The new COVID-19 Health, Racial, and Economic Equity data gallery helps you start understanding and addressing inequity in your community. The data is now available on the Esri COVID-19 GIS Hub. This gallery contains a collection of Esri maps, data, and apps that can help guide decisions around health, racial, and economic equity during COVID-19 and beyond. _Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

Poverty threatens the overall well-being of individuals and families, limiting access to stable housing, healthy foods, health care, and educational and employment opportunities, among other basic needs.Poverty is associated with a higher risk of adverse health outcomes, including chronic physical and mental illness, lower life expectancy, developmental delays, and others.

Racist policies and practices have contributed to racial economic inequities. Nationally, Black, Indigenous, and people of color experience poverty at higher rates than white Americans, on average.

The District's Response

Boosting assistance programs that provide temporary cash and health benefits to help low-income residents meet their basic needs, including Medicaid, TANF For District Families, SNAP, etc.

Housing assistance and employment and career training programs to support resident’s housing and employment security. These include the Emergency Rental Assistance Program, Permanent Supportive Housing vouchers, Career MAP, the DC Infrastructure Academy, among other programs and services.

Creation of the DC Commission on Poverty to study poverty issues, evaluate poverty reduction initiatives, and make recommendations to the Mayor and the Council.

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

According to the U.S. Bureau of Labor Statistics, the labor force participation rate is an important measure of the health of the labor market, which represents the relative amount of labor resources available for the production of goods and services.

Changes in overall labor force participation reflect demographic, policy, and employer changes, whereas gaps in labor force participation between different segments of the working-age population reveal barriers to participation.

Black, Indigenous, and people of color participate in the labor market at lower rates than white people. These inequities reflect policies and practices, such as employment discrimination, racial segregation, and mass incarceration, among other factors.

The District's Response

Investing in targeted programs that provide pathways to higher wages and jobs, such as the Advanced Technical Centers (ATC), the DC Infrastructure Academy, and Career MAP, which aim to tackle the systemic barriers that keep people out of the labor force.

Administering federal and local safety net programs such as TANF For District Families, SNAP, unemployment insurance, and Medicaid that provide temporary cash and health benefits to address economic hardship.

Partners with the Department of Employment Services in building youth from the ground up through its various programs and services, including mentorship, counseling justice system services, job training development, and employment.

Note: This dataset is on hiatus.

CDPH strives to respond equitably to the COVID-19 pandemic and is therefore interested in how different communities are impacted. Collecting and reporting health equity data helps to identify health disparities and improve the state’s response. To that end, CDPH tracks cases, deaths, and testing by race and ethnicity as well as other social determinants of health, such as income, crowded housing, and access to health insurance.

During the response, CDPH used a health equity metric, defined as the positivity rate in the most disproportionately-impacted communities according to the Healthy Places Index. The purpose of this metric was to ensure California reopened its economy safely by reducing disease transmission in all communities. This metric is tracked and reported in comparison to statewide positivity rate. More information is available at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/CaliforniaHealthEquityMetric.aspx.

Data completeness is also critical to addressing inequities. CDPH reports data completeness by race and ethnicity, sexual orientation, and gender identity to better understand missingness in the data.

Health equity data is updated weekly. Data may be suppressed based on county population or total counts.

For more information on California’s commitment to health equity, please see https://covid19.ca.gov/equity/

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined differences in youth's mental health and substance abuse needs in seven different racial/ethnic groups of justice-involved youth. Using de-identified data from the Survey of Youth in Residential Placement (SYRP), it was assessed whether differences in mental health and substance abuse needs and services existed in a racially/ethnically diverse sample of youth in custody. Data came from a nationally representative sample of 7,073 youth in residential placements across 36 states, representing five program types. An examination of the extent to which there were racial/ethnic disparities in the delivery of services in relation to need was also conducted. This examination included assessing the differences in substance-related problems, availability of substance services, and receipt of substance-specific counseling. One SAS data file (syrp2017.sas7bdat) is included as part of this collection and has 138 variables for 7073 cases, with demographic variables on youth age, sex, race and ethnicity. Also included as part of the data collection are two SAS Program (syntax) files for use in secondary analysis of youth mental health and substance use.

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

Homeownership has historically been an important source of intergenerational wealth. For many, homeownership can provide financial and housing security.Rising home prices over the past two decades have outpaced wage growth, perpetuating significant racial disparities in homeownership rates and contributing to the displacement of Black residents and other people of color from the District.

A history of redlining and racist real estate practices, like racial covenants, barred Black and other people of color from homeownership.

The District's Response

Convening of the Black Homeownership Strikeforce to address past harms and increase equitable homeownership rates through targeted, evidence-based recommendations, and setting the goal of creating 20,000 new Black homeowners by 2030.

Programs to enable homeowning families and individuals to remain in their homes, including the Homestead Deduction and Senior Citizen or Disabled Property Owner Tax Relief and the Heir Property Assistance Program.

Inclusionary Zoning (IZ) Affordable Housing Program and financial assistance programs like the Home Purchase Assistance Program (HPAP), Employer Assisted Housing Program (EAHP), and Negotiated Employee Assistance Home Purchase Program (NEAHP) to support homeownership among District residents.

Abstract copyright UK Data Service and data collection copyright owner. The Evidence for Equality National Survey (EVENS) is a national survey that documents the experiences and attitudes of ethnic and religious minorities in Britain. EVENS was developed by the Centre on the Dynamics of Ethnicity (CoDE) in response to the disproportionate impacts of COVID-19 and is the largest and most comprehensive survey of the lives of ethnic and religious minorities in Britain for more than 25 years. EVENS used pioneering, robust survey methods to collect data in 2021 from 14,200 participants of whom 9,700 identify as from an ethnic or religious minority. The EVENS main dataset, which is available from the UK Data Service under SN 9116, covers a large number of topics including racism and discrimination, education, employment, housing and community, health, ethnic and religious identity, and social and political participation.The EVENS Teaching Dataset provides a selection of variables in an accessible form to support the use of EVENS in teaching across a range of subjects and levels of study. The dataset includes demographic data and variables to support the analysis of: racism and belonging health and well-being during COVID-19 political attitudes and trust. Main Topics: Racism, belonging, impact of COVID-19, health, well-being, financial position, political attitudes and trust.

Meeting notes from Interagency Data Team meetings. These are best attempt to capture notable comments and questions from attendees. Notes are paraphrased. Please reference presentation or contact open.data@dc.gov with questions. The Interagency Data Team is a community of data analysts, or agency liaisons, who convene regularly with representation from DC agencies of all persuasions. Participants engage in discussions regarding the team’s core mission and priorities for a better kind of data culture – collection, application, sharing, classification and governance to name a few. The team is coordinated by the Office of the Chief Technology Officer (OCTO), lead by the Chief Data Officer (CDO), and directly supports the District of Columbia's Data Policy.Choice, Context and Collaboration: Piloting Racial Equity Data Standards in District GovernmentRedistrict: Data and Web ServicesLegacy MAR and MAR 2 APIData Report

This collection contains survey data collected at the end of October 2004 from the 49 state law enforcement agencies in the United States that had traffic patrol responsibility. Information was gathered about their policies for recording race and ethnicity data for persons in traffic stops, including the circumstances under which demographic data should be collected for traffic-related stops and whether such information should be stored in an electronically accessible format. The survey was not designed to obtain available agency databases containing traffic stop records.

Presentation slides from the Interagency Data Team meeting. The Interagency Data Team is a community of data analysts, or agency liaisons, who convene regularly with representation from DC agencies of all persuasions. Participants engage in discussions regarding the team’s core mission and priorities for a better kind of data culture – collection, application, sharing, classification and governance to name a few. The team is coordinated by the Office of the Chief Technology Officer (OCTO), lead by the Chief Data Officer (CDO), and directly supports the District of Columbia's Data Policy.Choice, Context and Collaboration: Piloting Racial Equity Data Standards in District GovernmentRedistrict: Data and Web ServicesLegacy MAR and MAR 2 APIData Report

Institutions of higher education (IHE) throughout the United States have a long history of acting out various levels of commitment to diversity advancement, equity, and inclusion (DEI). Despite decades of DEI “efforts,†the academy is fraught with legacies of racism that uphold white supremacy and prevent marginalized populations from full participation. Furthermore, politicians have not only weaponized education but passed legislation to actively ban DEI programs and censor general education curricula (https://tinyurl.com/antiDEI). Ironically, systems of oppression are particularly apparent in the fields of Ecology, Evolution, and Conservation Biology (EECB)–which recognize biological diversity as essential for ecological integrity and resilience. Yet, amongst EECB faculty, people who do not identify as cis-heterosexual, non-disabled, affluent white males are poorly represented. Furthermore, IHE lack metrics to quantify DEI as a priority. Here we show that only 30.3% of US-faculty posi..., Here we investigated the (lack of) process in faculty searches at IHE for evaluating candidates’ ability to advance DEI objectives. We quantified the prevalence of required diversity statements relative to research and/or teaching statements for all faculty positions posted to the Eco-Evo Jobs Board (http://ecoevojobs.net) from January 2019 - May 2020 as a proxy for institutional DEI prioritization (Supplement). We also mapped the job posts that required diversity statements geographically to gauge whether and where diversity is valued in higher education across the US. Data analysis We pulled all faculty jobs posted on Eco-Evo jobs board (http://ecoevojobs.net) from Jan 1, 2019, to May 31, 2020. For each position, we recorded the Location (i.e., state), Subject Area, Closing Date, Rank, whether or not the position is Tenure Track, and individual application materials (i.e., Research statement, Teaching statement, combined Teaching and Research statement, Diversity statement, Mentorship..., Google Sheets or Excel is required to open Lafferty et al. Data_File.xlsx Sankey Flow Show (THORTEC Software GmbH: www.sankeyflowshow.com) used to create the Sankey diagram Figure 2 produced in R

, Reference Information

Provenance for this README

• File name: README_Dataset-Academic-JEDI.txt
• Authors: Erin A. McKenney
• Other contributors: Diana J. R. Lafferty, Tru Hubbard, Sarah Trujillo, DeAnna Beasley
• Date created: 2023-06-08
• Date modified: 2023-10-18

Dataset Attribution and Usage

• Dataset Title: Data for the article “A path forward: creating an academic culture of justice, equity, diversity and inclusionâ€
• Persistent identifier: DOI:10.5061/dryad.cfxpnvxbb
• License: Use of these data is covered by the following license:
  • Title: CC0 1.0 Universal (CC0 1.0)
  • Specification: https://creativecommons.org/publicdomain/zero/1.0/; the authors respectfully request to be contacted by researchers interested in the re-use of these data so that the possibility of collaboration can be discussed.

Methodological Information

• All data were collected by the authors.
• Methods of data collection/generation: see manuscript and Supplemental Materials f...

Note: This dataset is on hiatus. CDPH strives to respond equitably to the COVID-19 pandemic and is therefore interested in how different communities are impacted. Collecting and reporting health equity data helps to identify health disparities and improve the state’s response. To that end, CDPH tracks cases, deaths, and testing by race and ethnicity as well as other social determinants of health, such as income, crowded housing, and access to health insurance. During the response, CDPH used a health equity metric, defined as the positivity rate in the most disproportionately-impacted communities according to the Healthy Places Index. The purpose of this metric was to ensure California reopened its economy safely by reducing disease transmission in all communities. This metric is tracked and reported in comparison to statewide positivity rate. More information is available at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/CaliforniaHealthEquityMetric.aspx. Data completeness is also critical to addressing inequities. CDPH reports data completeness by race and ethnicity, sexual orientation, and gender identity to better understand missingness in the data. Health equity data is updated weekly. Data may be suppressed based on county population or total counts. For more information on California’s commitment to health equity, please see https://covid19.ca.gov/equity/

These files comprise material collected through an 'Equity in Academia' workshop, convened by the Nuffield Department of Medicine at the University of Oxford in September 2019.The pdf file 'EIA supplementary data' contains qualitative information collected from meeting delegates, before, during and after the workshop.The jpeg files are visual representations of the day's events, summarising themes and discussions. Artwork produced by Alex Hughes at Drawnalism (https://drawnalism.com/), posted with permission.The xls file contains a list of the consortium authorship list.

IntroductionEnsuring high-quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieving the goal of inclusion of racial and ethnic minorities in scientific research and detecting disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network and assess impact of improved data completion on conclusions drawn from the registry.MethodsThis is a mixed-methods quality improvement study that consisted of five parts, as follows: (1) Identifying baseline missing race and ethnicity data, (2) Surveying current collection and entry, (3) Completing data through audit and feedback cycles, (4) Assessing the impact on outcome measures, and (5) Conducting participant interviews and thematic analysis.ResultsAcross six participating centers, 29% of the patients were missing data on race and 31% were missing data on ethnicity. Of patients missing data, most patients were missing both race and ethnicity. Rates of missingness varied by data entry method (electronic vs. manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared with patients with non-missing race and ethnicity data at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow-up compared with White patients. There was no significant change in odds ratio of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values, which may affect the ability to detect changes in odds ratio of cJADAS ≥5 after completion.ConclusionsAbout one-third of the patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data were not uniformly distributed compared with those with non-missing race and ethnicity data at baseline, suggesting that differences in outcomes after completing race and ethnicity data may be seen with larger sample sizes.

The overall goal of this research project was to examine the impact that risk assessment has on the sentencing of racial minority youth and youth from disadvantaged neighborhoods. One of the main goals of correctional risk assessment is to reduce disparate outcomes for certain groups of youth in the juvenile justice system (e.g. Black youth). In practice, risk assessment is used with this goal in mind. However, there is very little research which shows whether or not risk assessment actually has its intended effects on sentencing. Therefore, this study set out to examine whether or not risk assessment reduces the sentencing gap seen in most research for minority youth and youth from disadvantaged neighborhoods. In addition, several other important research topics were explored to understand the role that race and socioeconomic disadvantage play in the juvenile justice system. These research topics included: (1) variation in the predictive validity of risk assessment across race, (2) variation in the predictive validity of risk assessment across neighborhood disadvantage, and (3) the moderating effects of race/gender and court dispositions on the predictive validity of risk assessment. To achieve the research goals in this study, data was collected from a large juvenile court in a Midwestern County. Information from 4,383 youth that came into contact with the court between January 2010 and December 2016 were included in the study. Data was collected that related to youth demographics, neighborhood characteristics in which youth lived, risk assessments data measured by the Ohio Youth Assessment System (OYAS), treatment programming received, court dispositions/sentencing, and recidivism.

Abstract copyright UK Data Service and data collection copyright owner. Two surveys were conducted in post-apartheid South Africa to explore, among other factors, attitudes towards race-targeted policies, perceptions of racial justice and discrimination, and racial prejudice. The surveys also examined people's experiences of inter-racial contact in terms of both its frequency and its quality and were designed to explore the relationship between such contact and various kinds of political attitudes. Survey One sampled black, coloured, Indian and white South Africans. Survey Two sampled white and black South Africans. Both surveys employed a computer assisted, random digit dialling methodology to recruit respondents. Further information is available from the ESRC Award web page. Main Topics: Survey One focused on attitudes towards a variety of policies designed to achieve racial equality in post-apartheid South Africa. It also explored racial attitudes and other variables such as stratification beliefs. Survey Two focused on perceptions of racial discrimination and justice, and included measures of racial prejudice and racial contact. Standard measures: Racial stereotyping Perceptions of injustice measured using Cantrill Ladder technique (both on Survey Two) Sampling was a variation on simple random sampling. Data were gathered using a telephone survey method, employed a random digit dialling technique to select respondents. Telephone interview

Context

This dataset contains information about fatal shooting of civilians by police officers in the US since Jan 1st, 2015. The data about the shootings was collected by the Washington Post in their fatal police shootings dataset. The city locations were geocoded using OpenStreetMap Nominatim.

Content

fatal-police-shootings-data.csv contains information about each shooting. Each row is a shooting, and columns contain information about

• Name of the individual shot
• Date of the shooting
• Manner of death of the individual shot
• If and how the individual shot was armed
• Age of the individual shot
• Gender of the individual shot
• Race of the individual shot
• Whether the individual shot displayed signs of mental illness
• To what level the individual shot was attacking when shot
• If and how the individual shot was fleeing from police
• If an officer present for the shooting was wearing a body camera

CityLocations.csv contains the latitude and longitude for each city present in fatal-police-shootings-data.csv.

Acknowledgements and Licenses

The data in fatal-police-shootings-data.csv was collected by the Washington Post, and is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License.

The data in CityLocations.csv was geocoded using OpenStreetMap Nominatim, and is licensed under the Open Database License.

Cover image by Spenser.

In order to study the effects and outcomes of intergroup dialogue (IGD) on Colgate students, qualitative and quantitative methods were used to collect data. Interviews with 16 Colgate students (two were graduates who currently work at Colgate) were conducted along with data from a post-test survey that was administered to students enrolled in the two Intergroup Dialogue courses. Scales were created using questions and data from the post-test survey to analyze and examine the means of students' answers and the Cronbach Alpha scores. This project used a comparative study as well to examine how levels of exposure to intergroup dialogue pedagogy impacted and effected students. For the comparative study a sample of students was collected from 2 non-IGD diversity courses, 2 partial IGD courses and 2 full IGD courses. Five students were interviewed for the non-IGD courses as well as full IGD courses and six for the courses that used partial IGD. In regards to gender, 13 females were interviewed and 3 males. These methods were used to answer the following questions: 1) what effect does participation in intergroup dialogue have on students attending a liberal arts college? 2) What is the process through which exposure to intergroup dialogue pedagogy leads to these changes in student outcomes? This study solely collected data from Colgate University. This project covers Colgate’s racial climate, past literature, past applicable theories as well as the creation of a new theory, data and methods, quantitative and qualitative results as well a discussion and conclusion section.

Abstract

Insecurity is partly objective, partly subjective. To develop a strategy for reducing socio economic insecurity, it is necessary to consider both objective and subjective indicators of security. While it may be relatively easy to create objective indices of socio economic security, to create sound indicators that capture subjective elements of socio economic security, effort has to be made to find out what people in different parts of the world, from different social and demographic groups, perceive as the extent of their security or insecurity. It was with this purpose that IFPSES launched its PSS in mid 2000. The objective of the PSS is therefore to: - Provide an improved understanding of perceived needs of people for policies and institutional support to provide them with basic security. - Enable the creation of a Decent Work Index, which is crucial to ILO's work. - Provide important information that can be used to flag security concerns of workers in ILO - Reports and other documents.

Geographic coverage

The survey covered Cape Town, and Durban and Matatiele in Kwazulu-Natal.

Analysis unit

Units of analysis in the survey include households and individuals

Universe

The universe of the survey was household members aged 15-64

Kind of data

Sample survey data [ssd]

Sampling procedure

The pure random sample was drawn following the Census Data, adjusted according to Research Surveys’ statistics department. The unit of analysis is the household. A single respondent was selected per household. Although the first level of stratification was drawn on race and dwelling, within a predominant racial suburb, individual households that were selected might not be part of that race. If, however, a household was chosen, or a respondent within a household, that was not of the same predominant race of the strata, the respondent was not replaced. This would allow for the mixed suburb representation that is common in South Africa today.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PSS contain about 100 questions - some common to all respondents, and some to specific worker or social groups. A set of core questions are asked in all the PSS. In addition there are questions about specific issues that are important in the context of the country and/or the socio economic group that the PSS targets. The questionnaires are divided into sections by the seven types of securities that the IFP/SES monitors (except three Latin American surveys).

In addition, there are sections on: - Background characteristics - these refer to a respondent's age, marital status and household characteristics. - Basic security - Basic security is analysed in these surveys in terms of access to adequate food, health care, shelter and access to water. - Social justice - these set of questions explore people's sense of social justice since these perceptions are closely linked to the sense of anxiety and distributive justice that underlies insecurity.

Here are sections of the questionnare: Section A of the questionnaire is the household roster, providing data on household members Section B of the questionnaire collects data on basic security of households, including security related to food, clothing, health and the financial resources of the household. Section C of the questionnaire collects data on Labour market security Section D of the questionnaire collects data on Employment security Section E of the questionnaire collects data on Job security Section F of the questionnaire collects data on Skill security Section G of the questionnaire collects data on Work security Section H of the questionnaire collects data on Income security Section I of the questionnaire collects data on Representation security Section J of the questionnaire collects data on Social justice

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

Employment is the main source of income for most people. For many families and individuals, unemployment threatens access to basic needs, such as food, housing, transportation, health care, and education, among others.

Nationally, Black workers and workers of color, on average, experience persistently higher unemployment rates than white workers. Racist policies and practices, including segregation, employment discrimination, and inequities in the criminal justice system have undermined job security for workers of color.

The District's Response

Initiatives that support residents in career advancement and their efforts to secure sustainable employment through education and training support, such as Career MAP, Advanced Technical Centers (ATC), and the DC Infrastructure Academy, among other programs and services.

Administering federal and local safety net programs that provide temporary cash and health benefits to help residents experiencing unemployment and related economic hardship meet their basic needs, including unemployment insurance, Medicaid, TANF For District Families, SNAP, etc.

Programs to remove barriers employment for returning citizens, such as Pathways to Work and the Returning Citizens Access to Jobs Grant.