CDC SVI Documentation 2018

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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://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.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://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.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.
• 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 Tractsexternal icon (https://www.census.gov/newsroom/blogs/random-samplings/2012/07/decoding-state-county-census-tracts-versus-tribal-census-tracts.htmlexternal icon). 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.pdfpdf iconexternal icon

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.htmlexternal icon.

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.htmlexternal icon.
• 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:
  • Open a blank worksheet in Excel.
  • Click Data in the menu bar and choose the icon From Text/CSV
  • Navigate to the csv file and choose to Import
  • In the dialog box that opens, choose to Transform Data
  • 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.
  • As prompted in the Change Column Type dialog box, choose to Replace current. Click Close and Load.
  • 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.

 

Methods

Variables Used

American Community Survey (ACS), 2014-2018 (5-year) data for the following estimates:

Text version of overall vulnerability image:

• Socioeconomic Status
  • Below Poverty
  • Unemployed
  • Income
  • No High School Diploma
• Household Composition & Disability
  • Aged 65 or Older
  • Aged 17 or Younger
  • Civilian with a Disability
  • Single-Parent Households
• Minority Status & Language
  • Minority
  • Aged 5 or Older who Speaks English “Less than Well”
• Housing Type & Transportation
  • Multi-Unit Structures
  • Mobile Homes
  • Crowding
  • No Vehicle
  • 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.

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 90^th percentile of values, are given a value of 1 to indicate high vulnerability. Tracts below the 90^th 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 Managementpdf icon (https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.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.

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:

• 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.

View print only PDF of CDC SVI 2018 Documentation pdf icon[PDF – 423 KB]