This layer shows population broken down by race and Hispanic origin and is symbolized to show the proportion of different race categories excluding non-Hispanic White. This is shown by 2020 census tract boundaries. This map is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are additional calculated attributes related to this topic, which can be mapped or used within analysis. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. This map uses services from these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. For more information regarding the ACS vintage, table sources and data processing notes, please see the item page for the source map service.

Incompatible reaction, indicating the race does not cause disease to the differential.*Compatible reaction, indicating the race can cause disease to the differential.

In 2024, white Americans remained the largest racial group in the United States, numbering just over 254 million. Black Americans followed at nearly 47 million, with Asians totaling around 23 million. Hispanic residents, of any race, constituted the nation’s largest ethnic minority. Despite falling fertility, the U.S. population continues to edge upward and is expected to reach 342 million in 2025. International migrations driving population growth The United States’s population growth now hinges on immigration. Fertility rates have long been in decline, falling well below the replacement rate of 2.1. On the other hand, international migration stepped in to add some 2.8 million new arrivals to the national total that year. Changing demographics and migration patterns Looking ahead, the U.S. population is projected to grow increasingly diverse. By 2060, the Hispanic population is expected to grow to 27 percent of the total population. Likewise, African Americans will remain the largest racial minority at just under 15 percent.

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

In the fiscal year of 2019, 21.39 percent of active-duty enlisted women were of Hispanic origin. The total number of active duty military personnel in 2019 amounted to 1.3 million people.

Ethnicities in the United States The United States is known around the world for the diversity of its population. The Census recognizes six different racial and ethnic categories: White American, Native American and Alaska Native, Asian American, Black or African American, Native Hawaiian and Other Pacific Islander. People of Hispanic or Latino origin are classified as a racially diverse ethnicity.

The largest part of the population, about 61.3 percent, is composed of White Americans. The largest minority in the country are Hispanics with a share of 17.8 percent of the population, followed by Black or African Americans with 13.3 percent. Life in the U.S. and ethnicity However, life in the United States seems to be rather different depending on the race or ethnicity that you belong to. For instance: In 2019, native Hawaiians and other Pacific Islanders had the highest birth rate of 58 per 1,000 women, while the birth rae of white alone, non Hispanic women was 49 children per 1,000 women.

The Black population living in the United States has the highest poverty rate with of all Census races and ethnicities in the United States. About 19.5 percent of the Black population was living with an income lower than the 2020 poverty threshold. The Asian population has the smallest poverty rate in the United States, with about 8.1 percent living in poverty.

The median annual family income in the United States in 2020 earned by Black families was about 57,476 U.S. dollars, while the average family income earned by the Asian population was about 109,448 U.S. dollars. This is more than 25,000 U.S. dollars higher than the U.S. average family income, which was 84,008 U.S. dollars.

This dataset represents the popular last names in the United States for people of two or more races.

In 2021, the distribution by race and ethnicity reveals how diverse family caregivers are in the United States. That year, nearly ********** of family caregivers in the United States were white. However, with a ** percent share in 2021, the second-most common race and ethnicity of family caregivers was Hispanic, followed by Black/African American.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Tucson by race. It includes the distribution of the Non-Hispanic population of Tucson across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Tucson across relevant racial categories.

Key observations

Of the Non-Hispanic population in Tucson, the largest racial group is White alone with a population of 237,250 (76.21% of the total Non-Hispanic population).

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: This column displays the racial categories (for Non-Hispanic) for the Tucson
• Population: The population of the racial category (for Non-Hispanic) in the Tucson is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Tucson total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Tucson Population by Race & Ethnicity. You can refer the same here

See: https://oganm.github.io/dndstats/ Source: https://github.com/oganm/dnddata

About the data

Unique characters are acquired by grouping the characters that share the same name and class and picking the higher level version. This could have merged independent characters with tropey names like Grognak the Barbarian of Drizzt the Ranger but manual examination of the data showed no cases of characters who appear to be made by different people but still has the same name and class.

If a multi-classed character shares name with a single classed character, I assume they are duplicates if the single classed character is lower level and its class matches with one of the classes of the multi-classed character.

Any character above level 20 (there were 6) were removed.

9 Revised Rangers were merged back into the ranger class.

Most percentages are rounded to the nearest integer.

As all data, this data comes with caveats. It is a subset of all DnD players who are using a particular mobile application who also know about and use my applications and consented to let me to keep their character sheets. I don’t have reason to think that these would be enriching certain character building choices but it’s something to keep in mind.

In most parts of this document no information is provided about whether or not the differences are actually statistically significant. Sorry about that. Didn’t want to fill this place with too much math. For instance we can see that we have 24 battle masters vs 26 champions. This is not a statistically significant difference based on our sample size so we cannot state with high confidence that one is more popular than the other.

If you are interested in significance of any of these measures, you can take a peak at this article on Wikipedia where formulas needed are explained. For some of these at least you should be able to get the information you need from the article.

Data access

This dataset is present in 2 forms: in its entirety that includes duplicates of characters and filtered version that only includes unique characters.

Go here for the complete data and here for the filtered one. Click the raw button to get them in plain text. Both have the same columns as explained below. The code to generate these tables can be found here.

Below are the descriptions of the columns in the files. If you think something you’d be interested in is missing, you can let me know.

name: This column has hashes that represent character names. If the hashes are the same, that means the names are the same. Real names are removed to protect character anonymity. Yes D&D characters have rights.

race: This is the race field as it come out of the application. It is not really helpful as subrace and race information all mixed up together and unevenly available. It also includes some homebrew content. You probably want to use the processedRace column if you are interested in this.

background: Background as it comes out of the application.

date: Time & date of input. Dates before 2018-04-16 are unreliable as some has accidentally changed while moving files around.

class: Class and level. Different classes are separated by | when needed.

justClass: Class without level. Different classes are separated by | when needed.

subclass: Subclasses. Again, separated by | when needed.

level: Total character level.

feats: Feats chosen by character. Separated by | when needed.

HP: Character HP.

AC: Character AC.

Str, Dex, Con, Int, Wis, Cha: ability scores

alignment: Alignment free text field. It is a mess, don’t touch it. See processedAlignment,good and lawful instead.

skills: List of skills with proficiency. Separated by |.

weapons: List weapons. Separated by |. It is somewhat of a mess as it allows free text inputs. See processedWeapons.

spells: List of spells and their levels. Spells are separated by |s. Each spell has its level next to it separated by *s. This is a huge mess as its a free text field and some users included things like damage dice in them. See processedSpells.

day: A shortened version of date. Only includes day information.

processedAlignment: Processed version of the alignment column. Way people wrote up their alignments are manually sifted through and assigned to the matching aligmment. First character represents lawfulness (L, N, C), second one goodness (G,N,E). An empty string means alignment wasn’t written or unclear.

good, lawful: Isolated columns for goodness and lawfulness.

processedRace: I have gone through the way race column is filled by the app and asigned them to correct races. If empty, indiciates a homebrew race not natively supported by the app.

processedSpells: Formatting is same as the spells column but it is cleaned up. Using string similarity I tried to match the spells to the full list of spells avai...

There are large differences in the average earnings of people who choose different college majors. Could differences in major choice explain some of the income gap between blacks and Hispanics relative to whites and Asians?

American adults with low or middle income were more likely to use BNPL than adults with higher incomes. This is according to an annual household survey in the United States, that asked about how and why consumers would be using the alternative payment option. ***** percent of respondents who had an income of 100,000 U.S. dollars or more used buy now, pay later. This was noticeably different from all other income levels, where ** percent of respondents said they used BNPL. The source observed major difference between races, with Black and Hispanic users being significantly more common than White or Asian users.

This dataset tracks annual two or more races student percentage from 2013 to 2023 for Common Ground High School vs. Connecticut and Common Ground High School District

This data package consists of 26 datasets all containing statistical data relating to the population and particular groups within it belonging to different countries, mostly the United States.

In 2023, there were ***** incidents of race-based hate crimes in residences or homes - the most common location in that year. The second most common location, with ***** incidents, were highways, roads, alleys, streets, and sidewalks.

Introduction

As a part of the Google Data Analytics Professional Certificate Program, this case study serves as a data analytics adventure and a way to dive into something personal. While many face the difficulty of finding employment out of college, it became especially tedious to do so due to the COVID-19 pandemic. As such, this case study revolves around unemployment trends from 2021 using data sourced from the United States Bureau of Labor Statistics. I used datasets surrounding unemployment and employment trends in 2021 to answer the following:

Questions

1. What methods for job searching were the most prevalent across age ranges? Across gender/race/Hispanic-Latino ethnicity?
2. What trends exist between and within the most prevalent venues for job searching among the unemployed?
3. What job sector(s) does the majority of the population comprise? What trends exist within and between the most popular job sector and the least popular job sector? What relationship do these factors have with race/gender/Hispanic-Latino ethnicity?
4. How does information about prevalent job searching influence the job market and the applicants in the job search phase?

Insights (see the data section below for charts, graphs, and the .Rmd file I utilized)

• In 2021, the unemployed, with ages ranging from 16-65, preferred resumes and applications as their method for seeking out jobs. This method was especially prevalent in the age range 16-34, where, the highest bracket of job seekers were 24-35 years old. A close second was contacting an employer directly, primarily used by 45-64-year-olds. When considering gender/ethnicity/race, however, compared to their male counterparts, white women and women of color were the highest users of the resumes and applications method. However, white males and men of color were the highest users of the contacting employers directly method.
• Among the unemployed resumes were overall the most prevalent method of applying for jobs in 2021, where, people aged 16--34 and women regardless of ethnicity/race were the most likely to utilize this method to search for jobs.
• The majority of the population resides in the "Management, Professional, and related occupations" job sector, with the least popular form of occupations being in the "Farming, Fishing, and Forestry" sector. This sentiment can be found almost across all genders/races/ethnicities, though, some other job sectors like "Production, transportation, and material moving occupations" and "Natural resources, construction, and maintenance occupations" were more prevalent concerning the Black/African American men, Hispanic/Latino women, and Hispanic/Latino men respectively.
• This information is highly useful for job industries, specifically, those in the "Management, Professional, and related occupations" sector. With this, industries in this job sector can project what their incoming job applicant pool may look like and how to prepare for making the application process more accessible. This information can also serve to reinforce fairness and inclusivity in the job application process and in the work environment.

** Overall**

Using this information a company can project in 2022-2023 the majority of applicants will either apply to jobs using resumes/applications, the majority of these applicants may be 16-34 years old, and women regardless of ethnicity and race. They can also look out for applicants who are older, 45-64 years old, and applicants who are men regardless of ethnicity and race, being more likely to contact them as an employer directly. If an employer prefers to be directly contacted, they should make sure to consider the difficulties that people of different race/ethnic/and gender identities may have done so, and, either should either make the job positing more welcoming and inclusive to do so or, be sure to include a process of hiring via resumes/applications in order to better represent the unemployed population seeking jobs.

In 2014, 29% of Black women had experienced a common mental disorder in the week before being surveyed, a higher rate than for White women.

The graph illustrates the number of victims of race-based hate crimes in the United States in 2025. The x-axis lists various ethnic groups, while the y-axis represents the corresponding number of victims. The data reveals that Anti-Black hate crimes were the most prevalent, with 1,743 victims, followed by Anti-Hispanic and Anti-Asian crimes with 629 and 201 victims respectively. Other categories include Anti-Other Race (308), Anti-American Indian (74), Anti-Arab (73), and Anti-Native Pacific (25). The data indicates a significant disparity in the number of victims across different ethnic groups, with Anti-Black hate crimes being the most prominent.

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Region-HCEZ-/5sc6-ey97. COVID-19 vaccinations administered to Chicago residents by Healthy Chicago Equity Zones (HCEZ) based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f Vaccination Status Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" so care should be taken when summing rows. Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated. Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-year estimates. Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who have each vaccination status as of the date, divided by the estimated number of people in that subgroup. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact its estimates. Data reported in I-CARE only includes doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in another state, the number of people with a completed series or a booster dose is underesti