Knowing the racial and ethnic composition of a community is often one of the first steps in understanding, serving, and advocating for various groups. This information can help enforce laws, policies, and regulations against discrimination based on race and ethnicity. These statistics can also help tailor services to accommodate cultural differences.This multi-scale map shows the most common race/ethnicity living within an area. Map opens at tract-level in Los Angeles, CA but has national coverage. Zoom out to see counties and states.This map uses 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. The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.

Anti-Black or African American attacks were the most common form of racist hate crime in the United States in 2023, with 3,027 cases. Anti-White hate crimes were the next most common form of race-based hate crime in that year, with 831 incidents.

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

This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

This map shows the percentage of people who identify as something other than non-Hispanic white throughout the US according to the most current American Community Survey. The pattern is shown by states, counties, and Census tracts. Zoom or search for anywhere in the US to see a local pattern. Click on an area to learn more. Filter to your area and save a new version of the map to use for your own mapping purposes.The Arcade expression used was: 100 - B03002_calc_pctNHWhiteE, which is simply 100 minus the percent of population who identifies as non-Hispanic white. The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map update automatically annually when the newest estimates are released by ACS. For more detailed metadata, visit the ArcGIS Living Atlas Layer: ACS Race and Hispanic Origin Variables - Boundaries.The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.Other maps of interest:American Indian or Alaska Native Population in the US (Current ACS)Asian Population in the US (Current ACS)Black or African American Population in the US (Current ACS)Hawaiian or Other Pacific Islander Population in the US (Current ACS)Hispanic or Latino Population in the US (Current ACS) (some people prefer Latinx)Population who are Some Other Race in the US (Current ACS)Population who are Two or More Races in the US (Current ACS) (some people prefer mixed race or multiracial)White Population in the US (Current ACS)Race in the US by Dot DensityWhat is the most common race/ethnicity?

This dataset tracks annual two or more races student percentage from 2012 to 2023 for Dwight Common School vs. Illinois and Dwight Common SD 232 School District

Between 1982 and September 2024, 82 out of the 151 mass shootings in the United States were carried out by White shooters. By comparison, the perpetrator was African American in 26 mass shootings, and Latino in 12. When calculated as percentages, this amounts to 54 percent, 17 percent, and eight percent respectively. Race of mass shooters reflects the U.S. population Broadly speaking, the racial distribution of mass shootings mirrors the racial distribution of the U.S. population as a whole. While a superficial comparison of the statistics seems to suggest African American shooters are over-represented and Latino shooters underrepresented, the fact that the shooter’s race is unclear in around nine percent of cases, along with the different time frames over which these statistics are calculated, means no such conclusions should be drawn. Conversely, looking at the mass shootings in the United States by gender clearly demonstrates that the majority of mass shootings are carried out by men. Mass shootings and mental health With no clear patterns between the socio-economic or cultural background of mass shooters, increasing attention has been placed on mental health. Analysis of the factors Americans considered to be to blame for mass shootings showed 80 percent of people felt the inability of the mental health system to recognize those who pose a danger to others was a significant factor. This concern is not without merit – in over half of the mass shootings since 1982, the shooter showed prior signs of mental health issues, suggesting improved mental health services may help deal with this horrific problem. Mass shootings and guns In the wake of multiple mass shootings, critics have sought to look beyond the issues of shooter identification and their influences by focusing on their access to guns. The majority of mass shootings in the U.S. involve firearms which were obtained legally, reflecting the easy ability of Americans to purchase and carry deadly weapons in public. Gun control takes on a particular significance when the uniquely American phenomenon of school shootings is considered. The annual number of incidents involving firearms at K-12 schools in the U.S. was over 100 in each year since 2018. Conversely, similar incidents in other developed countries exceptionally rare, with only five school shootings in G7 countries other than the U.S. between 2009 and 2018.

Context

Formula One is the highest class of international racing for open-wheel single-seater racing cars sanctioned by the Fédération Internationale de l'Automobile (FIA). Ever since its inaugural season in 1950, Formula1 has been regarded as the pinnacle of motorsport.

Content

This dataset contains detailed information about qualifying and race results for all the tracks over the course of multiple seasons. There is a separate directory for each season. There are 2 sub-directories for each season, namely: Qualifying Results and Race Results. The Race Results directory contains an overall_race_results.csv file which summarizes the race results throughout the entire season. It also contains multiple .csv files for the results of each race in the season. The Qualifying Results directory contains multiple .csv files for the qualifying results before the start of each race.

Note

For the 1982 season and before the qualifying results contain only 1 entry in the file which is that of the polesitter. The lap times of the other drivers were not accounted for, and on the official website there is only 1 entry under the qualifying results.

Inspiration

F1 is one of my favorite sports and I almost never miss a race 😄

The motivation behind creating this dataset was to learn more about web scraping and try to perform a statistical analysis of the data. Some of the things you could do with the entire dataset are as follows: - Identify the driver with the most poles - Compare qualifying times of different drivers (championship contenders, team-mates, etc) - Determine how often a particular driver out-qualifies his team-mate - Compare qualifying lap times of a race from previous seasons - Identify the driver with the most number of wins at a particular track - Analyze how the championship battle unfolded based on the number of points scored by the drivers (specially interesting for the 2021 f1 season 👀) - Identify drivers with the highest number of wins, podiums, DNFs, etc - Compare the average lap times of different tracks to identify the slowest and fastest tracks on the calendar - Compare the number of laps for each race in the season (Belgium 2021 being the clear winner 😂) - Find out who won the Driver's Championship based on the total number of points - Find out who won the Constructor's Championship based on the total number of points for each team

Some Common F1 Terms You Might Come Across

• DNF: Did Not Finish. Commonly used nomenclature for drivers that crashed/failed to complete the entire race
• DNQ: Did Not Qualify. Eliminated missing values from the qualifying datasets by introducing this abbreviation for drivers who failed to qualify.
• NC: Not Confirmed. For drivers that DNF the term NC is used in the Position column
• DQ: Disqualified. Generally drivers are disqualified from races due to technical infringements or a breach of sporting regulations (Example: Sebastian Vettel was disqualified from the 2021 Hungarian Grand Prix due to fuel irregularites and stripped of all the points he earned from finishing the race in P2)

Future Work

As I collect more data for the previous seasons, I will create new versions for the dataset. The goal with this dataset is to create an archive of qualifying and race data from 1950-2021. The dataset will also be updated when the 2022 season commences.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Twin Groves, the largest racial group is Black or African American alone with a population of 159 (58.24% 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 Twin Groves
• Population: The population of the racial category (for Non-Hispanic) in the Twin Groves is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Twin Groves 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 Twin Groves Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Twin Groves by race. It includes the population of Twin Groves across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Twin Groves across relevant racial categories.

Key observations

The percent distribution of Twin Groves population by race (across all racial categories recognized by the U.S. Census Bureau): 38.27% are white, 57.40% are Black or African American and 4.33% are multiracial.

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 (excluding ethnicity) for the Twin Groves
• Population: The population of the racial category (excluding ethnicity) in the Twin Groves is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Twin Groves total 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 Twin Groves Population by Race & Ethnicity. You can refer the same here

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

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.

According to the Wikipedia, an ultramarathon, also called ultra distance or ultra running, is any footrace longer than the traditional marathon length of 42.195 kilometres (26 mi 385 yd). Various distances are raced competitively, from the shortest common ultramarathon of 31 miles (50 km) to over 200 miles (320 km). 50k and 100k are both World Athletics record distances, but some 100 miles (160 km) races are among the oldest and most prestigious events, especially in North America.}

The data in this file is a large collection of ultra-marathon race records registered between 1798 and 2022 (a period of well over two centuries) being therefore a formidable long term sample. All data was obtained from public websites.

Despite the original data being of public domain, the race records, which originally contained the athlete´s names, have been anonymized to comply with data protection laws and to preserve the athlete´s privacy. However, a column Athlete ID has been created with a numerical ID representing each unique runner (so if Antonio Fernández participated in 5 races over different years, then the corresponding race records now hold his unique Athlete ID instead of his name). This way I have preserved valuable information.

The dataset contains 7,461,226 ultra-marathon race records from 1,641,168 unique athletes.

The following columns (with data types) are included:

• Year of event (int64)
• Event dates (object)
• Event name (object)
• Event distance/length (object)
• Event number of finishers (int64)
• Athlete performance (object)
• Athlete club (object)
• Athlete country (object)
• Athlete year of birth (float64)
• Athlete gender (object)
• Athlete age category (object)
• Athlete average speed (object)
• Athlete ID (int64)

The Event name column include country location information that can be derived to a new column, and similarly seasonal information can be found in the Event dates column beyond the Year of event (these can be extracted with a bit of processing).

The Event distance/length column describes the type of race, covering the most popular UM race distances and lengths, and some other specific modalities (multi-day, etc.):

• Distances: 50km, 100km, 50mi, 100mi
• Lengths: 6h, 12h, 24h, 48h, 72h, 6d, 10d

Additionally, there is information of age, gender and speed (in km/h) in other columns.

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

In 2022, the child abuse rate for children of Hispanic origin was at 7, indicating 7 out of every 1,000 Hispanic children in the United States suffered from some sort of abuse. This rate was highest among American Indian or Alaska Native children, with 14.3 children out of every 1,000 experiencing some form of abuse. Child abuse in the U.S. The child abuse rate in the United States is highest among American Indian or Alaska Native victims, followed by African-American victims. It is most common among children between two to five years of age. While child abuse cases are fairly evenly distributed between girls and boys, more boys than girls are victims of abuse resulting in death. The most common type of maltreatment is neglect, followed by physical abuse. Risk factors Child abuse is often reported by teachers, law enforcement officers, or social service providers. In the large majority of cases, the perpetrators of abuse were a parent of the victim. Risk factors, such as teen pregnancy, violent crime, and poverty that are associated with abuse and neglect have been found to be quite high in the United States in comparison to other countries.

This dataset tracks annual two or more races student percentage from 2012 to 2023 for Edinburg Common School vs. New York and Edinburg Common School District

A growing body of research uses names to cue experimental subjects about race, ethnicity, and gender. However, researchers have not explored the myriad of characteristics that might be signaled by these names. In this paper, we introduce a large, publicly available database of the attributes associated with common American first and last names. For 1,000 first names and 21 last names, we provide ratings of perceived race; for 336 first names, we provide ratings on 26 social and personal characteristics. We show that the traits associated with first names vary widely, even among names associated with the same race and gender. Researchers using names to signal group memberships are thus likely cuing a number of other attributes as well. We demonstrate the importance of name selection by replicating DeSante (2013). We conclude by outlining two approaches researchers can use to choose names that successfully cue race (and gender) while minimizing potential confounds.

This layer shows race and ethnicity data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, Consolidated City, Census Designated Place, Incorporated Place boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also 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 above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P5, P9 Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, Consolidated City, Census Designated Place, Incorporated PlaceNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

Snapshot img

Horse Racing Market Size 2024-2028

The horse racing market size is forecast to increase by USD 114.5 billion at a CAGR of 14.71% between 2023 and 2028. The market is experiencing significant growth, driven by several key trends and factors. One notable trend is the increasing involvement of younger audiences in horse racing, as evidenced by the rising popularity of events like the Kentucky Derby and the Royal Ascot among millennials. Another trend is the rise in the adoption of online betting platforms, which has made horse racing more accessible and convenient for fans around the world. However, the market also faces challenges, including growing concerns for animal welfare and the potential for regulatory issues in various jurisdictions. Despite these challenges, the horse racing industry continues to thrive, driven by its rich history, tradition, and the excitement of live racing events.

What will be the Size of the Market During the Forecast Period?

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The market is an age-old tradition that continues to captivate audiences worldwide. This industry encompasses various aspects, including training regimens, professionalism, and the roles of trainers, jockeys, breeders, owners, and bettors. In this analysis, we delve into the key trends and developments shaping the market. The integration of technology advancements, such as Artificial Intelligence, has transformed the industry, making it more accessible and immersive for fans. Horses undergo rigorous training regimens to ensure they are at their best during races. Trainers employ various techniques to optimize their horses' fitness and conditioning, focusing on both physical and mental well-being.

Moreover, Jockeys, too, play a crucial role in maximizing a horse's potential, employing expert riding skills and race strategy. Breeders and trainers prioritize the health and well-being of their horses, adhering to ethical practices and implementing the latest advancements in horse care. This commitment to animal welfare not only benefits the horses but also enhances the reputation of the industry. Fans can now watch races live, place bets online, and access real-time race information from anywhere.

Furthermore, technology advancements, such as artificial intelligence, are being integrated into various aspects of the industry, from race analysis to horse health monitoring. However, there is a growing focus on sustainability and reducing the industry's environmental impact. Initiatives include the use of renewable energy sources at racetracks and the promotion of eco-friendly practices among participants. Breeders invest significant resources in producing top-performing horses, while owners take pride in their horses' achievements.

In addition, the market also offers various opportunities for individuals to invest in horse ownership, making it an attractive proposition for those with disposable income. By staying informed of these trends, industry participants can position themselves for success and contribute to the ongoing growth and innovation of this captivating industry.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Flat racing
  Jump racing
  Harness racing
  Endurance racing


Revenue Stream

  Betting revenue
  Live event revenue
  Broadcasting rights
  Sponsorship and advertising
  Horse sales and breeding


Geography

  North America

    US


  Europe

    UK
    France


  APAC

    Japan


  Middle East and Africa



  South America

By Type Insights

The flat racing segment is estimated to witness significant growth during the forecast period.Flat racing is a popular spectator sport in which horses compete against each other over a set distance, ranging from 402 to 4,828 meters. The majority of these races take place on Turf, surfaces, which are natural grass tracks, although in North America, dirt surfaces, composed of sand and local soil, are more common. With millions of people attending race meetings annually, particularly in the UK, flat racing holds significant cultural and economic importance. This sport is deeply ingrained in the culture, often accompanied by fashion and social gatherings. The thrill of flat horse racing lies in its speed, strategy, and the unique bond between horses and jockeys.

Furthermore, online platforms have revolutionized the way people engage with this dynamic and integral part of the equestrian sports landscape. Horse ownership and breeding have become more accessible, allowing individuals to invest in their passion. Wagering on races has also become easier than ever before, with various online betting sites and apps available. Racetracks offer hospitality experiences, providi