About this dataset

Context

This list ranks the 232 cities in the West Virginia by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the West Virginia by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total West Virginia Black Population: This tells us how much of the entire West Virginia Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 304 cities in the Louisiana by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the Louisiana by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Louisiana Black Population: This tells us how much of the entire Louisiana Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

THE POLL

As part of Cards Against Humanity Saves America, this poll is funded for one year of monthly public opinion polls. Cards Against Humanity is asking the American people about their social and political views, what they think of the president, and their pee-pee habits.

To conduct their polls in a scientifically rigorous manner, they partnered with Survey Sampling International — a professional research firm — to contact a nationally representative sample of the American public. For the first three polls, they interrupted people’s dinners on both their cell phones and landlines, and a total of about 3,000 adults didn’t hang up immediately. They examined the data for statistically significant correlations which can be found here: [https://thepulseofthenation.com/][1]

Content

• Polls are released each month (they are still polling so this will be updated each month)
  
• Row one is the header and contains the questions
  
• Each row is one respondent's answers
  

Questions in the Sep 2017 poll:

• Income
• Gender
• Age
• Age Range
• Political Affiliation
• Do you approve or disapprove of how Donald Trump is handling his job as president?
• What is your highest level of education?
• What is your race?
• What is your marital status?
• What would you say is the likelihood that your current job will be entirely performed by robots or computers within the next decade?
• Do you believe that climate change is real and caused by people, real but not caused by people, or not real at all?"
• How many Transformers movies have you seen?
• Do you agree or disagree with the following statement: scientists are generally honest and are serving the public good.
• Do you agree or disagree with the following statement: vaccines are safe and protect children from disease.
• "How many books, if any have you read in the past year?"
• Do you believe in ghosts?
• What percentage of the federal budget would you estimate is spent on scientific research?
• "Is federal funding of scientific research too high too low or about right?"
• True or false: the earth is always farther away from the sun in the winter than in the summer.
• "If you had to choose: would you rather be smart and sad or dumb and happy?"
• Do you think it is acceptable or unacceptable to urinate in the shower?

Questions from Oct 2017 poll

• Income
• Gender
• Age
• Age Range
• Political Affiliation
• Do you approve or disapprove of how Donald Trump is handling his job as president?
• What is your highest level of education?
• What is your race?
• From what you have heard or seen do you mostly agree or mostly disagree with the beliefs of White Nationalists?
• If you had to guess what percentage of Republicans would say that they mostly agree with the beliefs of White Nationalists?
• Would you say that you love America?
• If you had to guess, what percentage of Democrats would say that they love America?
• Do you think that government policies should help those who are poor and struggling in America?
• If you had to guess, what percentage of Republicans would say yes to that question?
• Do you think that most white people in America are racist?
• If you had to guess, what percentage of Democrats would say yes to that question?
• Have you lost any friendships or other relationships as a result of the 2016 presidential election?
• Do you think it is likely or unlikely that there will be a Civil War in the United States within the next decade?
• Have you ever gone hunting?
• Have you ever eaten a kale salad?
• If Dwayne "The Rock" Johnson ran for president as a candidate for your political party, would you vote for him?
• Who would you prefer as president of the United States, Darth Vader or Donald Trump?

Questions from Nov 2017 poll

• Income
• Gender
• Age
• Age Range
• In politics today, do you consider yourself a Democrat, a Republican or Independent?
• Would you say you are liberal, conservative, or moderate?
• What is your highest level of education? (High school or less, Some college, College degree, Graduate degree)
• What is your race? (white, black, latino, asian, other)
• Do you live in a city, suburb, or small town?
• Do you approve, disapprove, or neither approve nor disapprove of how Donald Trump is handling his job as president?
• Do you think federal funding for welfare programs in America should be increased, decreased, or kept the same?
• Do you think poor black people are more likely to benefit from welfare programs than poor white people?
• Do you think poor people in cities are more likely to benefit from welfare programs than poor people in small towns?
• If you had to choose, would you rather live in a more equal society or a more unequal society?

Acknowledgements

These polls are from Cards Against Humanity Saves America and the raw data can be found here: [https://thepulse...

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, 2015-2019 American Community Survey 5-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..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 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 delineation lists 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:An "**" entry in 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.An "-" entry in the estimate column indicates that 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, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

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, 2017-2021 American Community Survey 5-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..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..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 2017-2021 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 delineation lists 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.

Data Source: Open Data DC and American Community Survey (ACS) 5-Year Estimates

Why This Matters

Urban green spaces provide an array of health benefits, including protection from extreme heat, reducing stress and anxiety, and offering a place to stay physically active.

Parks can serve as a social gathering space in neighborhoods, offering a location for residents to host events, play sports, and connect with their neighbors. This benefit can be particularly beneficial for elderly individuals as they are more likely to suffer from social isolation.

While the District is considered a national leader in park equity today, this has not always been the case. Until 1954, many DC parks and playgrounds were segregated, either prohibiting their use by Black residents or only allowing them to be used during certain hours.

The District Response

The District consistently ranks well nationally for park equity, receiving a higher Trust for Public Land ParkScore®rating than any other city for four consecutive years (2021-2024). Unlike most cities in the US, District residents have access to a similar amount of park space regardless of their neighborhood’s racial demographics.

The District Department of Transportation’s Urban Forestry Division is on track to reach a goal of tree canopy coverage for 40% of the District, promoting better air quality and cooling our neighborhoods. Residents can also request the planting of a new street tree near them.

The Department of Parks and Recreation and the Department of General Services are modernizing and renovating parks across the District to improve park services, safety, and utilization.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015.

The Heat Vulnerability Index (HVI) shows neighborhoods whose residents are more at risk for dying during and immediately following extreme heat. It uses a statistical model to summarize the most important social and environmental factors that contribute to neighborhood heat risk. The factors included in the HVI are surface temperature, green space, access to home air conditioning, and the percentage of residents who are low-income or non-Latinx Black. Differences in these risk factors across neighborhoods are rooted in past and present racism. Neighborhoods are scored from 1 (lowest risk) to 5 (highest risk) by summing the following factors and assigning them into 5 groups (quintiles):

Median Household Income (American Community Survey 5 year estimate, 2016-2020) Percent vegetative cover (trees, shrubs or grass) (2017 LiDAR, NYC DOITT) Percent of population reported as Non-Hispanic Black on Census 2020 Average surface temperature Fahrenheit from ECOSSTRESS thermal imaging, August 27,2020 Percent of households reporting Air Conditioning access, Housing ad Vacancy Survey, 2017

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL STATISTICAL ORGANIZATION OF YEMEN (CSO)

The primary objective of LFS 2013-2014 was to provide current data on the employment and unemployment situation at national and governorate level using the preliminary version of the new standards concerning statistics of work, employment and labour underutilization on adopted by the 19th International Conference of Labour Statisticians (Geneva, October 2013).

---> The survey was then designed to meet five main measurement objectives as follows: 1- To provide current data on the number of employed, unemployed, and underemployed, and their demographic and social characteristics, including the size of women's participation in economic activity with a view to future policies in expanding their participation in the labour market. 2- To collect data on qualifications of the labour force and participation in training programmes of the youth population and other data requirements for improving the performance of employers through knowledge on the levels of skill available to them. 3- To measure the volume and characteristics of labour migration of Yemenis outside the country. 4- To provide information on the amount of wages and employment-related income in different occupations, branches of economic activity and sectors of employment. 5- To collect appropriate data for evaluating the microfinance projects funded through the Social Fund for Development.

Given the extent and diversity of data requirements, the survey was designed to spread over a one-year period, built around the five objectives of the survey. The core labour force survey was conducted throughout the four quarters of the survey period and incorporated the measurement of income from employment along the conventional items of data collection. Data on qualifications and participation in training was collected on the third quarter and on labour migration on the second quarter of the survey programme. Data collection on microfinance was undertaken as a separate survey over the four quarters.

Geographic coverage

Survey operations were carried out in all governorates except parts where recent events have disturbed the normal course of economic activity. In these circumstances, special procedures were used for compensation, either through the replacement of those areas with other areas having otherwise similar characteristics in the respective strata or through the adjustment of the sampling weights for missing values. There were 14 such cases, 5 each in quarters 1 and 4, and 2 each in quarters 2 and 3.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The labour force survey covered the civilian non-institutional settled population excluding certain areas with difficult access or low population densities, in particular, the nomad population, displaced populations who are homeless, population living in public housing (boarding, hotels, prisons, hospitals, etc.), individuals enlisted in the Armed Forces, who are residing permanently within camps and do not spend most days of the year with their families. Similarly, for marine crews and expatriates outside the country and other categories of persons in remote islands.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL STATISTICAL ORGANIZATION OF YEMEN (CSO)

The sample design of the labour force survey of Yemen 2013-2014 is a two-stage stratified sample of enumeration areas in the first stage of sampling and a fixed number of sample households at the second stage of sampling. The resulting sample is spread evenly over the four quarters of the survey period.

Accordingly, the Central Statistics Organization (CSO) has drawn a stratified sample of census enumeration areas recomposed as primary sampling units (PSUs). Sample selection has been made with probability proportional to the number of households as determined in the 2004 population on census. In the second stage of sampling, after relisting of the sample enumeration areas, a fixed number of households (16 sample households) are drawn as clusters with equal probability from each sample enumeration area. The strata consist of the urban and rural areas of the 21 governorates in Yemen.

According to the sample design, urban areas are oversampled and rural areas under-sampled. This is because a relatively larger sample size is required in urban areas where heterogeneity is greater in comparison with rural areas. Also, because the cost of transportation and field operations is relatively greater in rural areas, it is more cost effective to under sample the rural areas relative to the less costly operations in urban areas. The differential sampling rates are then corrected through the sample weights so that the final results accurately reflect to the overall employment pattern.

The sample selection of the cluster of 16 households in each sample enumeration area was drawn after fresh listing of the totality of the households living in the sample enumeration area at the time of listing. This procedure updates the census information that dates back to 2004. The listing operations are carried out in each quarter before survey interviewing. The updated lists are send to CSO in Sana'a for data entry and sample selection of households for transmission to the survey team in each area. Instructions were given so that sample households that could not be found in the field or were absent or refused to be interview should not be substituted with other households as this procedure may introduce bias in the results. Instructions were also given that in cases where the minimum number of households in the sample enumeration areas was to be found to be less than the required 16 in each quarter, all households in the enumeration area should be taken in the sample.

The total sample size was determined on the basis of the requirement of producing national estimates of the unemployment rate with 1.5% margin of errors at the national level, assuming an overall non-response rate of 15%, and a design effect of 3. For the determination of the national sample size, the expected unemployment rate was set at 15% and the expected number of sample households to reach one person of working age, 15 years old and over, in the labour force was set at 0.6.

A more detailed description of the allocation of sample across governorates is provided in the report document available among external resources in English.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire of the Yemen LFS 2013-2014 was designed on the basis of the ILO model LFS questionnaire (version A) and other national LFS questionnaires used in the region. The draft questionnaire was field tested with six households in Sana’a, each member of the field staff interviewing one sample household in his or her area. The experience gained in the field test was reviewed and led to some modifications of the draft questionnaire.

Apart from the cover page and the back page, the core LFS questionnaire contains 52 questions. There are 11 questions on the social and demographic characteristics of the household members in the household roster. In the individual questionnaire addressed to the working age population 15 years of age or older, there are 3 questions to identify the employed persons and 19 questions on their employment characteristics including timerelated underemployment followed by 8 additional questions on income from employment. The individual questionnaire also includes 5 questions to identify the unemployment and the potential labour force and 5 follow-up questions on unemployment characteristics.

Cleaning operations

----> Raw Data

Data processing involved data entry, coding, editing and tabulation of the survey results. Data entry was carried out in parallel with the interviewing of sample households. It was conducted at the Central Statistical Organization headquarter in Sana'a where all data processing operations except tabulation were centralized.

The supervisory staff of the data entry operations was responsible for editing the questionnaires before actual data entry. Editing at this stage involved review of the questionnaire regarding its filled-in contents including ensuring that there is no missing block of information for household members aged 15 years old and over and correct coding of occupation, branch of economic activity and other variables.

The data files were further processed at ILO headquarters in Geneva. They were first converted into a single file with 86,778 records and augmented with several fields, in particular, the sampling weights (“weight”) and the key derived variables: employed (E), unemployed (U), time-related underemployment (TRU), potential labour force (PLF) as well as other derived variables such as informal sector employment (IS) and informal employment (IE).

----> Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated

AbstractThe urban-wildland interface is expanding and increasing the risk of human-wildlife conflict. Some wildlife species adapt to or avoid living near people, while others select for anthropogenic resources and are thus more prone to conflict. To promote human-wildlife coexistence, wildlife and land managers need to understand how conflict relates to habitat and resource use in the urban-wildland interface. We investigated black bear (Ursus americanus) habitat use across a gradient of human disturbance in a North American hotspot of human-black bear conflict. We used camera traps to monitor bear activity from July 2018 to July 2019, and compared bear habitat use to environmental and anthropogenic variables and spatiotemporal probabilities of conflict. Bears predominantly used areas of high vegetation productivity, avoided higher human densities, and increased their nocturnality near people. Still, bears used more high-conflict areas in summer and autumn, specifically rural lands with ripe crops. Our results suggest that bears are generally modifying their behaviours in the urban-wildland interface through spatial and temporal avoidance of humans, which may facilitate coexistence. However, conflict still occurs, especially in autumn when hyperphagia and peak crop availability attract bears to abundant rural food resources. To improve conflict mitigation practices, we recommend targeting seasonal rural attractants such as with pre-emptive fruit picking, bear-proof compost containment, and other forms of behavioural deterrence. By combining camera-trap monitoring of a large carnivore along an anthropogenic gradient with conflict mapping, we provide a framework for evidence-based improvements in human-wildlife coexistence., MethodsWe set 54 camera traps within a 80 km2 area in and adjacent to Sooke, Vancouver Island, BC, Canada to assess spatial and temporal variation in bear distribution and habitat use along a gradient of human disturbance from urban to wild. We deployed cameras following a stratified random design to representatively allocate cameras based on the proportion of the survey area falling within each of three strata: urban (n = 11 cameras), rural (n = 19), or wild (n = 24). We aimed for >200 m between neighboring camera sites (mean = 446 m, range = 147-1467 m) to maintain spatial independence. Within strata, sampling distribution was randomized where possible. Due to the abundance of private land, urban and rural camera sites were selected from a candidate list of participating landowners provided by the local environmental non-governmental organization. Rural sites were either within agricultural land cover or low development areas, while urban sites were in town and close to other homes. Wild sites were in forested areas with minimal disturbance from human development, consisting of 21 in Sea to Sea Regional Park and three on undeveloped T’Sou-ke Nation lands. To randomize sampling locations within the main accessible block of the regional park, a 500 by 500 m grid was overlaid on park trail maps and cameras were placed in 10 random cells that contained a trail. The T’Sou-ke Nation forest sites and regional park sites on the northwest edge were only accessible by a single hiking trail, so cameras were set a minimum of 200 m apart. To avoid excessive human photos and privacy concerns, we avoided setting cameras directly on the main hiking trails in the park and T’Sou-ke Nation lands, and either targeted adjacent game and low-use human trails within the selected cell or set cameras off the main trail. Deployment occurred between July 18- August 20, 2018. To detect any seasonal variation in black bear habitat use, all cameras remained deployed for approximately one year, and were retrieved between July 16-19, 2019. We used a combination of three camera trap models (Reconyx PC900, Reconyx HC600, and Browning Strike Force HD Pro) randomly allocated across strata to reduce potential effects of different detectability between camera models. We set cameras at locations to maximize the probability of detecting bears that occurred there, using local knowledge of where bears moved across urban or rural properties, or the presence of animal trails and sign. Per site, one camera was set on a tree, approximately one metre above the ground, at high sensitivity, with a one second delay between triggers (one image per trigger as bears are large enough to be captured without a sequence and this saves battery and memory card space), and facing open spaces such as meadows, lawns, or trails. Black bears have shown a preference for using low-use human paths because of the ease of movement and increased shrub vegetation containing berries. Where possible, cameras faced an intersection of multiple animal and/or low-use human trails. We visited camera traps every 2... Visit https://dataone.org/datasets/sha256%3A494dbb159169867e98065de4e2002a26799bd09f45910ad01adb83edaee50a88 for complete metadata about this dataset.

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, 2019 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..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..The 2019 American Community Survey (ACS) data generally reflect the September 2018 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:An "**" entry in 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.An "-" entry in the estimate column indicates that 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, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

Abstract

The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.

Analysis unit

Households and individuals

Universe

South Africa: the survey covered all members of black households in the rural Eastern Cape and black and colored households in urban Western Cape.

Kind of data

Sample survey data [ssd]

Sampling procedure

South Africa: In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:

1. Predominantly black or colored EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Stats SA) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)

These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:

1. Geographical areas by population group of head of household for person weighted (African/Black or Colored)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).

These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.

All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.

Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.

Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.

The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.

The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.

Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban colored (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban colored households.

Brazil: Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"

The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.

The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.

To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:

1. At least

The purpose of the study was to assess the impact of Latino ethnicity on pretrial release decisions in large urban counties. The study examined two questions: Are Latino defendants less likely to receive pretrial releases than non-Latino defendants? Are Latino defendants in counties where the Latino population is rapidly increasing less likely to receive pretrial releases than Latino defendants in counties where the Latino population is not rapidly increasing? The study utilized the State Court Processing Statistics (SCPS) Database (see STATE COURT PROCESSING STATISTICS, 1990-2004: FELONY DEFENDANTS IN LARGE URBAN COUNTIES [ICPSR 2038]). The SCPS collects data on felony cases filed in state courts in 40 of the nation's 75 largest counties over selected sample dates in the month of May of every even numbered year, and tracks a representative sample of felony case defendants from arrest through sentencing. Data in the collection include 118,556 cases. Researchers supplemented the SCPS with county-level information from several sources: Federal Bureau of Investigation Uniform Crime Reporting Program county-level data series of index crimes reported to the police for the years 1988-2004 (see UNIFORM CRIME REPORTS: COUNTY-LEVEL DETAILED ARREST AND OFFENSE DATA, 1998 [ICPSR 9335], UNIFORM CRIME REPORTING PROGRAM DATA [UNITED STATES]: COUNTY-LEVEL DETAILED ARREST AND OFFENSE DATA, 1990 [ICPSR 9785], 1992 [ICPSR 6316], 1994 [ICPSR 6669], 1996 [ICPSR 2389], 1998 [ICPSR 2910], 2000 [ICPRS 3451], 2002 [ICPSR 4009], and 2004 [ICPSR 4466]). Bureau of Justice Statistics Annual Survey of Jails, Jurisdiction-Level data series for the years 1988-2004 (see ANNUAL SURVEY OF JAILS: JURISDICTION-LEVEL DATA, 1990 [ICPSR 9569], 1992 [ICPSR 6395], 1994 [ICPSR 6538], 1996 [ICPSR 6856], 1998 [ICPSR 2682], 2000 [ICPSR 3882], 2002 [ICPSR 4428], and 2004 [ICPSR 20200]). Bureau of Justice Statistics National Prosecutors Survey/Census data series 1990-2005 (see NATIONAL PROSECUTORS SURVEY, 1990 [ICPSR 9579], 1992 [ICPSR 6273], 1994 [ICPSR 6785], 1996 [ICPSR 2433], 2001 census [ICPSR 3418], and 2005 [ICPSR 4600]). United States Census Bureau State and County Quickfacts. National Center for State Courts, State Court Organization reports, 1993 (see NCJ 148346), 1998 (see NCJ 178932), and 2004 (see NCJ 212351). Bureau of Justice Statistics Felony Defendants in Large Urban Counties reports, 1992 (see NCJ 148826), 1994 (see NCJ 164616), 1996 (see NCJ 176981), 1998 (see NJC 187232), 2000 (see NCJ 202021), and 2002 (see NJC 210818). The data include defendant level variables such as most serious current offense charge, number of charges, prior felony convictions, prior misdemeanor convictions, prior incarcerations, criminal justice status at arrest, prior failure to appear, age, gender, ethnicity, and race. County level variables include region, crime rate, two year change in crime rate, caseload rate, jail capacity, two year change in jail capacity, judicial selection by election or appointment, prosecutor screens cases, and annual expenditure on prosecutor's office. Racial threat stimuli variables include natural log of the percentage of the county population that is Latino, natural log of the percentage of the county population that is African American, change in the percentage of the county population that is Latino over the last six years and change in the percentage of the county population that is African American over the last six years. Cross-level interaction variables include percentage minority (Latino/African American) population zero percent to 15 percent, percentage minority (Latino/African American) population 16 percent to 30 percent, and percentage minority (Latino/African American) population 31 percent or higher.

Link to this report's codebookUnfulfilled Promise of Racial EqualityUS states unequally distribute resources, services, and opportunities by raceThe US is failing to deliver on its promise of racial equality. While the US founding documents assert that ‘all men are created equal,’ this value is not demonstrated in outcomes across areas as diverse and varied as education, justice, health, gender, and pollution. On average, white communities receive resources and services at a rate approximately three times higher, than the least-served racial community (data on Asian, Black, Indigenous, Hawaiian and Pacific Islander, Hispanic, Multiracial and ‘Other’ racial communities, were used as available). Evidence shows that unequal treatment impacts each of these communities, however, it is most often Black and Indigenous communities that are left the furthest behind. When states are scored on how well they deliver the United Nations Sustainable Development Goals (SDGs) to the racial group least served, no state is even halfway to achieving the SDGs by 2030 (see Figure 1). To learn more about the Sustainable Development Goals, see the section “SDGs & Accountability.”One example of this inequality is in life expectancy. In Figure 2, the scatter plot on the left demonstrates a pattern in which Black and Indigenous communities, represented by orange and green dots closest to the bottom of the graph, are consistently the communities with least access to years of life. In the graph on the right, each box represents a racial population in a specific state, the boxes are organized from left to right, lowest to highest, according to the life expectancy for that group and state. The graph shows how large the gap is in life expectancy across racial communities and states, with green and orange boxes, representing Indigenous and Black communities respectively, clustered to the left of the graph.Patterns like this one, demonstrating both deep and wide racial inequalities, occur across the 51 indicators this analysis includes, covering 12 of 17 SDGs. In a similar example (Figure 3), a pattern emerges where white students are least likely to attend a school where 75 percent or more of its students receive free or reduced cost lunch when compared to all other racial groups. In the most unequal state, North Dakota, Indigenous students attend high poverty schools at a rate 42 times higher than white students. As Figure 3 shows, although the percentage of students from the least served racial group attending high poverty schools ranges from 2 percent in Vermont to 73 percent in Mississippi, the group least served, represented by the dots closest to the top of the graph, are most often Hispanic and Indigenous communities.Lack of Racial DataMore, and better, racially and ethnically disaggregated data are needed to assess delivery of racial equalityA significant barrier to evaluating progress is the unavailability of racial data across all areas of measurement. For too many important topic areas, such as food insecurity, maternal mortality and lead in drinking water, there is no racial data available at the state level. Even in the areas where there is some racial data, it is often not available for all groups (see Figure 4). Particularly missing, were measures of environmental justice; in Goals focusing on Water, Clean Energy, and Life on Land (Goals 6, 7, and 15), racial data was not found for any indicators, despite the fact that there is research indicating that clean water, for example, is unequally distributed across racial groups. The reasons for these gaps vary. For some indicators, data is not tracked through a nationally organized database, for other indicators, the data is old and out of date, and in many cases, surveys are not large enough to disaggregate by race. As was made clear with the disparate impacts of COVID-19 (for example, see CDC 2020), understanding to whom resources are being distributed has real life implications and is an important part of holding democratic institutions accountable to promises of equality.People are often left behind due to a combination of intersecting identities and factors; they remain hidden in averages. Evaluating the Leave No One Behind Agenda through the lens of gender, ability, class and other identities are undoubtedly important and urgent. Disaggregating data along two axes such as race and location—is revealing. But an even more refined analysis using multilevel disaggregation, such as looking at women and race in urban settings, would likely reveal even starker inequalities. Those are not included here and are important areas for future work. Other areas for further exploration include the use of longitudinal data to understand how these inequalities are changing over time.Though the full extent of this unequal treatment is unknown, this analysis sheds some light on the clouded story told by state averages. Whole group averages leave out important information, particularly about inequality. Racially disaggregated data is essential for holding governments accountable to the promise of racial equity. Without it, it is too easy to hide who is being excluded and left behind.SDGs and AccountabilitySDGs and AccountabilityThe SDGs can be an accountability tool to address racial inequality. This would not be the first time UN frameworks have been used to call attention to racial inequality in the US. In 1951, the Civil Rights Congress (CRC) led by William L. Patterson and Paul Robeson put a petition to the UN, named: “We Charge Genocide,” which charged that the United States government was in violation of the Charter of the United Nations and the Convention on the Prevention and Punishment of the Crime of Genocide (Figure 5). While this attempt did not succeed in charging the US government with genocide, it is a central example of how international instruments can be used to apply localized pressure to advance civil rights.All 193 member countries of the UN, including the United States, signed on to the Sustainable Development Goals in 2015, to be achieved by 2030. The Goals cover 17 wide-ranging topics, with 169 specific targets for action (Figure 6). The first agenda of the SDGs, the Leave No One Behind Agenda (LNOB), requires that those left furthest behind by governments must have the SDGs delivered to them first. The results of this project demonstrate that in a US-context, those left furthest behind would undoubtedly include Asian, Black, Indigenous, Hawaiian and Pacific Islander, Hispanic, Multiracial and ‘Other’ racial communities. The SDGs can offer a template for US states attempting to deliver on their promise of racial equality. The broad topic areas covered by the SDGs, in combination with the Leave No One Behind agenda, can be a tool to hold states accountable for addressing racial inequalities when and through developing solutions for clean water, quality education, ending hunger, delivering justice and more. This highlights an important implication of the Leave No One Behind Agenda, it is not meant to pit communities against each other, but rather to remind us how much everyone has to gain by building and advocating for sustainable communities that serve us all.Explore ResultsExplore the data from the In the Red: the US failure to deliver on a promise of racial equality in our interactive dashboards.These maps display how US states are delivering sustainability across different racial and ethnic groups. As part of the Leave No One Behind Agenda, which maintains that those who have been least served by development progress must be those first addressed through the SDGs, progress toward the goals in each state is displayed based on the racial group with the least access to resources, programs, and services in that state. In other words, the “Overall scores’’ map shows the score for the racial group least served in each state. Click on a state to toggle through the state’s performance by different SDGs, and click on an indicator to view how a state performs on a given indicator. At the indicator level, horizontal bar charts show the racial disparity in the selected indicator and state, when data is available.AboutIn the Red: the US Failure to Deliver on a Promise of Racial EqualityIn the Red: the US Failure to Deliver on a Promise of Racial Equality project highlights measurable gaps in how states deliver sustainability to different racial groups. The full report can be read here. It extends an earlier report, Never More Urgent, looking at policies and practices that have led to the inequalities described in this project. It was prepared by a group of independent experts at SDSN and Howard University.UN Sustainable Development Solutions Network (SDSN)The UN Sustainable Development Solutions Network (SDSN) mobilizes scientific and technical expertise from academia, civil society, and the private sector to support practical problem solving for sustainable development at local, national, and global scales. The SDSN has been operating since 2012 under the auspices of the UN Secretary-General Antonio Guterres. The SDSN is building national and regional networks of knowledge institutions, solution-focused thematic networks, and the SDG Academy, an online university for sustainable development.SDSN USASDSN USA is a network of 150+ research institutions across the United States and unincorporated territories. The network builds pathways toward achievement of the UN Sustainable Development Goals (SDGs) in the United States by mobilizing research, outreach, collective action, and global cooperation. SDSN USA is one of more than 40 national and regional SDSN networks globally. It is hosted by the UN Sustainable Development Solutions Network (SDSN) in New York City, and is chaired by Professors Jeffrey Sachs (Columbia University), Helen Bond (Howard University), Dan Esty (Yale University), and Gordon McCord (UC San Diego).

BackgroundThe gap between the highest and lowest life expectancies for race-county combinations in the United States is over 35 y. We divided the race-county combinations of the US population into eight distinct groups, referred to as the “eight Americas,” to explore the causes of the disparities that can inform specific public health intervention policies and programs. Methods and FindingsThe eight Americas were defined based on race, location of the county of residence, population density, race-specific county-level per capita income, and cumulative homicide rate. Data sources for population and mortality figures were the Bureau of the Census and the National Center for Health Statistics. We estimated life expectancy, the risk of mortality from specific diseases, health insurance, and health-care utilization for the eight Americas. The life expectancy gap between the 3.4 million high-risk urban black males and the 5.6 million Asian females was 20.7 y in 2001. Within the sexes, the life expectancy gap between the best-off and the worst-off groups was 15.4 y for males (Asians versus high-risk urban blacks) and 12.8 y for females (Asians versus low-income southern rural blacks). Mortality disparities among the eight Americas were largest for young (15–44 y) and middle-aged (45–59 y) adults, especially for men. The disparities were caused primarily by a number of chronic diseases and injuries with well-established risk factors. Between 1982 and 2001, the ordering of life expectancy among the eight Americas and the absolute difference between the advantaged and disadvantaged groups remained largely unchanged. Self-reported health plan coverage was lowest for western Native Americans and low-income southern rural blacks. Crude self-reported health-care utilization, however, was slightly higher for the more disadvantaged populations. ConclusionsDisparities in mortality across the eight Americas, each consisting of millions or tens of millions of Americans, are enormous by all international standards. The observed disparities in life expectancy cannot be explained by race, income, or basic health-care access and utilization alone. Because policies aimed at reducing fundamental socioeconomic inequalities are currently practically absent in the US, health disparities will have to be at least partly addressed through public health strategies that reduce risk factors for chronic diseases and injuries.

This dataset represents ethnic group (19 tick-box level) by highest level qualification, for England and Wales combined. The data are also broken down by age and by sex.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021. This dataset shows population counts for usual residents aged 16+ Some people aged 16 years old will not have completed key stage 4 yet on census day, and so did not have the opportunity to record any qualifications on the census.

These estimates are not comparable to Department of Education figures on highest level of attainment because they include qualifications obtained outside England and Wales.

For quality information in general, please read more from here.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

No qualifications

No qualifications

Level 1

Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C , Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills

Level 2

5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma

Apprenticeship

Apprenticeship

Level 3

2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma

Level 4 +

Degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy)

Other

Vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown)