC03_VC21
MP_MOBILE
Percentage of
mobile homes
estimate MOE
4 DP04 Yes DP04_0014PM HC04_VC21
EP_CROWD
Percentage of
occupied
housing units
with more
people than
rooms
estimate
4 SVI and DP04 Yes
(E_CROWD/
DP04_0002E)*1
00
(Occupied housing units with more people
than rooms estimate / Occupied housing
units estimate)*100
This calculation resulted in
some division by 0 errors in
cases where HC01_VC04
equals 0. These rows were
revised with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
E_CROWD/HC01_
VC04)*100
16
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_CROWD
Percentage of
occupied
housing units
with more
people than
rooms
estimate MOE
4 SVI and DP04 Yes
((SQRT(M_CRO
WD^2-
((EP_CROWD/10
0)^2*
DP04_0002M^2
)))/
DP04_0002E)*1
00
((SQRT(MOE Occupied housing units with
more people than rooms^2 - (Estimated
proportion of occupied housing units with
more people than rooms^2 * MOE
Occupied housing units^2))) /Occupied
housing units estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
((SQRT(M_CROW
D^2-
((EP_CROWD/100
)^2
*HC02_VC04^2)))
/
HC01_VC04)*100
EP_NOVEH
Percentage of
households
with no vehicle
available
estimate
4 DP04 Yes DP04_0058PE HC03_VC85
MP_NOVEH
Percentage of
households
with no vehicle
available
estimate MOE
4 DP04 Yes DP04_0058PM HC04_VC85
EP_GROUPQ
Percentage of
persons in
institutionalize
d group
quarters
estimate,
2014-2018 ACS
4 SVI No (E_GROUPQ/E_TOTPOP)*100
(Persons in group quarters estimate /
Total population estimate) * 100
This calculation resulted in
some division by 0 errors in
cases where E_TOTPOP
equals 0. These rows were
revised with the estimated
proportions set to 0 and
their corresponding MOEs
set to -999.
17
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_GROUPQ
Percentage of
persons in
institutionalize
d group
quarters
estimate MOE,
2014-2018 ACS
4 SVI No
((SQRT(M_GRO
UPQ^2-
((EP_GROUPQ/1
00)^2*M_TOTP
OP^2)))/E_TOTP
OP)*100
((SQRT(MOE Persons in group quarters^2
- (Estimated proportion persons in group
quarters^2 * MOE Total population^2))) /
Total population estimate) * 100
Some MOE calculations
resulted in errors because
the value under the square
root was negative. For
these rows, as the Census
Bureau suggests, we used
the formula for derived
ratios, as opposed to that
for derived proportions.
Instead of the subtraction
in the standard formula,
we add. See A Compass for
Understanding and Using
American Community
Survey Data, page A-15
(https://www.census.gov/c
ontent/dam/Census/library
/publications/2008/acs/AC
SGeneralHandbook.pdf).
EPL_POV
Percentile
Percentage of
persons below
poverty
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on EP_POV
array with 4
significant digits
EPL_UNEMP
Percentile
Percentage of
civilian (age
16+)
unemployed
estimate
1 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_UNEMP
array with 4
significant digits
EPL_PCI
Percentile per
capita income
estimate
1 SVI No
In Excel: 1-
(PERCENTRANK.I
NC on EP_PCI
array with 4
significant
digits)
Per capita income
necessarily reversed as
high income equates with
low vulnerability and vice
versa.
18
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
EPL_NOHSDP
Percentile
Percentage of
persons with
no high school
diploma (age
25+) estimate
1 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_NOHSDP
array with 4
significant digits
SPL_THEME1
Sum of series
for
Socioeconomic
theme
1 SVI No
EPL_POV +
EPL_UNEMP +
EPL_PCI +
EPL_NOHSDP
Null values (-999) removed
before calculating output
sum. Output for sums with
null values in the same row
set to -999.
RPL_THEME1
Percentile
ranking for
Socioeconomic
theme
summary
1 SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEME1
array with 4
significant digits
Null values (-999) removed
from the array before
calculating output
percentile ranks. Output
for -999 input cells set to -
999.
EPL_AGE65
Percentile
percentage of
persons aged
65 and older
estimate
2 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_AGE65 array
with 4
significant digits
EPL_AGE17
Percentile
percentage of
persons aged
17 and
younger
estimate
2 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_AGE17 array
with 4
significant digits
EPL_DISABL
Percentile
percentage of
civilian
noninstitutiona
lized
population
with a
disability
estimate
2 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_DISABL array
with 4
significant digits
19
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
EPL_SNGPNT
Percentile
percentage of
single parent
households
with children
under 18
estimate
2 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_SNGPNT
array with 4
significant digits
SPL_THEME2
Sum of series
for Household
Composition
theme
2 SVI No
EPL_AGE65 +
EPL_AGE17 +
EPL_DISABL +
EPL_SNGPNT
RPL_THEME2
Percentile
ranking for
Household
Composition
theme
summary
2 SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEME2
array with 4
significant digits
EPL_MINRTY
Percentile
percentage
minority (all
persons except
white, non-
Hispanic)
estimate
3 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_MINRTY
array with 4
significant digits
EPL_LIMENG
Percentile
percentage of
persons (age
5+) who speak
English "less
than well"
estimate
3 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_LIMENG
array with 4
significant digits
SPL_THEME3
Sum of series
for Minority
Status/Languag
e theme
3 SVI No EPL_MINRTY + EPL_LIMENG
20
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
RPL_THEME3
Percentile
ranking for
Minority
Status/Languag
e theme
3 SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEME3
array with 4
significant digits
EPL_MUNIT
Percentile
percentage
housing in
structures with
10 or more
units estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_MUNIT array
with 4
significant digits
EPL_MOBILE
Percentile
percentage
mobile homes
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_MOBILE
array with 4
significant digits
EPL_CROWD
Percentile
percentage
households
with more
people than
rooms
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_CROWD
array with 4
significant digits
EPL_NOVEH
Percentile
percentage
households
with no vehicle
available
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_NOVEH
array with 4
significant digits
EPL_GROUPQ
Percentile
percentage of
persons in
institutionalize
d group
quarters
estimate
4 SVI No
In Excel:
PERCENTRANK.I
NC on
EP_GROUPQ
array with 4
significant digits
21
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
SPL_THEME4
Sum of series
for Housing
Type/
Transportation
theme
4 SVI No
EPL_MUNIT +
EPL_MOBIL +
EPL_CROWD +
EPL_NOVEH +
EPL_GROUPQ
RPL_THEME4
Percentile
ranking for
Housing Type/
Transportation
theme
4 SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEME4
array with 4
significant digits
SPL_THEMES Sum of series themes SVI No
SPL_THEME1 +
SPL_THEME2 +
SPL_THEME3 +
SPL_THEME4
Null values (-999) removed
before calculating output
sum. Output for sums with
null values in the same row
set to -999.
RPL_THEMES
Overall
percentile
ranking
SVI No
In Excel:
PERCENTRANK.I
NC on
SPL_THEMES
array with 4
significant digits
Null values (-999) removed
from the array before
calculating output
percentile ranks. Output
for -999 input cells set to -
999.
F_POV
Flag - the
percentage of
persons in
poverty is in
the 90th
percentile (1 =
yes, 0 = no)
1 SVI No EPL_POV >= 0.90
F_UNEMP
Flag - the
percentage of
civilian
unemployed is
in the 90th
percentile (1 =
yes, 0 = no)
1 SVI No EPL_UNEMP >= 0.90
22
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
F_PCI
Flag - per
capita income
is in the 90th
percentile (1 =
yes, 0 = no)
1 SVI No EPL_PCI >= 0.90 Output for -999 input cells set to -999.
F_NOHSDP
Flag - the
percentage of
persons with
no high school
diploma is in
the 90th
percentile (1 =
yes, 0 = no)
1 SVI No EPL_NOHSDIP >= 0.90
F_THEME1
Sum of flags
for
Socioeconomic
Status theme
1 SVI No
F_POV +
F_UNEMP +
F_PCI +
F_NOHSDP
Null values (-999) removed
before calculating output
sum. Output for sums with
null values in the same row
set to -999.
F_AGE65
Flag - the
percentage of
persons aged
65 and older is
in the 90th
percentile (1 =
yes, 0 = no)
2 SVI No EPL_AGE65 >= 0.90
F_AGE17
Flag - the
percentage of
persons aged
17 and
younger is in
the 90th
percentile (1 =
yes, 0 = no)
2 SVI No EPL_AGE17 >= 0.90
F_DISABL
Flag - the
percentage of
persons with a
disability is in
the 90th
percentile (1 =
yes, 0 = no)
2 SVI No EPL_DISABL >= 0.90
23
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
F_SNGPNT
Flag - the
percentage of
single parent
households is
in the 90th
percentile (1 =
yes, 0 = no)
2 SVI No EPL_SNGPNT >= 0.90
F_THEME2
Sum of flags
for Household
Composition
theme
2 SVI No
F_AGE65 +
F_AGE17 +
F_DISABL +
F_SNGPNT
F_MINRTY
Flag - the
percentage of
minority is in
the 90th
percentile (1 =
yes, 0 = no)
3 SVI No EPL_MINRTY >= 0.90
F_LIMENG
Flag - the
percentage
those with
limited English
is in the 90th
percentile (1 =
yes, 0 = no)
3 SVI No EPL_LIMENG >= 0.90
F_THEME3
Sum of flags
for Minority
Status/Languag
e theme
3 SVI No F_MINRTY + F_LIMENG
F_MUNIT
Flag - the
percentage of
households in
multi-unit
housing is in
the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_MUNIT >= 0.90
24
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
F_MOBILE
Flag - the
percentage of
mobile homes
is in the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_MOBILE >= 0.90
F_CROWD
Flag - the
percentage of
crowded
households is
in the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_CROWD >= 0.90
F_NOVEH
Flag - the
percentage of
households
with no
vehicles is in
the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_NOVEH >= 0.90
F_GROUPQ
Flag - the
percentage of
persons in
institutionalize
d group
quarters is in
the 90th
percentile (1 =
yes, 0 = no)
4 SVI No EPL_GROUPQ >= 0.90
F_THEME4
Sum of flags
for Housing
Type/
Transportation
theme
4 SVI No
F_MUNIT +
F_MOBILE +
F_CROWD +
F_NOVEH +
F_GROUPQ
F_TOTAL
Sum of flags
for the four
themes
4 SVI No
F_THEME1 +
F_THEME2 +
F_THEME3 +
F_THEME4
Null values (-999) removed
before calculating output
sum. Output for sums with
null values in the same row
set to -999.
25
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
E_UNINSUR
Adjunct
variable -
Uninsured in
the total
civilian
noninstitutiona
lized
population
estimate,
2014-2018 ACS
S2701 Yes S2701_C04_001E HC04_EST_VC01
M_UNINSUR
Adjunct
variable -
Uninsured in
the total
civilian
noninstitutiona
lized
population
estimate MOE,
2014-2018 ACS
S2701 Yes S2701_C04_001M HC04_MOE_VC01
EP_UNINSUR
Adjunct
variable -
Percentage
uninsured in
the total
civilian
noninstitutiona
lized
population
estimate,
2014-2018 ACS
S2701 Yes S2701_C05_001E HC05_EST_VC01
26
2018
VARIABLE
NAME
2018
DESCRIPTION
THEME CENSUS or SVI TABLE(S)
FIELD
NAME
CHANGED
SINCE
2016?
2018 TABLE
FIELD
CALCULATION
CALCULATION DESCRIPTION NOTES
2016 TABLE
FIELD
CALCULATION
if changed
MP_UNINSUR
Adjunct
variable -
Percentage
uninsured in
the total
civilian
noninstitutiona
lized
population
estimate MOE,
2014-2018 ACS
S2701 Yes S2701_C05_001M HC05_MOE_VC01
E_DAYPOP
Adjunct
variable -
Estimated
daytime
population,
LandScan 2018
N/A No
Derived from LandScan 2018 -
http://web.ornl.gov/sci/landscan/index.sh
tml. We followed ORNL's instructions for
processing in ArcGIS, loading the
LandScan grid first and maintaining
WGS84 projection parameters. Using
Spatial Analyst, we ran the Zonal Statistics
as Table function to sum estimated
daytime population for each LandScan
raster cell to obtain an estimated daytime
population for each SVI 2018 census tract.
Tracts having no LandScan
cells that overlay have
been assigned null values
(i.e. -999).
LandScan daytime
populations are unavailable
for Puerto Rico, therefore
all Puerto Rico tracts and
municipios are assigned -
999.
