Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

United States Unemployment Rate: NH: Black or African American data was reported at 5.800 % in Apr 2025. This records a decrease from the previous number of 6.000 % for Mar 2025. United States Unemployment Rate: NH: Black or African American data is updated monthly, averaging 6.600 % from Jan 2016 (Median) to Apr 2025, with 112 observations. The data reached an all-time high of 16.600 % in May 2020 and a record low of 4.400 % in Apr 2023. United States Unemployment Rate: NH: Black or African American data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Population Survey: Unemployment Rate.

United States Employment: Black or African American data was reported at 20,886.000 Person th in Apr 2025. This records an increase from the previous number of 20,787.000 Person th for Mar 2025. United States Employment: Black or African American data is updated monthly, averaging 14,555.000 Person th from Jan 1972 (Median) to Apr 2025, with 640 observations. The data reached an all-time high of 20,938.000 Person th in Mar 2023 and a record low of 7,367.000 Person th in Jan 1972. United States Employment: Black or African American data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Population Survey: Employment.

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

This dataset includes data on 25 transitions of a matrix demographic model of the invasive species Vincetoxicum nigrum (L.) Moench (black swallow-wort or black dog-strangling vine) and Vincetoxicum rossicum (Kleopow) Barb. (pale swallow-wort or dog-strangling vine) (Apocynaceae, subfamily Asclepiadoideae), two invasive perennial vines in the northeastern U.S.A. and southeastern Canada. The matrix model was developed for projecting population growth rates as a result of changes to lower-level vital rates from biological control although the model is generalizable to any control tactic. Transitions occurred among the five life stages of seeds, seedlings, vegetative juveniles (defined as being in at least their second season of growth), small flowering plants (having 1–2 stems), and large flowering plants (having 3 or more stems). Transition values were calculated using deterministic equations and data from 20 lower-level vital rates collected from 2009-2012 from two open field and two forest understory populations of V. rossicum (43°51’N, 76°17’W; 42°48'N, 76°40'W) and two open field populations of V. nigrum (41°46’N, 73°44’W; 41°18’N, 73°58’W) in New York State. Sites varied in plant densities, soil depth, and light levels (forest populations). Detailed descriptions of vital rate data collection may be found in: Milbrath et al. 2017. Northeastern Naturalist 24(1):37-53. Five replicate sets of transition data obtained from five separate spatial regions of a particular infestation were produced for each of the six populations. Note: Added new excel file of vital rate data on 12/7/2018. Resources in this dataset:Resource Title: Matrix model transition data for Vincetoxicum species. File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

United States Unemployment Rate: Black or African American: Female data was reported at 5.500 % in Apr 2025. This records an increase from the previous number of 5.400 % for Mar 2025. United States Unemployment Rate: Black or African American: Female data is updated monthly, averaging 10.900 % from Jan 1972 (Median) to Apr 2025, with 640 observations. The data reached an all-time high of 21.100 % in Jun 1983 and a record low of 4.000 % in Apr 2023. United States Unemployment Rate: Black or African American: Female data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Population Survey: Unemployment Rate.

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dictionary describing what each numeric digit means within each classification. The “Category” column uses numeric digits (2-6, depending on the factor) defined in the “Classification” column.

Metro vs. Non-Metro – “Metro_Rural” Metro vs. Non-Metro classification type is an aggregation of the 6 National Center for Health Statistics (NCHS) Urban-Rural classifications, where “Metro” counties include Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro areas and “Non-Metro” counties include Micropolitan and Non-Core (Rural) areas. 1 – Metro, including “Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro” areas 2 – Non-Metro, including “Micropolitan, and Non-Core” areas

Urban/rural - “NCHS_Class” Urban/rural classification type is based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. Levels consist of:

1 Large Central Metro
2 Large Fringe Metro 3 Medium Metro 4 Small Metro 5 Micropolitan 6 Non-Core (Rural)

American Community Survey (ACS) data were used to classify counties based on their age, race/ethnicity, household size, poverty level, and health insurance status distributions. Cut points were generated by using tertiles and categorized as High, Moderate, and Low percentages. The classification “Percent non-Hispanic, Native Hawaiian/Pacific Islander” is only available for “Hawaii” due to low numbers in this category for other available locations. This limitation also applies to other race/ethnicity categories within certain jurisdictions, where 0 counties fall into the certain category. The cut points for each ACS category are further detailed below:

Age 65 - “Age65”

1 Low (0-24.4%) 2 Moderate (>24.4%-28.6%) 3 High (>28.6%)

Non-Hispanic, Asian - “NHAA”

1 Low (<=5.7%) 2 Moderate (>5.7%-17.4%) 3 High (>17.4%)

Non-Hispanic, American Indian/Alaskan Native - “NHIA”

1 Low (<=0.7%) 2 Moderate (>0.7%-30.1%) 3 High (>30.1%)

Non-Hispanic, Black - “NHBA”

1 Low (<=2.5%) 2 Moderate (>2.5%-37%) 3 High (>37%)

Hispanic - “HISP”

1 Low (<=18.3%) 2 Moderate (>18.3%-45.5%) 3 High (>45.5%)

Population in Poverty - “Pov”

1 Low (0-12.3%) 2 Moderate (>12.3%-17.3%) 3 High (>17.3%)

Population Uninsured- “Unins”

1 Low (0-7.1%) 2 Moderate (>7.1%-11.4%) 3 High (>11.4%)

Average Household Size - “HH”

1 Low (1-2.4) 2 Moderate (>2.4-2.6) 3 High (>2.6)

Community Vulnerability Index Value - “CCVI” COVID-19 Community Vulnerability Index (CCVI) scores are from Surgo Ventures, which range from 0 to 1, were generated based on tertiles and categorized as:

1 Low Vulnerability (0.0-0.4) 2 Moderate Vulnerability (0.4-0.6) 3 High Vulnerability (0.6-1.0)

Social Vulnerability Index Value – “SVI" Social Vulnerability Index (SVI) scores (vintage 2020), which also range from 0 to 1, are from CDC/ASTDR’s Geospatial Research, Analysis & Service Program. Cut points for CCVI and SVI scores were generated based on tertiles and categorized as:

1 Low Vulnerability (0-0.333) 2 Moderate Vulnerability (0.334-0.666) 3 High Vulnerability (0.667-1)

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. This translates into the following equation: Where i represents census tracts, () is the metropolitan/micropolitan (CBSA) mean tract poverty rate, is the ith tract poverty rate, () is the non-Hispanic white population in tract i, and Pop is the population in tract i.While this definition of R/ECAP works well for tracts in CBSAs, place outside of these geographies are unlikely to have racial or ethnic concentrations as high as 50 percent. In these areas, the racial/ethnic concentration threshold is set at 20 percent. Data Source: Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17.Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.References:Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.To learn more about R/ECAPs visit:https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2017 - 2021 ACSDate Updated: 10/2023

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

United States Unemployment Rate: Black or African American: Male data was reported at 6.100 % in Apr 2025. This records a decrease from the previous number of 7.100 % for Mar 2025. United States Unemployment Rate: Black or African American: Male data is updated monthly, averaging 11.400 % from Jan 1972 (Median) to Apr 2025, with 640 observations. The data reached an all-time high of 23.300 % in Jan 1983 and a record low of 4.700 % in Apr 2023. United States Unemployment Rate: Black or African American: Male data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Population Survey: Unemployment Rate.

Unemployment Rate in the United States remained unchanged at 4.20 percent in May. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

NOTE: As of 2/16/2023 this table is no longer being updated. For information on COVID-19 Updated (Bivalent) Booster Coverage, go to https://data.ct.gov/Health-and-Human-Services/COVID-19-Updated-Bivalent-Booster-Coverage-By-Race/8267-bg4w.

Important change as of June 1, 2022

As of June 1, 2022, we will be using 2020 DPH provisional census estimates* to calculate vaccine coverage percentages by age at the state level. 2020 estimates will replace the 2019 estimates that have been used. Caution should be taken when making comparisons of percentages calculated using the 2019 and 2020 census estimates since observed difference may result from the shift in the denominator. The age groups in the state-level data tables will also be changing as a result of the switch to the new denominator.

• DPH Provisional State and County Characteristics Estimates April 1, 2020. Hayes L, Abdellatif E, Jiang Y, Backus K (2022) Connecticut DPH Provisional April 1, 2020 State Population Estimates by 18 age groups, sex, and 6 combined race and ethnicity groups. Connecticut Department of Public Health, Health Statistics & Surveillance, SAR, Hartford, CT.

This table shows the number and percent of people that have initiated COVID-19 vaccination, are fully vaccinated and had additional dose 1 by race / ethnicity and age group.

All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. The age groups in the state-level data tables will also be changing as a result of the switch to the new denominator.

Population size estimates are based on 2019 DPH census estimates until 5/26/2022. From 6/1/2022, 2020 DPH provisional census estimates are used.

In the data shown here, a person who has received at least one dose of COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if he/she has completed a primary vaccination series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the people who have received at least one dose.

A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional monovalent dose of COVID-19 vaccine is considered to have had additional dose 1. The additional dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations.

The percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported.

Race and ethnicity data may be self-reported or taken from an existing electronic health care record. Reported race and ethnicity information is used to create a single race/ethnicity variable. People with Hispanic ethnicity are classified as Hispanic regardless of reported race. People with a missing ethnicity are classified as non-Hispanic. People with more than one race are classified as multiple races.

A vaccine coverage percentage cannot be calculated for people classified as NH Other race or NH Unknown race since there are not population size estimates for these groups. Data quality assurance activities suggest that in at least some cases NH Other may represent a missing value. Vaccine coverage estimates in specific race/ethnicity groups may be underestimated as result of the classification of records as NH Unknown Race or NH Other Race.

Connecticut COVID-19 Vaccine Program providers are required to report information on all COVID-19 vaccine doses administered to CT WiZ, the Connecticut Immunization Information System. This includes doses given to residents of CT and to residents of other states vaccinated in CT. Data on doses administered to CT residents out-of-state are being added to CT WiZ jurisdiction-by-jurisdiction. Doses administered by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) are not yet reported to CT WiZ. Data reported here reflect the vaccination records reported to CT WiZ. However, once CT residents who have received doses in each jurisdiction are added to CT WiZ, the records for residents of that jurisdiction vaccinated in CT are removed. For example, when CT residents vaccinated in NYC were added, NYC residents vaccinated in CT were removed.

Note: This dataset takes the place of the original "COVID-19 Vaccinations by Race/Ethnicity" dataset (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Race-Ethnicity/xkga-ifz3 ), which will not be updated after 5/20/2021 and “COVID-19 Vaccinations by Race / Ethnicity” dataset (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Race-Ethnicity/ybkg-w5x2), which will not be updated after 10/20/2021.

United States Unemployment Rate: sa: NH: Black or African American (BA) data was reported at 6.300 % in Apr 2025. This records an increase from the previous number of 6.200 % for Mar 2025. United States Unemployment Rate: sa: NH: Black or African American (BA) data is updated monthly, averaging 11.000 % from Jan 1972 (Median) to Apr 2025, with 640 observations. The data reached an all-time high of 21.200 % in Jan 1983 and a record low of 4.800 % in Apr 2023. United States Unemployment Rate: sa: NH: Black or African American (BA) data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Population Survey: Unemployment Rate: Seasonally Adjusted.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. This translates into the following equation: Where i represents census tracts, () is the metropolitan/micropolitan (CBSA) mean tract poverty rate, is the ith tract poverty rate, () is the non-Hispanic white population in tract i, and Pop is the population in tract i.While this definition of R/ECAP works well for tracts in CBSAs, place outside of these geographies are unlikely to have racial or ethnic concentrations as high as 50 percent. In these areas, the racial/ethnic concentration threshold is set at 20 percent.

Data Source: American Community Survey (ACS), 2009-2013; Decennial Census (2010); Brown Longitudinal Tract Database (LTDB) based on decennial census data, 1990, 2000 & 2010.

Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17. Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.

References:Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

To learn more about R/ECAPs visit:https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 11/2017

California Commuting Mode Choice

Regional Disparities in Risk of Injury and Death

By Health [source]

About this dataset

This dataset contains data on the modes of transportation used by California residents aged 16 and older to commute to work. It includes data from the U.S. Census Bureau, Decennial Census and American Community Survey, covering all regions, counties, cities/towns, and census tracts in California. With each region showing detailed information regarding how its population travels to work (modes of transportation used), this dataset provides vital insight into the development of transport infrastructure in California over the past decade.

Unlike other states where private cars constitute an overwhelming majority of daily commuters (over 79% nationwide according to a 2015 survey), Californians have built up varied commuting habits – bicycles are commonly reported 5%, public transit stands at 15%, walking alone 4%, and carpooling is at 11%. Commuting plays a significant role on overall health—active modes such as biking or walking lead to healthier lifestyles that lower heart disease risks, obesity rates, diabetes prevalence; passengers on public transport also have a lower chance of injury in collisions compared with pedestrians or cyclists.

The consequences of inadequate planning for human mobility extend beyond physical health – it can also cause huge disparities between different racial groups such as Native Americans who experience four times higher death rate from pedestrian-car collisions than Whites or Asians; African-Americans and Latinos suffer twice as much as White people do when driving privately in their own cars due to air pollution hazards or lack thereof access to reliable public transportation system that could provide them with healthier alternatives. It is our hope that policymakers will use this dataset prominently stated by the Healthy Communities Data & Indicators Project - part of the Office Of Health Equity - while ensuring every resident’s right for safe mobility no matter their background!

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How to use the dataset

This dataset contains information on the percent of Californians aged 16 and older who use different modes of transportation to get to work. The data is collected from the U.S. Census Bureau and American Community Survey, and covers all counties, cities/towns and census tracts in California.

In this dataset, there are several columns of data such as mode (mode of transport), race_eth_name (name of the race/ethnicity), region_code (code for the region) and pop_total (total population). This makes it possible to look at relations between transportation choice and demographic factors like gender or ethnicity, or comparison between regions within California regarding commuting habits.

The purpose of this dataset is to provide information on how Californians travel to their jobs with respect to both geographical area as well as demographic characteristics. It allows studies into why certain areas might have higher usage rates for specific types of transport compared with others, how gender affects travel decisions, or which regions have access issues with public transit compared with driving for example.

To use this dataset you should start by familiarizing yourself with descriptive statistics such as percentages, hazard ratios etc., in order to understand each variable's contribution towards commuting trends more effectively. It might also help if you filter data by geographic area or personal characteristics first before performing more detailed analysis for more insightful results that can be used in policy-making when planning effective infrastructure investments related to transportation options over time or among differing populations within California state population levels noted here year-by-year across a decade period provided here

Research Ideas

• Creating interactive maps to visualize and compare the transportation methods of different race/ethnicities in California.
• Analyzing the transportation trends across regions, counties, cities/towns, and census tracts to forecast and plan for infrastructure investments.
• Comparing the risk ratio of pedestrian-car fatalities across different ethnic groups in order to address safety issues within underserved populations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [Open Database License (ODbL) v1.0](https://opendatacommons.org/lice...

United States Unemployment Rate: Male: NH: Black or African American data was reported at 6.200 % in Apr 2025. This records a decrease from the previous number of 7.100 % for Mar 2025. United States Unemployment Rate: Male: NH: Black or African American data is updated monthly, averaging 7.300 % from Jan 2016 (Median) to Apr 2025, with 112 observations. The data reached an all-time high of 16.100 % in Jun 2020 and a record low of 4.700 % in Apr 2023. United States Unemployment Rate: Male: NH: Black or African American data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G037: Current Population Survey: Unemployment Rate.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black). Later vintages of this layer have a different age group for children that includes age 18. This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Unemployment Rate in South Africa increased to 32.90 percent in the first quarter of 2025 from 31.90 percent in the fourth quarter of 2024. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Demographic characteristics of the total and stroke population in the CDM dataset 2017–2020.

In 2023, the around 11.1 percent of the population was living below the national poverty line in the United States. Poverty in the United StatesAs shown in the statistic above, the poverty rate among all people living in the United States has shifted within the last 15 years. The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines poverty as follows: “Absolute poverty measures poverty in relation to the amount of money necessary to meet basic needs such as food, clothing, and shelter. The concept of absolute poverty is not concerned with broader quality of life issues or with the overall level of inequality in society.” The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the most people living in poverty in 2022, with about 25 percent of the population earning an income below the poverty line. In comparison to that, only 8.6 percent of the White (non-Hispanic) population and the Asian population were living below the poverty line in 2022. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2022. Child poverty peaked in 1993 with 22.7 percent of children living in poverty in that year in the United States. Between 2000 and 2010, the child poverty rate in the United States was increasing every year; however,this rate was down to 15 percent in 2022. The number of people living in poverty in the U.S. varies from state to state. Compared to California, where about 4.44 million people were living in poverty in 2022, the state of Minnesota had about 429,000 people living in poverty.