Table from the American Community Survey (ACS) S1810 disability characteristics by age. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2015 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2015, 2020, 2021, 2022, 2023ACS Table(s): S1810Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Much is known about the effects of candidate characteristics, such as gender, age, and ethnicity, on voter perceptions and support. Meanwhile, very little research on citizens' attitudes towards and stereotypes about disabled candidates and politicians has been conducted. This dataset provides the opportunity to explore how citizens perceive disabled candidates. The data comes from a survey conducted in Britain and the US in 2020 and 2021 among representative samples of the populations of approx. 3,000 respondents in each country. The surveys include two survey experiments with conjoint designs, where respondents are presented with vignettes describing two fictional candidates competing for election. A range of candidate attributes are randomly varied, including whether candidates are blind, Deaf, wheelchair users, or not described as disabled. The design allows identifying the effects of disability and other candidate attributes on respondents' perceptions of the candidates' traits, issue priorities, issue competence, and representative links. The surveys also include a range of measures of respondents' socio-demographic characteristics, in particular their experience with disability, and political attitudes.This project asks how voters perceive disabled election candidates and whether these perceptions influence their vote choice. While almost one in five people in the UK have a disability, the numbers are much lower among politicians, with currently only five MPs known to be disabled. This under-representation might hamper the representation of the interests of disabled citizens and dampen their political engagement. It also indicates unequal access to political office. While the underlying reasons are manifold, potential prejudices among voters not only pose an electoral hurdle but might also prevent disabled candidates from running and parties from nominating them. Understanding how voters perceive and evaluate disabled candidates is thus essential to addressing the barriers to elected office that disabled people face. This project collects novel survey data from the UK and other countries in order to investigate how citizens perceive disabled candidates in terms of their character traits, issue priorities and competence, and representative links. It uses survey experiments with to test how citizens react to disabled candidates while minimising social desirability bias and demand effects. The data come from a survey conducted in the UK (Wave 1: 20 May - 6 July 2020; Wave 2: 8 January - 5 February 2021) and the US (8 January - 7 February 2021). It was conducted online through the platform Qualtrics, who also provided the samples from opt-in online panels. The quota samples (UK: N=2,998; US: N=3,011) are representative of the populations along age groups, gender, and region. The survey included two near-identical survey experiments with conjoint designs. The experiment presented respondents with vignettes describing fictional election (House of Commons/Representatives) candidates, including a set of socio-demographic and political attributes which were randomly varied, and asked respondents to evaluate them. The effects of candidate characteristics on respondent evaluations can be analysed. The survey includes a range of socio-demographic and attitudinal variables, focusing particularly on disability.

Abstract copyright UK Data Service and data collection copyright owner. The main purpose of the research was to establish the prevalence rates of disability within the Northern Ireland household population. That is, the study aimed to provide us with estimates of the numbers of people with different types of disability. The definition of disability for the purposes of the NISALD was based on the concepts of the International Classification of Functioning, Disability and Health (ICF) which was developed and endorsed by the World Health Organisation. The NISALD series of questionnaires included an initial set of questions that established the type, nature and severity of disabilities. The survey instrument also included questions dedicated to collecting information on the socio-economic characteristics of the respondents and their perceptions of the environment in which they live. Fieldwork for adults and children living in private households was carried out throughout 2006 and was completed in early 2007. Results are planned to be released via a series of bulletins. The first bulletin containing top-line results from NISALD was published on 5 July 2007. Results showed that, in 2006/07, 18% of all people living in private households in Northern Ireland have some degree of disability. The prevalence rate for adults is 21% and 6% for children. If researchers or other interested parties require more in-depth analysis to be carried out requiring the use of string variables such as ‘cause of limitation’, which are not included in the UK Data Archive version, they should contact NISRA to discuss their needs. NISRA may be able to complete analysis on their behalf, thus ensuring any sensitive data remains protected. Main Topics: Disability; Activity limitation; Impairment; Health; Disabled; ICF Simple random sample Face-to-face interview Telephone interview 2006 2007 ACCESS TO PUBLIC SE... ACCIDENTS ADULTS AGE AIDS FOR THE DISABLED AIDS FOR THE HEARIN... AIDS FOR THE SPEECH... AIDS FOR THE VISUAL... BEHAVIOURAL DISORDERS BEHAVIOURAL PROBLEMS BULLYING CARE IN THE COMMUNITY CARE OF DEPENDANTS CARE OF THE DISABLED CARERS BENEFITS CHILD BEHAVIOUR CHILD BENEFITS CHILD CARE CHILDREN CHRONIC ILLNESS COGNITION DISORDERS COGNITIVE PROCESSES COMMUNICATION DISAB... COMMUNICATION PROCESS CONGENITAL DISORDERS CONTACT LENSES DISABILITIES DISABLED CHILDREN DISABLED FACILITIES DISABLED PERSONS DISMISSAL DOMESTIC EQUIPMENT ... DOMESTIC RESPONSIBI... ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL INSTITU... EMPLOYERS EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENGLISH LANGUAGE EVERYDAY LIFE Education FAMILIES FAMILY LIFE FEAR OF CRIME FINANCIAL COMPENSATION FINANCIAL INCENTIVES FINANCIAL RESOURCES FINANCIAL SUPPORT GENDER GRANTS General health and ... HATE CRIME HEALTH HEALTH PROFESSIONALS HEALTH SERVICES HEARING AIDS HEARING IMPAIRED PE... HEARING IMPAIRMENTS HOLIDAYS HOSPITAL SERVICES HOSPITALIZATION HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSING HOUSING FACILITIES HOUSING TENURE Health INCOME INDUSTRIES INFANTS INTELLECTUAL IMPAIR... INTERPERSONAL COMMU... INTERPERSONAL RELAT... JOB CHARACTERISTICS LANGUAGE DISCRIMINA... LEARNING DISABILITIES LEISURE TIME ACTIVI... LIFE STYLES LIVING CONDITIONS LOCAL COMMUNITY FAC... Labour and employment MARITAL STATUS MEDICAL EQUIPMENT A... MEMORY DISORDERS MENTAL DISORDERS MENTALLY DISABLED P... MOBILITY AIDS MOTOR PROCESSES NEIGHBOURHOODS NON VERBAL COMMUNIC... Northern Ireland OWNERSHIP AND TENURE PAIN PAIN CONTROL PAYMENTS PERSONAL HYGIENE PHYSICAL ACTIVITIES PHYSICAL DISABILITIES PHYSICAL MOBILITY PHYSICALLY DISABLED... PLACE OF BIRTH PROPERTY PUBLIC TRANSPORT QUALIFICATIONS QUALITY OF LIFE READING ACTIVITY RELIGIOUS AFFILIATION RESIDENTIAL CARE OF... RESIDENTIAL CHILD CARE RESIDENTIAL MOBILITY RESPIRATORY TRACT D... RESPITE CARE SCHOOLS FOR THE DIS... SICKNESS AND DISABI... SIGHT SOCIAL ACTIVITIES L... SOCIAL PARTICIPATION SOCIAL SERVICES SOCIAL SUPPORT SPECIAL EDUCATION SPECTACLES SPEECH IMPAIRED PER... SPORT SPOUSE S EMPLOYMENT SPOUSES STANDARD OF LIVING SURGICAL AIDS Social welfare poli... Specific diseases TRAINING COURSES TRANSPORT UNEMPLOYMENT VERBAL SKILLS VISION IMPAIRMENTS VISUALLY IMPAIRED P... WAGES WORKING CONDITIONS disorders and medic...

This layer shows disability status by sex and age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of elderly (65+) with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B18101Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Context

There are more than 26.8 million people or 2.2% of the population currently who have disabilities in India (Census 2011) which itself is said to be a very conservative estimate. There is a lot of stigma associated with the disabled community and a very high inequality in terms of social as well as monetary status between the disabled community and the entire population.

Content

The data in the csv file gives us the statewise values of the following:

1.State 2.number_disabled : It gives the total number of people in the region that are disabled. 3.total_population: It gives the total number of people in the region. 4.percent_disabled: It gives the total percentage of the people disabled in the given region. 5.literacy_rate_disabled : It represents the literacy rate of the disabled community in the region. 6.literacy_rate_general : It shows the total literacy rate of the population in the state. 7.workforce_rate_disabled : It tells us the total percent of all the disabled people that are part of the workforce in the given region.(inclusive all ages). 8.workforce_rate_general : It shows the total percent of all the people that are part of the workforce in the given region(inclusive of all ages).

This report provides county-level estimates of the number of Veterans who received VA Disability Compensation benefits during fiscal year 2020. It includes the Veterans’ total service-connected disability (SCD) rating, age group, and sex. Blank values represent small cell counts that have been suppressed to protect the identity of Veterans. In the "Total: Disability Compensation Recipients" column, each blank cell represents less than 10 Veterans. Some categories may not sum to the total due to missing information (e.g., age, sex, etc.). Source: Department of Veterans Affairs, Office of Enterprise Integration, United States Veterans Eligibility Trends & Statistics (USVETS) 2020 and Veterans Benefits Administration VETSNET FY 2020 compensation data. Prepared by National Center for Veterans Analysis & Statistics, www.va.gov/vetdata.

Household Pulse Survey (HPS): HPS is a rapid-response survey of adults ages ≥18 years led by the U.S. Census Bureau, in partnership with seven other federal statistical agencies, to measure household experiences during the COVID-19 pandemic. Detailed information on probability sampling using the U.S. Census Bureau’s Master Address File, questionnaires, response rates, and bias assessment is available on the Census Bureau website (https://www.census.gov/data/experimental-data-products/household-pulse-survey.html).

Data from adults age ≥18 years are collected by 20-minute online survey from randomly sampled households stratified by state and the top 15 metropolitan statistical areas (MSAs). Data are weighted to represent total persons age 18 and older living within households and to mitigate possible bias that can result from non-responses and incomplete survey frame. Data from adults age ≥18 years are collected by 20-minute online survey from randomly sampled households stratified by state and the top 15 metropolitan statistical areas (MSAs). For more information on this survey, see https://www.census.gov/programs-surveys/household-pulse-survey.html.

Data are weighted to represent total persons age 18 and older living within households and to mitigate possible bias that can result from non-responses and incomplete survey frame. Responses in the Household Pulse Survey (https://www.census.gov/programs-surveys/household-pulse-survey.html) are self-reported. Estimates of vaccination coverage may differ from vaccine administration data reported at COVID-19 Vaccinations in the United States (https://covid.cdc.gov/covid-data-tracker/#vaccinations).

VBA EDUCATION PROGRAM BENEFITS to provide educational opportunities to the dependents of certain disabled and deceased veterans. Spouses, surviving spouses, and children (including stepchild or adopted child) between age 18 and 26 of veterans who died from service-connected disabilities, of living veterans whose service-connected disabilities are considered permanently and totally disabling, of those who died from any cause while such service-connected disabilities were in existence, of servicepersons who have been listed for a total of more than 90 days as currently missing in action, or as currently prisoners of war, a service member who VA determines has a service connected permanent and total disability and at the time of VA’s determination is a member of the Armed Forces who is hospitalized or receiving outpatient medical care, services, or treatment; and is likely to be discharged or released from service for this service-connected disability. Children under the age of 18 may be eligible under special circumstances.

VBA BENEFIT PROGRAM to provide educational opportunities to the dependents of certain disabled and deceased veterans. Spouses, surviving spouses, and children (including stepchild or adopted child) between age 18 and 26 of veterans who died from service-connected disabilities, of living veterans whose service-connected disabilities are considered permanently and totally disabling, of those who died from any cause while such service-connected disabilities were in existence, of servicepersons who have been listed for a total of more than 90 days as currently missing in action, or as currently prisoners of war, a service member who VA determines has a service connected permanent and total disability and at the time of VA's determination is a member of the Armed Forces who is hospitalized or receiving outpatient medical care, services, or treatment; and is likely to be discharged or released from service for this service-connected disability. Children under the age of 18 may be eligible under special circumstances.

VBA BENEFIT PROGRAM to provide educational opportunities to the dependents of certain disabled and deceased veterans. Spouses, surviving spouses, and children (including stepchild or adopted child) between age 18 and 26 of veterans who died from service-connected disabilities, of living veterans whose service-connected disabilities are considered permanently and totally disabling, of those who died from any cause while such service-connected disabilities were in existence, of servicepersons who have been listed for a total of more than 90 days as currently missing in action, or as currently prisoners of war, a service member who VA determines has a service connected permanent and total disability and at the time of VA's determination is a member of the Armed Forces who is hospitalized or receiving outpatient medical care, services, or treatment; and is likely to be discharged or released from service for this service-connected disability. Children under the age of 18 may be eligible under special circumstances.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Charactertics of the veteran population such as age, period of service, educational attainment, income, disability, etc. as reported by the US Census Bureau's American Community Survey five-year estimates. The year shown in the dataset refers to the final year of the five-year reporting period (ie "2010" refers to the 2006-2010 ACS).

VBA EDUCATION PROGRAM BENEFITS to provide educational opportunities to the dependents of certain disabled and deceased veterans. Spouses, surviving spouses, and children (including stepchild or adopted child) between age 18 and 26 of veterans who died from service-connected disabilities, of living veterans whose service-connected disabilities are considered permanently and totally disabling, of those who died from any cause while such service-connected disabilities were in existence, of servicepersons who have been listed for a total of more than 90 days as currently missing in action, or as currently prisoners of war, a service member who VA determines has a service connected permanent and total disability and at the time of VA’s determination is a member of the Armed Forces who is hospitalized or receiving outpatient medical care, services, or treatment; and is likely to be discharged or released from service for this service-connected disability. Children under the age of 18 may be eligible under special circumstances.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Link: https://storymaps.arcgis.com/stories/774eed11e4ce432c89c74d011ec6c65c This dataset contains the Hampton Roads Transportation Planning Organization (HRTPO) 9 Environmental Justice (EJ) Indicators (Carless Households, Cash Public Assistance Households, Disabled Population, Elderly Population, Female Head of Household, Food Stamps/SNAP Household, Limited English Proficiency Population, Minority Population, and Low-Income/Poverty Households) at the Census Block Group level. The U.S. Census data source uses the 2017-2021 ACS 5-Year Estimates. The dataset includes Youth Population, which is not an EJ Indicator but is used in the Transportation Challenges and Strategies Long-Range Transportation Plan (LRTP) report. This data will be used for the HRTPO 2050 LRTP, for planning purposes only.Using US Census Bureau’s 2017-2021 American Community Survey data, each transportation-vulnerability key indicator was assessed by census block groups, the smallest available geography for the identified key indicators, and compared to regional averages. Any census block group with an average key indicator equal to or higher than the regional average for that indicator is identified as a transportation-vulnerable community.

The dataset contains the 9 EJ Indicators used for the HRTPO Title VI/EJ Analysis and the 2050 LRTP. The field names/aliases will change based on what platform the user is viewing the data (e.g., ArcMap, ArcPro, ArcGIS Online, Microsoft Excel, etc.). The suggestion is to view 'Field Alias Names'. To help preserve the field names and descriptions and to help the user understand the data, the following list contains the field names, field alias names, and field descriptions: (EXAMPLE: Field Name = Field Alias Name. Field Description.).

OBJECTID = OBJECTID. Unique integer field used to identify rows in tables in a geodatabase uniquely. ESRI ArcMap/ArcPro automatically defines this field.

Shape = Shape. The type of shape for the data. In this case, the EJ data are all 2021 Census Block Group (CBG) polygons. ESRI ArcMap/ArcPro automatically defines this field.

GEOID = Census GEOID. Census numeric codes that uniquely identify all administrative/legal and statistical geographic areas. In this case, the EJ data are all 2021 CBGs.

GEOID_1 = Census GEOID - Joined. Census numeric codes that uniquely identify all administrative/legal and statistical geographic areas. In this case, the EJ data are all 2021 CBGs.

Block_Grou = Census Block Group. CBG is a geographical unit used by the U.S. Census Bureau which is between the Census Tract and the Census Block levels.

TAZ = Transportation Analysis Zones (TAZ). HRTPO Transportation Analysis Zones (TAZs) that spatially join with the CBGs. Each CBG has a TAZ that intersects/overlays with the HRTPO TAZs.

Locality = Locality. Locality name: the dataset includes 16 localities (Cities of Chesapeake, Franklin, Hampton, Newport News, Norfolk, Poquoson, Portsmouth, Suffolk, Virginia Beach, and Williamsburg, and the Counties of Gloucester, Isle of Wight, James City, Southampton, Surry*, and York). The HRTPO/MPO Boundary does not include Surry County, but the data is included for HRPDC/MPA purposes.

Total_Popu = Total Population. Census Total Population.

Total_Hous = Total Households. Census Total Households.

Carless_To = Carless Total. Total Carless Households. Households with no vehicles available.

Carless_Re = Carless regional Avg. Carless Households regional average.

Carless_BG = Carless BG Avg. Carless Households Census Block Group average.

Carless_AB = Carless Above Avg (Yes/No). Carless Households above the regional average. No = Not an EJ Community, Yes = EJ Community.

Carless_Nu = Carless Numeric Value (0/1). Carless Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.

Cash_Assis = Cash Public Assistance Total. Total Households Receiving Cash Public Assistance (CPA). household that received either cash assistance or in-kind benefits.

Cash_Ass_1 = Cash Public Assistance Regional Avg. CPA Households regional average.

Cash_Ass_2 = Cash Public Assistance BG Avg. CPA Households Census Block Group average.

Cash_Ass_3 = Cash Assistance Above Avg (Yes/No). CPA Households above the regional average. No = Not an EJ Community, Yes = EJ Community.

CPA_Num = Cash Public Assistance Numeric Value (0/1). CPA Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.

Disability = Disability Total. Total Disabled Populations. non-institutionalized persons identified as having a disability of the following basic areas of functioning - hearing, vision, cognition, and ambulation.

Disabili_1 = Disability Regional Avg. Disabled Populations regional average.

Disabili_2 = Disability BG Average. Disabled Populations Census Block Group average.

Disabili_3 = Disability Above Avg (Yes/No). Disabled Populations above the regional average. No = Not an EJ Community, Yes = EJ Community.

Disabili_4 = Disability Numeric Value (0/1). Disabled Populations numerical value. 0 = Not an EJ Community, 1 = EJ Community.

Elderly_To = Elderly Total. Total Elderly Populations. People who are aged 65 and older.

Elderly_Re = Elderly Region Avg. Elderly Population regional average.

Elderly_BG = Elderly BG Avg. Elderly Population Census Block Group avg.

Elderly_Ab = Elderly Above Avg (Yes/No). Elderly Population above the regional average. No = Not an EJ Community, Yes = EJ Community.

Elderly_Num = Elderly Numeric Value (0/1). Elderly Population numerical value. 0 = Not an EJ Community, 1 = EJ Community.

Female_HoH = Female Head of Households Total. Total Female Head of Households. Households where females are the head of households with children present and no husband present.

Female_H_1 = Female Head of Households Regional Avg. Female Head of Households regional average.

Female_H_2 = Female Head of Households BG Avg. Female Head of Households Census Block Group average.

Female_H_3 = Female Head of Households Above Avg (Yes/No). Female Head of Households above the regional average. No = Not an EJ Community, Yes = EJ Community.

FemaleHoH_ = Female Head of Households Numeric Value (0/1). Female Head of Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.

Food_Stamp = Food Stamps Total. Total Households receiving Food Stamps. Households that received Supplemental Nutrition Assistance Program (SNAP) or Food Stamps.

Food_Sta_1 = Food Stamps Region Avg. Food Stamps Households regional average.

Food_Sta_2 = Food Stamps BG Avg. Food Stamps Households Census Block Group average.

Food_Sta_3 = Food Stamps Above Avg (Yes/No). Food Stamps Households above the regional average. No = Not an EJ Community, Yes = EJ Community.

FoodStamps = Food Stamps Numeric Value (0/1). Food Stamps Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.

Limited_En = Limited English Proficiency Total. Total Limited English Proficiency (LEP) Populations. Population 5 years or over who speak English less than "very well".

Limited_1 = Limited English Proficiency Regional Avg. LEP Population regional average.

Limited_2 = Limited English Proficiency BG Avg. LEP Populations Census Block group average.

Limited_3 = Limited English Proficiency Above Avg (Yes/No). LEP Population above the regional average. No = Not an EJ Community, Yes = EJ Community.

LEP_Num = Limited English Proficiency Numeric Value (0/1). LEP Population numerical value. 0 = Not an EJ Community, 1 = EJ Community.

Minority_T = Minority Total. Total Minority Populations. A person who is Black, Hispanic, American Indian, Alaskan Native or Asian American.

Minority_R = Minority Regional Average. Minority Population regional average.

Minority_B = Minority BG Average. Minority Population Census Block Group average.

Minority_A = Minority Above Average (Yes/No). Minority Population above the regional average. No = Not an EJ Community, Yes = EJ Community.

Minority_N = Minority Numeric Value (0/1). Minority Population numerical value. 0 = Not an EJ Community, 1 = EJ Community.

Total_Ho_1 = Total Households for Poverty. Census Total Low-Income/Poverty Households.

Poverty_To = Poverty Total. Total Poverty Households. A low-income household is one who income is low, relative to other households of the same size.

Poverty_Re = Poverty Regional Avg. Poverty Households regional average.

Poverty_BG = Poverty BG Avg. Poverty households Census Block Group average.

Poverty_Ab = Poverty Above Avg (Yes/No). Poverty Households above the regional average. No = Not an EJ Community, Yes = EJ Community.

Poverty_Num = Poverty Numeric Value (0/1). Poverty Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.

EJCommunit = EJ Community (Yes/No). The Census Block Group contains at least one EJ Community (>=1 = Yes). If the Census Block Group does not contain any EJ Community, the value will be = No.

EJComm_Num = EJ Community Numeric Value (0/1). The Census Block Group contains at least one EJ Community. 0 = No EJ Communities, 1 = Contains at least one EJ Community.

TotalEJ_Co = Total EJ Communities (0-9). The Total EJ Communities within the Census Block Group. The value range is 0 - 9.

HighEJ = High EJ Community (0/1). High EJ Communities are those Census Block Groups that contain 5 or more EJ Communities (value = 1). Any Census block groups with less than 5 EJ Communities will have the value = 0.

Predominan = Predominant EJ Community. The Predominant EJ Community within the Census Block Group, based on averages.

Youth_Tota = Youth Total. Total Youth Population. People who are ages 0 - 14. Not used in the Title VI/EJ Analysis, used in the Transportation

VBA BENEFIT PROGRAM to assist wartime veterans in need whose non-service- connected disabilities are permanent and total preventing them from following a substantially gainful occupation. A Veteran who meets the wartime service requirements is potential eligible if he/she is: • permanently and totally disabled for reasons not necessarily due to service, • age 65 or older, or • is presumed to be totally and permanently disabled for pension purposes because: o he/she is a patient in a nursing home for long-term care due to a disability, or o being disabled, as determined by the Commissioner of Social Security (SS) for purposes of any benefits administered by the Commissioner, such as SS disability benefits or Supplemental Security Income. Income restrictions are prescribed in 38 U.S.C. 1521. Pension is not payable to those whose estates are so large that it is reasonable they use the estate for maintenance. A Veteran meets wartime service requirements if he/she served: • a total of 90 days or more during one or more periods of war; • 90 or more consecutive days that began or ended during a period of war; or • for any length of time during a period of war if he/she was discharged or released for a service-connected disability. Veterans entering service after September 7, 1980, must also meet the minimum active duty requirement of 24 months of continuous service or the full period to which the Veteran was called to active duty. (38 U.S.C.5303(A)).

This layer is intended for researchers, students, policy makers, and the general public for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production. This layer will provide a basemap for layers related to socio-political analysis, statistical enumeration and analysis, or to support graphical overlays and analysis with other spatial data. More advanced user applications may focus on demographics, urban and rural land use planning, socio-economic analysis and related areas (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output.)