{"title":"The geography of gambling: A socio-spatial analysis of gambling machine location and area-level socio-economic status","authors":"Søren Kristiansen, R. Lund","doi":"10.4309/jgi.2022.49.2","DOIUrl":null,"url":null,"abstract":"This study mapped the geographical location and density of electronic gambling machines (EGMs) in Denmark and investigated whether gambling machines cluster in areas with specific socio-economic status (SES) characteristics. Using micro-area modeling and inverse probability weighted regression adjustments, the study was based on register data on SES, EGM location data and geographical grid data. Findings showed that EGMs were distributed throughout the country with some notable clusters in the larger cities. While identifying city-based hotspots, findings also indicated that pure population density offered merely partial explanations in term of EGM location. In terms of links between area-level SES and EGM density, the study found a significant and positive correlation between low level of SES and EGM density. This study could inform fine grained geographical risk localization and harm minimizing measures that transcends well-known administrative area classifications.","PeriodicalId":45414,"journal":{"name":"Journal of Gambling Issues","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Gambling Issues","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4309/jgi.2022.49.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"SUBSTANCE ABUSE","Score":null,"Total":0}
引用次数: 6
Abstract
This study mapped the geographical location and density of electronic gambling machines (EGMs) in Denmark and investigated whether gambling machines cluster in areas with specific socio-economic status (SES) characteristics. Using micro-area modeling and inverse probability weighted regression adjustments, the study was based on register data on SES, EGM location data and geographical grid data. Findings showed that EGMs were distributed throughout the country with some notable clusters in the larger cities. While identifying city-based hotspots, findings also indicated that pure population density offered merely partial explanations in term of EGM location. In terms of links between area-level SES and EGM density, the study found a significant and positive correlation between low level of SES and EGM density. This study could inform fine grained geographical risk localization and harm minimizing measures that transcends well-known administrative area classifications.