Estimating gambling venue catchments for impact assessment using a calibrated gravity model

Francis Markham*, Bruce Doran, Martin Young

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)


    Gambling using electronic gaming machines (EGMs) has emerged as a significant public health issue. While social impact assessments are required prior to the granting of new gaming machine licenses in Australia, there are a few established techniques for estimating the spatial distribution of a venue's clientele. To this end, we calibrated a Huff model of gambling venue catchments based on a geocoded postal survey (n = 7040). We investigated the impact of different venue attractiveness measures, distance measures, distance decay functions, levels of spatial aggregation and venue types on model fit and results. We then compared model estimates for different behavioural subgroups. Our calibrated spatial model is a significant improvement on previously published models, increasing R2 from 0.23 to 0.64. Venue catchments differ radically in size and intensity. As different population subgroups are attracted to different venues, there is no single best index of venue attractiveness applicable to all subpopulations. The calibrated Huff model represents a useful regulatory tool for predicting the extent and composition of gambling venue catchments. It may assist in decision-making with regard to new license applications and evaluating the impact of health interventions such as mandated reductions in EGM numbers. Our calibrated parameters may be used to improve model accuracy in other jurisdictions.

    Original languageEnglish
    Pages (from-to)326-342
    Number of pages17
    JournalInternational Journal of Geographical Information Science
    Issue number2
    Publication statusPublished - Feb 2014


    Dive into the research topics of 'Estimating gambling venue catchments for impact assessment using a calibrated gravity model'. Together they form a unique fingerprint.

    Cite this