Mapping High Risk Drinking Locations from Different Clustering Methods

Over the years, there has been a significant increase in the prevalence of diseases associated with the misuse of alcoholic beverages, resulting in three million annual deaths worldwide. Despite this alarming trend, there is a lack of dedicated applications to support individuals in their recovery from alcohol abuse. In light of this situation, the literature presents machine learning techniques that can be employed to identify and characterize urban areas with a high propensity for alcohol consumption in major cities. This study explores the utilization of Location-Based Social Networks (LBSN) to assess alcohol consumption habits in Tokyo and New York. Data from check-ins at bars and restaurants were collected, and through clustering methods, the study examined the drinking patterns of urban residents. The findings revealed that, while there were cultural variations in drinking behaviors between the two cities, users tended to consume more alcohol during weekends and nighttime. Furthermore, the research successfully pinpointed the regions most conducive to such consumption.