<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20250910</CreaDate>
<CreaTime>14320400</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idAbs>This layer provides block group and census tract Social Vulnerability Index (SVI) estimates for Virginia. SVI for coastal counties in Virginia was estimated at the Block Group level by the VIMS SoVI project (detailed below). SVI for all other counties in Virginia was estimated at the Census Tract level by the ATSDR project (detailed below). &lt;div&gt;&lt;br /&gt;&lt;/div&gt;&lt;div&gt;&lt;b&gt;VIMS SoVI&lt;/b&gt;: Following other social vulnerability indexes, including the SoVI® developed by the Hazards &amp;amp; Vulnerability Research Institute at the University of South Carolina, this vulnerability index is based on a principal component analysis (PCA). PCA is a statistical technique that takes as its input a matrix of interrelated socioeconomic variables – in this case those considered to measure various dimensions of social vulnerability – and creates a new set of orthogonal principal components that extract the important variation the underlying input data while reducing the noise and redundancy in the data. After conducting the PCA, the researcher combines the newly created component variables into a composite index that provides a single value for each observation in the dataset, in this case a social vulnerability score. The utility of a PCA-based index is that it encapsulates a lot of information in an easily consumed form and individual observations can be ranked relative to each other. This update uses data from the 2015-2019 American Community Survey at the census block group level where available and at the census tract level where block group data is not available. It uses the following percent of population variables: income (per capita), Black or African American, Hispanic, Native American, over 65, unemployed, poverty status, no high school degree, group quarters (i.e. nursing homes and prisons), female labor force, female households, and social security. The vulnerability indices generated depend on the variables included in the PCA as well as the geographic area of the study and the component selection and weighting criteria. Thus this vulnerability index will not necessarily match the vulnerability indices created by other researchers.
See complete documentation &lt;a href='https://cmap22.vims.edu/AdaptVA/metadata/VulnerabilityRisk_Metadata/Updated_Social_Vulnerability_Index_Description_2021.pdf' target='_blank'&gt;here&lt;/a&gt;. &lt;/div&gt;&lt;div&gt;&lt;br /&gt;&lt;/div&gt;&lt;div&gt;&lt;b&gt;ATSDR&lt;/b&gt;: The CDC\ATSDR Social Vulnerability Index (SVI) is a tool, created by the Geospatial Research, Analysis and Services Program (GRASP), 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. The tract-level SVI shows the relative vulnerability of the population of every U.S. Census tract. The county-level SVI shows the relative vulnerability of every U.S. county population. The SVI ranks tracts (or counties) on 16 social factors, described in detail in the documentation. The tract (or county) rankings for individual factors are further grouped into four related themes. Thus each enumeration unit receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking. See complete documentation &lt;a href='https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html' target='_blank'&gt;here&lt;/a&gt;. For additional questions, contact the SVI Lead at svi_coordinator@cdc.gov.&lt;/div&gt;</idAbs>
<searchKeys>
<keyword>SVI</keyword>
<keyword>Social Vulnerability</keyword>
<keyword>Floodplain</keyword>
<keyword>CFPF</keyword>
<keyword>RVRLF</keyword>
<keyword>Virginia</keyword>
<keyword>US Census Bureau</keyword>
<keyword>Block Groups</keyword>
<keyword>Coastal Virginia</keyword>
<keyword>Social Vulnerability Index SOVI</keyword>
</searchKeys>
<idPurp>This layer provides block group and census tract Social Vulnerability Index (SVI) estimates for Virginia. Coastal counties were estimated at Block Group level by VIMS SoVI. SVI for all other counties was estimated at Census Tract level by ATSDR. </idPurp>
<idCredit>CDC\ATSDR\Office of Innovation and Analytics\Geospatial Research, Analysis, and Services Program (GRASP), U.S. Census Bureau, 2015-2019 American Community Survey, William &amp; Mary. </idCredit>
<resConst>
<Consts>
<useLimit>Use Constraints:The TIGER/Line Shapefile products are not copyrighted however TIGER/Line and Census TIGER are registered trademarks of the U.S. Census Bureau. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. The boundary information in the TIGER/Line Shapefiles are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions.Coordinates in the TIGER/Line shapefiles have six implied decimal places, but the positional accuracy of these coordinates is not as great as the six decimal places suggest.</useLimit>
</Consts>
</resConst>
<idCitation>
<resTitle>Social Vulnerability Index</resTitle>
</idCitation>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
</Thumbnail>
</Binary>
</metadata>
