Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘District Resource Statement - SNAP Population’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4ef4a33e-02ab-4923-a148-6f6cfb57d4dc on 26 January 2022.
--- Dataset description provided by original source is as follows ---
Population of individuals and households receiving SNAP Benefits. For a complete list of District Resource Statement datasets,please follow this link.
--- Original source retains full ownership of the source dataset ---
Population of individuals and households receiving Supplemental Nutrition Assistance Program (SNAP), Cash Assistance (CA), or Medicaid Benefits (MA) as shown on the Borough Consultation Report.
Data Source: U.S. Census Bureau, American Community Survey (ACS) 5-year Estimates 2016-2020 .
*Note. The total population refers to households with at least one person aged 60 years and over.
This dataset includes information regarding the population of individuals and households receiving SNAP, Cash Assistance, and/or Medicaid.
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.
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.
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
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021
More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx
Population over 60 (S0101), Women Who Had a Birth in the Past 12 Months (B13002), Below Poverty Level (B17015), No Health Insurance (B27001), Household Receiving SNAP Assistance (S2201), No Internet Access (B28002), Total Population (B01003) and Language at Home (C16001)
As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.
Teens and social media
As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘District Resource Statement - SNAP Population’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4ef4a33e-02ab-4923-a148-6f6cfb57d4dc on 26 January 2022.
--- Dataset description provided by original source is as follows ---
Population of individuals and households receiving SNAP Benefits. For a complete list of District Resource Statement datasets,please follow this link.
--- Original source retains full ownership of the source dataset ---