Analysis of the factors affecting the time spent on leisure activities by using an ordered logit model

ABSTRACTThe objective of the current study is to analyze the time spent on leisure activities in Budapest, considering five influencing factors. Data were collected from Google Popular Time (GPT) via location services using Python, resulting in a dataset of 1336 entries from July 2022. The analysis utilized the Ordered Logit Model (OLM). According to the outcomes, about 17% of visitors allocate significant time to leisure, while half spend relatively less. Leisure time is positively influenced by ratings and location but negatively affected by security levels. This study demonstrates the utility of GPT data for understanding individual behavior, offering valuable insights for decision-makers, tourism managers, and planners. Additionally, it sheds light on leisure-related traffic patterns, aiding in the identification of popular locations and peak time periods for leisure activities. This information can indirectly impact traffic flow in specific areas.KEYWORDS: leisure activitiesactivity durationordered logit modelGPT data AcknowledgmentsThis research was supported by the János Bolyai Research Fellowship of the Hungarian Academy of Sciences (BO/00090/21/6). The linguistic revision of current paper is prepared by Eszter Tóth.Disclosure statementThe authors declare no conflict of interest.Additional informationFundingThis project (2019-2.1.7-ERA-NET-2021-00019) has received funding from the NRDI Fund by the National Research, Development and Innovation Office Hungary and the ERA-NET COFUND/EJP COFUND Programme with co-funding from the European Union Horizon 2020 research and innovation programme.