The statistic depicts the survey results concerned with the question wether the respondents gambled at a casino in the past twelve months in 2010 and 2011. 29 percent of the male respondents had done so.

The aim of this data collection was to gauge the impact of legalized casino gambling on the level and spatial distribution of crime in the Atlantic City region by comparing crime rates before and after the introduction of this type of gambling in the area. Data for the years 1972 through 1984 were collected from various New Jersey state publications for 64 localities and include information on population size and density, population characteristics of race, age, per capita income, education and home ownership, real estate values, number of police employees and police expenditures, total city expenditure, and number of burglaries, larcenies, robberies and vehicle thefts. Spatial variables include population attributes standardized by land area in square miles, and measures of accessibility, location, and distance from Atlantic City. For the 1970/1980 data file, additional variables pertaining to population characteristics were created from census data to match economic and crime attributes found in the 1972-1984 data. Data on eight additional locations are available in the 1970/1980 file.

In a survey held between September 2024 and January 2025, it was found that the age group with the highest share of online gamblers in Great Britain was 45 to 54 years old. This demographic had a 42 percent share of individuals who had gambled in the previous four weeks.

This data set included population and expenditure breakdowns by LGA,\r
demographic statistics, labor statistics and Socio Economis Indexes for Areas\r
(SEIFA) LGA score and ranking per LGA.\r
\r
Detailed descriptions of this data set include: \r
\- Region (LGA name) \r
\- Population (18+) projection \r
\- Venue numbers \r
\- EGM numbers \r
\- Total net expenditures \r
\- Population (18+) per venue \r
\- Net EGM expenditure per adult \r
\- EGM per 1000 (18+)\r
\r

This study sought to examine the extent, nature, and consequences of pathological gambling disorders in arrestee populations. Five research questions were addressed: (1) What is the prevalence of pathological gambling in arrestee populations? (2) What is the profile of the pathological gambler arrested for felony and misdemeanor offenses? (3) How does the nature and level of criminal activity among pathological gamblers compare to that of non- pathological gamblers? (4) What proportion of the crime committed by offenders with pathological gambling disorders is linked to their gambling activities (either to fund gambling or pay off gambling debts)? and (5) How does substance abuse interact with pathological gambling to affect the nature and extent of criminal activity? The data for this research were collected in conjunction with the National Institute of Justice's Arrestee Drug Abuse Monitoring (ADAM) programs. Arrestees in Las Vegas, Nevada, and Des Moines, Iowa, who had completed the ADAM interview and provided a urine specimen were asked if they would be willing to answer an additional set of questions concerning their gambling behavior. Data from the ADAM interview and drug screening were merged with data collected using the gambling addendum, producing the dataset for this study. Variables from the ADAM instruments were comprised of demographic data on each arrestee, calendar of admissions to drug treatment-related programs, data on dependence and abuse, drug market and use data, and urine test results. The gambling addendum was used to collect data on five topics: (1) past-year gambling activity, (2) the use of alcohol and illegal drugs prior to and during gambling, (3) substance abuse and/or self-reported gambling problems, (4) past-year criminal activity (property, drug, and violent offending), and (5) motivations for criminal activity (gambling or non-gambling related).

This data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2016/17 financial year. The data has been joined with LGA 2011 boundaries.

For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website.

Please note:

• AURIN has spatially enabled the original data.

• EGM Numbers: a venue may be operating less machines than its licensed or attached numbers

• Gaming Machine Density calculations are based on operating gaming machines with attached entitlements divided by adult population divided by 1,000 (gaming machines per 1,000 adults).

• Expenditure / EGM: Refers to the average Expenditure per Gaming Machine. In 2013, these figures are based on net expenditures divided by number of gaming machines with attached entitlements.

• SEIFA Scores: a lower score indicates that an area is relatively disadvantaged compared to an area with a higher score. Scores should only be used in distributive analysis. Rankings are based on highest score, for example 1st ranking in index of disadvantage means the LGA is least disadvantaged.

• Disclaimer: Care must be taken in using any figures for an LGA involving net expenditure and population. The expenditure per person in an LGA may include an amount of expenditure coming from persons not living within the LGA.

This data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2017/18 financial year. The data has been joined with LGA 2011 boundaries.

For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website.

Please note:

• AURIN has spatially enabled the original data.

• EGM Numbers: a venue may be operating less machines than its licensed or attached numbers

• Gaming Machine Density calculations are based on operating gaming machines with attached entitlements divided by adult population divided by 1,000 (gaming machines per 1,000 adults).

• Expenditure / EGM: Refers to the average Expenditure per Gaming Machine. In 2013, these figures are based on net expenditures divided by number of gaming machines with attached entitlements.

• SEIFA Scores: a lower score indicates that an area is relatively disadvantaged compared to an area with a higher score. Scores should only be used in distributive analysis. Rankings are based on highest score, for example 1st ranking in index of disadvantage means the LGA is least disadvantaged.

• Disclaimer: Care must be taken in using any figures for an LGA involving net expenditure and population. The expenditure per person in an LGA may include an amount of expenditure coming from persons not living within the LGA.

This data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2011/12 financial year. The data has been joined with LGA 2006 boundaries.

For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website.

Please note:

• AURIN has spatially enabled the original data.

• Data may not have been fully audited to date. Any changes are unlikely to have a material effect on the figures provided.

• EGM Numbers: a venue may be operating less machines than its licensed or attached numbers

• Gaming Machine Density calculations are based on operating gaming machines with attached entitlements divided by adult population divided by 1,000 (gaming machines per 1,000 adults).

• Expenditure / EGM: Refers to the average Expenditure per Gaming Machine.

• Disclaimer: Care must be taken in using any figures for an LGA involving net expenditure and population. The expenditure per person in an LGA may include an amount of expenditure coming from persons not living within the LGA.

This table provides data from 2021 on the estimated population aged 16 and over in the Canary Islands by types of games of chance or bets, frequency of play and age groups.

In 2024, the states with the highest revenue from iGaming were Pennsylvania and Michigan, generating 2.71 and 2.44 billion U.S. dollars in gaming revenue, respectively. Meanwhile, iGaming revenue in New Jersey totaled 2.39 billion U.S. dollars.

According to a gambling survey in Thailand in 2021, **** percent of male respondents indicated that they had gambled before. Meanwhile, **** percent of the female survey participants said they also gambled.

This statistic displays the distribution of online gambling in Finland in 2017, by socioeconomic status. During that year, ** percent of the population in manual labor had played online gambling games within the last 12 months. The corresponding figure for the unemployed population was ** percent, and for upper-level employees ** percent.

Distribution of gambling among the 16- to 65-year-old population in Germany 2007, 2009, 2011 and 2013. Presented are the prevalences of participation in any gambling in relation to the past 12 months, differentiated by age group and gender.

This graph presents the percentage of French people who stated gambling (scratch cards, lotto games, horse race betting, sports bets, casino games or poker) in 2019. It shows that ** percent of respondents declared that they gambled sometimes.

This dataset includes gaming venues by street address, suburb or town, venue name, licence number or licence category. Gambling data is regularly updated directly from the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) licensing database. Gambling licence data is updated monthly.

For more information visit the VCGLR's website.

Please note:

• AURIN has extracted venue lists from the VCGLR's interactive map of Victoria's gaming venues and spatially enabled them on the 8th of March 2019.

The Australia online gambling market size was valued at USD 5.2 Billion in 2024. Looking forward, IMARC Group estimates the market to reach USD 8.9 Billion by 2033, exhibiting a CAGR of 5.88% from 2025-2033. Favorable regulatory environment in the country, rapid technological advancements, changing consumer preferences, ongoing promotion and advertising activities, and expansion of variety of offerings are some of the factors bolstering the market Australia online gambling market share.

IMARC Group provides an analysis of the key trends in each segment of the Australia online gambling market, along with forecasts at the country and regional levels from 2025-2033. The market has been categorized based on game type and device.

This data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2013/14 financial year. The data has been joined with LGA 2011 boundaries.

For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website.

Please note:

• AURIN has spatially enabled the original data.

• EGM Numbers: a venue may be operating less machines than its licensed or attached numbers

• Gaming Machine Density calculations are based on operating gaming machines with attached entitlements divided by adult population divided by 1,000 (gaming machines per 1,000 adults).

• Expenditure / EGM: Refers to the average Expenditure per Gaming Machine.

• Disclaimer: Care must be taken in using any figures for an LGA involving net expenditure and population. The expenditure per person in an LGA may include an amount of expenditure coming from persons not living within the LGA.

This data set includes population and electronic gaming machine (EGM) expenditure breakdowns by local government area (LGA) and gaming venue, demographic statistics, labour statistics and Socio-Economic Indexes for Areas (SEIFA) LGA score and ranking per LGA for the 2009/10 financial year. The data has been joined with LGA 2006 boundaries.

For more information visit the Victorian Commission for Gambling and Liquor Regulation's (VCGLR) website.

Please note:

• AURIN has spatially enabled the original data.

• EGM Numbers: a venue may be operating less machines than its licensed or attached numbers

• Gaming Machine Density calculations are based on operating gaming machines with attached entitlements divided by adult population divided by 1,000 (gaming machines per 1,000 adults).

• Expenditure / EGM: Refers to the average Expenditure per Gaming Machine.

• Disclaimer: Care must be taken in using any figures for an LGA involving net expenditure and population. The expenditure per person in an LGA may include an amount of expenditure coming from persons not living within the LGA.

According to a 2025 survey, the age group with the largest share of individuals with an online sports betting acount in the United States was ********-years-old. In total, ** percent of U.S. adults belonging to this demographic had an account with an online sportsbook.

According to the source, the percentage distribution of the population in Spain that participates in gambling activities was observed to be the highest for men who carried out these activities in person with a total of 65.9 percent. Whereas it was was reported that 61.2 percent were women who gambled in person. On the other hand, 9.1 percent of the men surveyed gambled online and only 4.2 percent of women did.