Graph and download economic data for Income Before Taxes: Public Assistance, Supplemental Security Income, SNAP by Race: White and All Other Races, Not Including Black or African American (CXUWELFARELB0903M) from 2003 to 2023 about supplements, assistance, social assistance, public, SNAP, food stamps, tax, white, food, income, and USA.

In 2022, 39.8 percent of Snap's workforce in the United States was Asian, whilst 4.3 percent self-identified were Black. Overall, around one percent of U.S. employees were Indigenous, and 2.6 percent were Middle Eastern, North African, or Arab. Snap Inc. owns mobile photo, video, and messaging app Snapchat.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Title SNAP Households by Household Types and Demographics 2016-2020 ACS - SNAP_HH_2020

Summary SNAP Households by type and demographics from 2016-2020 5-year period in NM Census tracts

Notes

Source US CENSUS TABLE FOOD STAMPS/SUPPLEMENTAL NUTRITION ASSISTANCE PROGRAM (SNAP) S2201 2020 ACS 5-YEAR ESTIMATE

Prepared by EMcRae_NMCDC

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=8c3e62b5050f4bcc8853ecf0130f976d

Alias Definition

ID id

GeoName Geographic Area Name

ETH_1 Estimate Total Households

ETH_2 Estimate Total Households With one or more people in the household 60 years and over

ETH_3 Estimate Total Households No people in the household 60 years and over

ETH_4 Estimate Total Households Married-couple family

ETH_5 Estimate Total Households Other family:

ETH_6 Estimate Total Households Other family: Male householder, no spouse present

ETH_7 Estimate Total Households Other family: Female householder, no spouse present

ETH_8 Estimate Total Households Nonfamily households

ETH_9 Estimate Total Households With children under 18 years

ETH_10 Estimate Total Households With children under 18 years Married-couple family

ETH_11 Estimate Total Households With children under 18 years Other family:

ETH_12 Estimate Total Households With children under 18 years Other family: Male householder, no spouse present

ETH_13 Estimate Total Households With children under 18 years Other family: Female householder, no spouse present

ETH_14 Estimate Total Households With children under 18 years Nonfamily households

ETH_15 Estimate Total Households No children under 18 years

ETH_16 Estimate Total Households No children under 18 years Married-couple family

ETH_17 Estimate Total Households No children under 18 years Other family:

ETH_18 Estimate Total Households No children under 18 years Other family: Male householder, no spouse present

ETH_19 Estimate Total Households No children under 18 years Other family: Female householder, no spouse present

ETH_20 Estimate Total Households No children under 18 years Nonfamily households

ETH_POV_1 Estimate Total Households POVERTY STATUS IN THE PAST 12 MONTHS Below poverty level

ETH_POV_2 Estimate Total Households POVERTY STATUS IN THE PAST 12 MONTHS At or above poverty level

ETH_DIS_1 Estimate Total Households DISABILITY STATUS With one or more people with a disability

ETH_DIS_2 Estimate Total Households DISABILITY STATUS With no persons with a disability

ETH_RHO_1 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone

ETH_RHO_2 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Black or African American alone

ETH_RHO_3 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER American Indian and Alaska Native alone

ETH_RHO_4 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Asian alone

ETH_RHO_5 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Native Hawaiian and Other Pacific Islander alone

ETH_RHO_6 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Some other race alone

ETH_RHO_7 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Two or more races

ETH_RHO_8 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Hispanic or Latino origin (of any race)

ETH_RHO_9 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone, not Hispanic or Latino

ETH_INC_1 Estimate Total Households HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2020 INFLATION-ADJUSTED DOLLARS) Median income (dollars)

ETH_WS_1 Estimate Total WORK STATUS Families

ETH_WS_2 Estimate Total WORK STATUS Families No workers in past 12 months

ETH_WS_3 Estimate Total WORK STATUS Families 1 worker in past 12 months

ETH_WS_4 Estimate Total WORK STATUS Families 2 or more workers in past 12 months

EPH_2 Estimate Percent Households With one or more people in the household 60 years and over

EPH_3 Estimate Percent Households No people in the household 60 years and over

EPH_4 Estimate Percent Households Married-couple family

EPH_5 Estimate Percent Households Other family:

EPH_6 Estimate Percent Households Other family: Male householder, no spouse present

EPH_7 Estimate Percent Households Other family: Female householder, no spouse present

EPH_8 Estimate Percent Households Nonfamily households

EPH_9 Estimate Percent Households With children under 18 years

EPH_10 Estimate Percent Households With children under 18 years Married-couple family

EPH_11 Estimate Percent Households With children under 18 years Other family:

EPH_12 Estimate Percent Households With children under 18 years Other family: Male householder, no spouse present

EPH_13 Estimate Percent Households With children under 18 years Other family: Female householder, no spouse present

EPH_14 Estimate Percent Households With children under 18 years Nonfamily households

EPH_15 Estimate Percent Households No children under 18 years

EPH_16 Estimate Percent Households No children under 18 years Married-couple family

EPH_17 Estimate Percent Households No children under 18 years Other family:

EPH_18 Estimate Percent Households No children under 18 years Other family: Male householder, no spouse present

EPH_19 Estimate Percent Households No children under 18 years Other family: Female householder, no spouse present

EPH_20 Estimate Percent Households No children under 18 years Nonfamily households

EPH_POV_1 Estimate Percent Households POVERTY STATUS IN THE PAST 12 MONTHS Below poverty level

EPH_POV_2 Estimate Percent Households POVERTY STATUS IN THE PAST 12 MONTHS At or above poverty level

EPH_DIS_1 Estimate Percent Households DISABILITY STATUS With one or more people with a disability

EPH_DIS_2 Estimate Percent Households DISABILITY STATUS With no persons with a disability

EPH_RHO_1 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone

EPH_RHO_2 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Black or African American alone

EPH_RHO_3 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER American Indian and Alaska Native alone

EPH_RHO_4 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Asian alone

EPH_RHO_5 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Native Hawaiian and Other Pacific Islander alone

EPH_RHO_6 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Some other race alone

EPH_RHO_7 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Two or more races

EPH_RHO_8 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Hispanic or Latino origin (of any race)

EPH_RHO_9 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone, not Hispanic or Latino

EPH_WS_2 Estimate Percent WORK STATUS Families No workers in past 12 months

EPH_WS_3 Estimate Percent WORK STATUS Families 1 worker in past 12 months

EPH_WS_4 Estimate Percent WORK STATUS Families 2 or more workers in past 12 months

SNAP_1 Estimate Households receiving food stamps/SNAP Households

SNAP_2 Estimate Households receiving food stamps/SNAP Households With one or more people in the household 60 years and over

SNAP_3 Estimate Households receiving food stamps/SNAP Households No people in the household 60 years and over

SNAP_4 Estimate Households receiving food stamps/SNAP Households Married-couple family

SNAP_5 Estimate Households receiving food stamps/SNAP Households Other family:

SNAP_6 Estimate Households receiving food stamps/SNAP Households Other family: Male householder, no spouse present

SNAP_7 Estimate Households receiving food stamps/SNAP Households Other family: Female householder, no spouse present

SNAP_8 Estimate Households receiving food stamps/SNAP Households Nonfamily households

SNAP_9 Estimate Households receiving food stamps/SNAP Households With children under 18 years

SNAP_10 Estimate Households receiving food stamps/SNAP Households With children under 18 years Married-couple family

SNAP_11 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family:

SNAP_12 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family: Male householder, no spouse present

SNAP_13 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family: Female householder, no spouse present

SNAP_14 Estimate Households receiving food stamps/SNAP Households With children under 18 years Nonfamily households

SNAP_15 Estimate Households receiving food stamps/SNAP Households No children under 18 years

SNAP_16 Estimate Households receiving food stamps/SNAP Households No children under 18 years Married-couple family

SNAP_17 Estimate Households receiving food stamps/SNAP Households No children under 18 years Other family:

SNAP_18 Estimate Households

This study features Supplemental Nutrition Assistance Program (SNAP) policy and enrollment data organized in three datasets. The data were originally collected for a companion paper, Pukelis, K. (2024). "SNAP Policies and Enrollment following the COVID-19 Pandemic." The SNAP COVID Policy Data (DS1) provides monthly data on states' adoption of policies to adjust SNAP enrollment requirements and benefits during the COVID-19 federal public health emergency, from March 2020 through June 2023. This dataset features information from all 50 states regarding policy waivers that were requested to simplify SNAP application and recertification requirements, temporarily waive recertification requirements, and provide emergency supplemental benefits. SNAP implementation procedures data from 2019 are also available for comparison. The SNAP County Enrollment Data (DS2) contains county-month level data on SNAP enrollment numbers, total benefits issued, applications, and recertifications, as well as a handful of measures on the Temporary Assistance for Needy Families (TANF) program and Medicaid. The SNAP State Enrollment Detail Data (DS3) includes state-month level data on SNAP enrollment details, including applications, recertifications, enrollment by demographic group, and information about office walk-in visits and calls to the assistance line. TANF and Medicaid state-month level data is also provided. The state enrollment file also features 62 variables detailing Louisiana case closures. County and state enrollment files contain demographic information for a limited number of states, including SNAP, TANF, and Medicaid enrollment by age group, and state-month SNAP enrollment by gender, race, and ethnicity.

Lake County, Illinois Demographic Data. Explanation of field attributes:

Total Population – The entire population of Lake County.

White – Individuals who are of Caucasian race. This is a percent.

African American – Individuals who are of African American race. This is a percent.

Asian – Individuals who are of Asian race. This is a percent.

Hispanic – Individuals who are of Hispanic ethnicity. This is a percent.

Does not Speak English- Individuals who speak a language other than English in their household. This is a percent.

Under 5 years of age – Individuals who are under 5 years of age. This is a percent.

Under 18 years of age – Individuals who are under 18 years of age. This is a percent.

18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent.

65 years of age and older – Individuals who are 65 years old or older. This is a percent.

Male – Individuals who are male in gender. This is a percent.

Female – Individuals who are female in gender. This is a percent.

High School Degree – Individuals who have obtained a high school degree. This is a percent.

Associate Degree – Individuals who have obtained an associate degree. This is a percent.

Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent.

Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent.

Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount.

No High School – Individuals who have not obtained a high school degree. This is a percent.

Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

In 2022, 22.1 percent of Snap Inc. employees in the United States in leadership roles were Asian, and 3.3 percent were Black. Overall, 4.4 percent were Hispanic or Latinx, and just one percent were Indigenous. Snap Inc. owns mobile photo, video, and messaging app Snapchat.

Characteristics of Veterans in Supplemental Nutritional Assistance Program (SNAP) household. Regardless of race, marital status, education, and income veteran households participate in SNAP less than non-veteran households.

This Indicator measures the percent of individuals who live in households in which at least one household member received SNAP (Supplemental Nutrition Assistance Program) benefits in the past 12 months, by race/ethnicity.

This report provides information at the state and town level of people served by the Connecticut Department of Social Services for the Calendar Years 2012-2024 by demographics (gender, age-groups, race, and ethnicity) at the state and town level by Medical Benefit Plan (Husky A-D, Husky limited benefit, MSP and Other Medical); Assistance Type (Cash, Food, Medical, Other); and Program (CADAP, CHCPE, CHIP, ConnTRANS, Medicaid, Medical, MSP, Refugee Cash, Repatriation, SAGA, SAGA Funeral, SNAP, Social Work Services, State Funded Medical, State Supplement, TFA). NOTE: On March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients has changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This change in methodology causes a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged.

This file contains data on race, ethnicity, and gender of U.S. farm and ranch operators collected by the 2007 Census of Agriculture.

This data collection contains information gathered in the Survey of Income and Education (SIE) conducted in April-July 1976 by the Census Bureau for the United States Department of Health, Education, and Welfare (HEW). Although national estimates of the number of children in poverty were available each year from the Census Bureau's Current Population Survey (CPS), those estimates were not statistically reliable on a state-by-state basis. In enacting the Educational Amendments of 1974, Congress mandated that HEW conduct a survey to obtain reliable state-by-state data on the numbers of school-age children in local areas with family incomes below the federal poverty level. This was the statistic that determined the amount of grant a local educational agency was entitled to under Title 1, Elementary and Secondary Education Act of 1965. (Such funds were distributed by HEW's Office of Education.) The SIE was the survey created to fulfill that mandate. Its questions include those used in the Current Population Survey regarding current employment, past work experience, and income. Additional questions covering school enrollment, disability, health insurance, bilingualism, food stamp recipiency, assets, and housing costs enabled the study of the poverty concept and of program effectiveness in reaching target groups. Basic household information also was recorded, including tenure of unit (a determination of whether the occupants of the living quarters owned, rented, or occupied the unit without rent), type of unit, household language, and for each member of the household: age, sex, race, ethnicity, marital history, and education.

StoryMap link:https://arcg.is/1OXPW1

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.

Title VI - Environmental Justice Framework

Applied to 2050 Long-Range Transportation Plan

This data collection is comprised of responses from the March and April installments of the 2008 Current Population Survey (CPS). Both the March and April surveys used two sets of questions, the basic CPS and a separate supplement for each month.The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment.In addition to the basic CPS questions, respondents were asked questions from the March supplement, known as the Annual Social and Economic (ASEC) supplement. The ASEC provides supplemental data on work experience, income, noncash benefits, and migration. Comprehensive work experience information was given on the employment status, occupation, and industry of persons 15 years old and older. Additional data for persons 15 years old and older are available concerning weeks worked and hours per week worked, reason not working full time, total income and income components, and place of residence on March 1, 2007. The March supplement also contains data covering nine noncash income sources: food stamps, school lunch program, employer-provided group health insurance plan, employer-provided pension plan, personal health insurance, Medicaid, Medicare, CHAMPUS or military health care, and energy assistance. Questions covering training and assistance received under welfare reform programs, such as job readiness training, child care services, or job skill training were also asked in the March supplement.The April supplement, sponsored by the Department of Health and Human Services, queried respondents on the economic situation of persons and families for the previous year. Moreover, all household members 15 years of age and older that are a biological parent of children in the household that have an absent parent were asked detailed questions about child support and alimony. Information regarding child support was collected to determine the size and distribution of the population with children affected by divorce or separation, or other relationship status change. Moreover, the data were collected to better understand the characteristics of persons requiring child support, and to help develop and maintain programs designed to assist in obtaining child support. These data highlight alimony and child support arrangements made at the time of separation or divorce, amount of payments actually received, and value and type of any property settlement.The April supplement data were matched to March supplement data for households that were in the sample in both March and April 2008. In March 2008, there were 4,522 household members eligible, of which 1,431 required imputation of child support data. When matching the March 2008 and April 2008 data sets, there were 170 eligible people on the March file that did not match to people on the April file. Child support data for these 170 people were imputed. The remaining 1,261 imputed cases were due to nonresponse to the child support questions. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income. Data on employment and income refer to the preceding year, although other demographic data refer to the time at which the survey was administered.

This data collection supplies standard monthly labor force data as well as supplemental data on work experience, income, noncash benefits, and migration. Comprehensive information is given on the employment status, occupation, and industry of persons 15 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence on March 1, 1992. This file also contains data covering nine noncash income sources: food stamps, school lunch programs, employer-provided group health insurance plans, employer-provided pension plans, personal health insurance, Medicaid, Medicare, CHAMPUS or military health care, and energy assistance. Information on demographic characteristics, such as age, sex, race, household relationship, and Hispanic origin, are available for each person in the household enumerated. (Source: ICPSR, retrieved 06/23/2011)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06244.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Archived as of 5/30/2025: The datasets will no longer receive updates but the historical data will continue to be available for download. In August 2018, 10 optional questions were added to all online applications through the state for health coverage, the Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance for Needy Families (TANF). It does not represent anyone who applied in-person, by telephone, by main, or any other method. In 2019, 79% of those who applied for SNAP, TANF, or health coverage applied online. The assessment does not impact eligibility for SNAP, TANF, or health coverage. Applications are filed at a household level and may represent several individuals. The application includes demographic information for the person who applied and not all members of the household. An individual may complete an assessment every time they apply for health coverage, SNAP or TANF. If an individual completed the survey more than once with multiple applications for assistance, each set of survey responses is represented on the dashboard. If an individual completes more than one assessment when applying for multiple programs, only one assessment will be represented in the data. To ensure personally identifiable information is protected, all data are presented in aggregate and data representing 20 or fewer individuals in any county will not be displayed (the demographic field will show as 0). Because some survey responses are not included in the individual race categories shown here, total counts from the individual race categories add up to less than the total for the "All" race category.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

SeeClickFix data (issue IDs & statistics) along with socioeconomic, demographic and walkability information: - Neighborhood names (nei_final_simple) - Number of unique users - Number of issue reports - Number of thanks and votes - Number of anonymous issue reports - Number of non-anonymous reports and reporters - Response times, in seconds - Median household incomes - Household types count - Population and population density - Race and ethnic population - Age range population - Marital statuses count - Employment statuses count - Food stamps count - Educational attainments count - Walk, transit, bike scores SeeClickFix data was collected in April 2018 and includes reported civil issues between January 5, 2010 and February 10, 2018. Socioeconomic and demographic information was collected from Statistical Atlas (https://statisticalatlas.com/place/New-York/Albany/Overview), which obtains its data from the US Census Bureau, and Walk, Bike and Transit Scores were collected from the WalkScore website (https://www.walkscore.com/).

The dataset contains information collected from 122 K-12 public school food service directors in Mississippi, USA, who completed an online survey designed for Mississippi school food service directors. The survey was created using Snap Surveys Desktop software. Information includes school size (number of enrolled students), percent of students participating in free or reduced-price lunch, foods sourced locally (defined as grown or produced in Mississippi), desire to purchase more or start purchasing locally sourced foods, fresh fruit and vegetable purchasing practices, experience purchasing fruits and vegetables from farmers, challenges purchasing from farmers, and interest in other farm to school (F2S) activities. School food service directors' demographic characteristics collected include gender, age, ethnicity/race, marital status, and education level. The data were collected from October 2021 to January 2022 using an online mobile and secure survey management system called Snap Online. The data were collected to obtain updated demographic and school purchasing characteristics from school food service directors in Mississippi and to determine their current abilities, experiences, and desires to engage in F2S activities. The dataset can be used to learn about K-12 public school food service directors in Mississippi but results should not be generalized to all school food service directors in Mississippi or elsewhere in the USA. Resources in this dataset:Resource Title: Mississippi Farm to School Food Service Director Dataset. File Name: MS F2S School Data Public.csvResource Description: The dataset contains information collected from 122 K-12 public school food service directors in Mississippi regarding their experience with and interest in farm to school, including purchasing local foods. It also contains demographic characteristics of the school food service directors and their fresh fruit and vegetable purchasing practices.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Mississippi Farm to School Food Service Director Data Dictionary. File Name: MS F2S School Data Dictionary Public.csvResource Description: The file contains information for variables contained in the associated dataset including names, brief descriptions, types, lengths, and values.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.