Poverty and low-income statistics by selected demographic characteristics
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Updated May 1, 2025+ more versions
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EmailClick to copy linkLink copiedCiteGovernment of Canada, Statistics Canada (2025). Poverty and low-income statistics by selected demographic characteristics [Dataset]. http://doi.org/10.25318/1110009301-engUnique identifierhttps://doi.org/10.25318/1110009301-engDataset updatedMay 1, 2025Area coveredCanadaDescriptionPoverty and low-income statistics by visible minority group, Indigenous group and immigration status, Canada and provinces.
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HRTPO Transportation-Vulnerable Communities
- data.virginia.gov
- hub.arcgis.com
Updated Nov 25, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteHampton Roads PDC & Hampton Roads TPO (2024). HRTPO Transportation-Vulnerable Communities [Dataset]. https://data.virginia.gov/dataset/hrtpo-transportation-vulnerable-communitiesgeojson, csv, zip, arcgis geoservices rest api, html, kmlAvailable download formatsDataset updatedNov 25, 2024Dataset provided byHRPDC & HRTPOAuthorsHampton Roads PDC & Hampton Roads TPODescriptionStoryMap 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
IntroductionProviding equitable access to transportation is essential for thriving communities. Below are federal regulations to help foster transportation equity.Title VI of the Civil Rights Act prohibits discrimination based on race, color, and national origin in programs and activities receiving federal financial assistance.Environmental Justice (EJ) is the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies. The Environmental Justice Executive Order 12898, signed in 1994, reinforces the requirements of Title VI.Transportation-Vulnerability Key IndicatorsThe following transportation-vulnerability key indicators were used to identify individuals or households that may experience varying degrees of disadvantage in transportation accessibility and/or the transportation planning process:- Minority
- Low-Income Households
- Households Receiving Cash Public Assistance
- Households Receiving Food Stamps
- Carless Households
- Disabled Populations
- Elderly Populations
- Female Heads of Household
- Limited English Proficiency Households
Transportation-Vulnerable CommunitiesUsing 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
Data from: Unintended Impacts of Sentencing Reforms and Incarceration on...
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- datasets.ai
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Updated Mar 12, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteNational Institute of Justice (2025). Unintended Impacts of Sentencing Reforms and Incarceration on Family Structure in the United States, 1984-1998 [Dataset]. https://catalog.data.gov/dataset/unintended-impacts-of-sentencing-reforms-and-incarceration-on-family-structure-in-the-1984-f3960Dataset updatedMar 12, 2025Area coveredUnited StatesDescriptionThis project sought to investigate a possible relationship between sentencing guidelines and family structure in the United States. The research team developed three research modules that employed a variety of data sources and approaches to understand family destabilization and community distress, which cannot be observed directly. These three research modules were used to discover causal relationships between male withdrawal from productive spheres of the economy and resulting changes in the community and families. The research modules approached the issue of sentencing guidelines and family structure by studying: (1) the flow of inmates into prison (Module A), (2) the role of and issues related to sentencing reform (Module B), and family disruption in a single state (Module C). Module A utilized the Uniform Crime Reporting (UCR) Program data for 1984 and 1993 (Parts 1 and 2), the 1984 and 1993 National Correctional Reporting Program (NCRP) data (Parts 3-6), the Urban Institute's 1980 and 1990 Underclass Database (UDB) (Part 7), the 1985 and 1994 National Longitudinal Survey on Youth (NLSY) (Parts 8 and 9), and county population, social, and economic data from the Current Population Survey, County Business Patterns, and United States Vital Statistics (Parts 10-12). The focus of this module was the relationship between family instability, as measured by female-headed families, and three societal characteristics, namely underclass measures in county of residence, individual characteristics, and flows of inmates. Module B examined the effects of statewide incarceration and sentencing changes on marriage markets and family structure. Module B utilized data from the Current Population Survey for 1985 and 1994 (Part 12) and the United States Statistical Abstracts (Part 13), as well as state-level data (Parts 14 and 15) to measure the Darity-Myers sex ratio and expected welfare income. The relationship between these two factors and family structure, sentencing guidelines, and minimum sentences for drug-related crimes was then measured. Module C used data collected from inmates entering the Minnesota prison system in 1997 and 1998 (Part 16), information from the 1990 Census (Part 17), and the Minnesota Crime Survey (Part 18) to assess any connections between incarceration and family structure. Module C focused on a single state with sentencing guidelines with the goal of understanding how sentencing reforms and the impacts of the local community factors affect inmate family structure. The researchers wanted to know if the aspects of locations that lose marriageable males to prison were more important than individual inmate characteristics with respect to the probability that someone will be imprisoned and leave behind dependent children. Variables in Parts 1 and 2 document arrests by race for arson, assault, auto theft, burglary, drugs, homicide, larceny, manslaughter, rape, robbery, sexual assault, and weapons. Variables in Parts 3 and 4 document prison admissions, while variables in Parts 5 and 6 document prison releases. Variables in Part 7 include the number of households on public assistance, education and income levels of residents by race, labor force participation by race, unemployment by race, percentage of population of different races, poverty rate by race, men in the military by race, and marriage pool by race. Variables in Parts 8 and 9 include age, county, education, employment status, family income, marital status, race, residence type, sex, and state. Part 10 provides county population data. Part 11 contains two different state identifiers. Variables in Part 12 describe mortality data and welfare data. Part 13 contains data from the United States Statistical Abstracts, including welfare and poverty variables. Variables in Parts 14 and 15 include number of children, age, education, family type, gender, head of household, marital status, race, religion, and state. Variables in Part 16 cover admission date, admission type, age, county, education, language, length of sentence, marital status, military status, sentence, sex, state, and ZIP code. Part 17 contains demographic data by Minnesota ZIP code, such as age categories, race, divorces, number of children, home ownership, and unemployment. Part 18 includes Minnesota crime data as well as some demographic variables, such as race, education, and poverty ratio.
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