Social_Vulnerability_Entities

Layman's description of Community area.

Census County Division name (long form).

Census County Division name (short form).

Census County Division fully qualified geographic identifier (American Community Census, geoid2). This is the field to join the data in this table with the GEOID field in the TIGER Line shapefiles for County Subdivisions, so the data can be plotted in a mapping application (e.g., ArcGIS).

County Code, (American Community Census).

State Code, (American Community Census).

Regional two-letter code. HI=Hawaii, AS=American Samoa, GU=Guam, and MP=Commonwealth of the Northern Mariana Islands.

Un-normalized principal component analysis (PCA) axis metric of Housing Characteristics: The housing characteristics index highlights the character of housing available within a community and measures the median rent (lowers vulnerability), median number of rooms in family dwellings (lowers vulnerability), and the percentage of houses that lack plumbing facilities (increases vulnerability).

Un-normalized principal component analysis (PCA) axis metric of Labor Force Structure: Labor force structure index provides an indication of the strength and stability of the labor force, including variables on the percent of females in the labor force (lowers vulnerability), the percent of those in the service industry (lowers vulnerability), as well as the percent of families with income under $10,000 (increases vulnerability).

Un-normalized principal component analysis (PCA) axis metric of Personal Disruption: The personal disruption index includes variables that might indicate unstable personal circumstances, including poverty (increases vulnerability), unemployment (increases vulnerability), and low educational attainment (increases vulnerability).

Un-normalized principal component analysis (PCA) axis metric of Population Composition: The population composition index describes demographic variables identified as indicators of socially vulnerable populations, including the percent of young children (increases vulnerability), the percent of female-headed households (increases vulnerability), and the percent of people without a Bachelor's degree (increases vulnerability).

Un-normalized principal component analysis (PCA) axis metric of Poverty: The poverty index highlights the percentage of the total population in poverty (increases vulnerability) as well as the percentage in poverty of other vulnerable groups such as children (under 18; increases vulnerability), families with children under 5 (increases vulnerability), and single-female headed families (increases vulnerability).

Leveled Housing Characteristic Metric: Housing Characteristic principal component analysis (PCA) data broken into four levels dependent upon the distribution of values within a Census County Division's region: 1, "Low" (values greater than mean plus one standard deviation); 2, "Med-Low" (values falling between the mean and the mean plus one standard deviation); 3, "Med-High" (values falling between the mean minus one standard deviation and the mean); 4, "High" (values less than the mean minus one standard deviation).

Leveled Labor Force Structure Metric: Labor Force Structure principal component analysis (PCA) data broken into four levels dependent upon the distribution of values within a Census County Division's region: 1, "Low" (values greater than mean plus one standard deviation); 2, "Med-Low" (values falling between the mean and the mean plus one standard deviation); 3, "Med-High" (values falling between the mean minus one standard deviation and the mean); 4, "High" (values less than the mean minus one standard deviation).

Leveled Personal Disruption Metric: Personal Disruption principal component analysis (PCA) data broken into four levels dependent upon the distribution of values within a Census County Division's region: 1, "Low" (values greater than mean plus one standard deviation); 2, "Med-Low" (values falling between the mean and the mean plus one standard deviation); 3, "Med-High" (values falling between the mean minus one standard deviation and the mean); 4, "High" (values less than the mean minus one standard deviation).

Leveled Population Composition Metric: Population Composition principal component analysis (PCA) data broken into four levels dependent upon the distribution of values within a Census County Division's region: 1, "Low" (values greater than mean plus one standard deviation); 2, "Med-Low" (values falling between the mean and the mean plus one standard deviation); 3, "Med-High" (values falling between the mean minus one standard deviation and the mean); 4, "High" (values less than the mean minus one standard deviation).

Leveled Poverty Metric: Poverty principal component analysis (PCA) data broken into four levels dependent upon the distribution of values within a Census County Division's region: 1, "Low" (values greater than mean plus one standard deviation); 2, "Med-Low" (values falling between the mean and the mean plus one standard deviation); 3, "Med-High" (values falling between the mean minus one standard deviation and the mean); 4, "High" (values less than the mean minus one standard deviation).

Number of underlying leveled social vulnerability metrics rated as "High". Values range from 0-5.