Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description_LE_ Sex_Race_Ethnicity.pdf

About this dataset

Context

The dataset presents the median household income across different racial categories in Little Canada. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Little Canada population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 62.74% of the total residents in Little Canada. Notably, the median household income for White households is $77,233. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $77,233.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Little Canada.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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/.

Recommended for further research

This dataset is a part of the main dataset for Little Canada median household income by race. You can refer the same here

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin; for the United States, States, Counties; and for Puerto Rico and its Municipios: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2024.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

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).

Labour force characteristic estimates by age group, gender, and visible minority group.

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco po

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

Introduction: Racial minority groups have been disproportionately affected by the 2019 novel coronavirus disease (COVID-19). Vaccine hesitancy may be a major barrier to achieving equitable herd immunity and must be addressed to reduce the excess morbidity and mortality of COVID-19 in disproportionately affected communities. This study aimed to determine if COVID-19 vaccine hesitancy, and its factors vaccine complacency and confidence, are more prominent among disproportionately affected racial minority groups.Methods:We collected data from participants aged 18 years or older from the four most populous U.S. states, including New York, California, Florida, and Texas, and Canada. Data were collected using a web-based survey platform. Data are available at http://www.covid19-database.com.Results:Data from 4,434 participants were included [mean (SD) age = 48.7 (17.2) and 50.4% women]. Vaccine hesitancy was higher in Black, Indigenous (Native American and Indigenous People of Canada, including First Nations, Inuit and Métis), and Latinx compared to White participants, while no difference was found between East Asian and White participants. The group differences in vaccine hesitancy for Indigenous and Black compared to White participants remained after controlling for sociodemographic factors. Determinants of vaccine complacency were equivalent between disproportionately affected racial groups and white participants. Vaccine confidence (i.e., trust in vaccine benefit) was generally lower in all racial groups compared to White participants. Differences in vaccine mistrust comparing Black and East Asian to White participants remained after controlling for sociodemographic factors.Discussion:Disproportionately affected racial minorities may have higher vaccine hesitancy and lower confidence in COVID-19 vaccines. Public health and other relevant government services should address vaccine hesitancy among racial minorities using a culturally sensitive, community-centered approach to attain equitable herd immunity.

This layer contains block level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables. To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual blocks will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. Citation: U.S. Census, 2020Census tables: P1, P2, P3, P4, H1, P5, HeaderData Source: https://hub.arcgis.com/datasets/d1105f1e65a743cc84fc12c034625fc7/exploreData downloaded from source: 11/28/2022

About this dataset

Context

The dataset presents the median household income across different racial categories in West Palm Beach. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of West Palm Beach population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 52.28% of the total residents in West Palm Beach. Notably, the median household income for White households is $81,878. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $83,333. This reveals that, while Whites may be the most numerous in West Palm Beach, Asian households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/west-palm-beach-fl-median-household-income-by-race.jpeg" alt="West Palm Beach median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in West Palm Beach.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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/.

Recommended for further research

This dataset is a part of the main dataset for West Palm Beach median household income by race. You can refer the same here

Note: These datasets are part of our manuscript, which might be submitted to a double-anonymous review journal. We have to delete the information of all the authors at this point because reviews might check these datasets. We will add the information and affiliation of all authors once the manuscript is accepted. This study aims to provide insight into the US Southwest social and environmental inequality problems by combining high-resolution ECOSTRESS data, including Land Surface Temperature (LST), Evaporative Stress Index (ESI), and actual Evapotranspiration (ETa) with sociodemographic data at the block group level acquired from US Census. ESI and ETa represent drought and consumptive water use, respectively. Further, disparities of environmental changes over the past two decades in connection with races/ethnicities are explored using Landsat-based LST and ET from 2000 to 2020 across major US Southwest cities in light of global climate changes. We narrow our investigations to the summer months, including June, July, and August, when environmental issues are more pronounced during the day, such as heat-related mortality and morbidity and higher water consumption. This dataset covers major US Southwest cities. The dataset includes social-environmental data at the block level and remotely sensed environmental data. The description for each individual dataset is listed as follows, T01(T01_dataset_meta_race_ethnicity_economy_env_southwest.csv): This dataset provides statistics on race/ethnicity, social economy, and arithmetic mean of environmental variables at the block group level. T02: These datasets provide processed environmental data, including Daytime Land Surface Temperature (LST), Actual Evapotranspiration (ETa), and Evaporative Stress Index (ESI) images at 70m resolution. T03: These datasets include annual Landsat-based summer LST, and ETa means at 30m resolution from 2000 to 2020. The images of LST and ETa in 2012 are missing because Landsat datasets in 2012 are unavailable. We collected all available Landsat images with cloud cover lower than 75% in the summer from 2000 to 2020 to produce the dataset. The high-resolution (30m) LST and ETa are processed on Google Earth Engine (GEE). There are a total of 8860 scenes (including Landsat 4, 5, 7, and 8) from 2000 to 2020 to cover all major US Southwest urban areas. We also include two sample images of 2020 LST and ETa for demonstration. T04: These datasets contain LST and ETa Sen's slope results, which represent the median rate of change of LST and ETa from 2000 to 2020 on an annual basis. T05: The boundary of the major US Southwest urban areas

Note: These datasets are part of our research, which was published in Environmental Science & Technology.

Please cite this paper when you use these datasets:

Yuanhui Zhu, Soe W. Myint, Danica Schaffer-Smith, Daoqin Tong, Yuyu Zhou, Yubin Li, and Rebecca L. Muenich. Rising Environmental Inequalities and Their Relationship to Racial and Socioeconomic Disparities in the US Southwest, 2025, Environmental Science & Technology. https://doi.org/10.1021/acs.est.4c14369">https://doi.org/10.1021/acs.est.4c14369

For any questions or concerns, contact Yuanhui Zhu at jta90@txstate.edu

This study aims to provide insight into the US Southwest social and environmental inequality problems by combining high-resolution ECOSTRESS data, including Land Surface Temperature (LST), Evaporative Stress Index (ESI), and actual Evapotranspiration (ETa) with sociodemographic data at the block group level acquired from US Census. ESI and ETa represent drought and consumptive water use, respectively. Further, disparities of environmental changes over the past two decades in connection with races/ethnicities are explored using Landsat-based LST and ET from 2000 to 2020 across major US Southwest cities in light of global climate changes. We narrow our investigations to the summer months, including June, July, and August, when environmental issues are more pronounced during the day, such as heat-related mortality and morbidity and higher water consumption.

This dataset covers major US Southwest cities. The dataset includes social-environmental data at the block level and remotely sensed environmental data. The description for each individual dataset is listed as follows,

T01(T01_dataset_meta_race_ethnicity_economy_env_southwest.csv): This dataset provides statistics on race/ethnicity, social economy, and arithmetic mean of environmental variables at the block group level.

T02: These datasets provide processed environmental data, including Daytime Land Surface Temperature (LST), Actual Evapotranspiration (ETa), and Evaporative Stress Index (ESI) images at 70m resolution.

T03: These datasets include annual Landsat-based summer LST, and ETa means at 30m resolution from 2000 to 2020. The images of LST and ETa in 2012 are missing because Landsat datasets in 2012 are unavailable. We collected all available Landsat images with cloud cover lower than 75% in the summer from 2000 to 2020 to produce the dataset. The high-resolution (30m) LST and ETa are processed on Google Earth Engine (GEE). There are a total of 8860 scenes (including Landsat 4, 5, 7, and 8) from 2000 to 2020 to cover all major US Southwest urban areas. We also include two sample images of 2020 LST and ETa for demonstration.

T04: These datasets contain LST and ETa Sen's slope results, which represent the median rate of change of LST and ETa from 2000 to 2020 on an annual basis.

T05: The boundary of the major US Southwest urban areas

description: This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P: EPI OEPHI MATERIALS REFERENCES Life Expectancy Dataset description_LE_ Sex_Race_Ethnicity.pdf; abstract: This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P: EPI OEPHI MATERIALS REFERENCES Life Expectancy Dataset description_LE_ Sex_Race_Ethnicity.pdf

This data set is no longer being updated and is historical, last update 10/10/2022.Percentage of first vaccination dose given to Jefferson County residents by age group and race. Field Name Field Definition Group_type A label used to identify the lowest limit of the age group Race self identified race of vaccinated recipient Total_Vaccinated Number of vaccinated Jefferson County residents by Group_type Total_Race_Vaccinated Number of vaccinated Jefferson County residents by Group_type and race Race_Vaccinated_Pct Percentage of Number of vaccinated Jefferson County residents by Group_type and race/Number of vaccinated Jefferson County residents by Group_type census_pop_count Total number of Jefferson County residents by Group_type and race census_pop_byGroup Total number of Jefferson County residents by Group_type census_pop_PCT Percentage of total number of residents by group type and race / Total number of residents by Group_type PCT_group_vaccinated Total_Race_Vaccinated/census_pop_count age_group the lowest limit of the age group (ex. 35 represent 35 up to 45 and 45 represents 45 up to 55) LOADED Date the data was loaded into the system Note: This data is preliminary, routinely updated, and is subject to changeFor questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department o

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

About this dataset

Context

The dataset presents the median household income across different racial categories in Au Sable charter township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Au Sable charter township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 94.09% of the total residents in Au Sable charter township. Notably, the median household income for White households is $46,614. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $46,614.

https://i.neilsberg.com/ch/au-sable-charter-township-mi-median-household-income-by-race.jpeg" alt="Au Sable charter township median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Au Sable charter township.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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/.

Recommended for further research

This dataset is a part of the main dataset for Au Sable charter township median household income by race. You can refer the same here