Urban association rules: Uncovering linked trips for shopping behavior

In this article, the authors introduce the method of urban association rules and its uses for extracting frequently appearing combinations of stores that are visited together to characterize shoppers’ behaviors. The Apriori algorithm is used to extract the association rules (i.e. if -> result) from customer transaction datasets in a market-basket analysis. An application to the authors' large-scale and anonymized bank card transaction dataset enables them to output linked trips for shopping all over the city: the method enables the authors to predict the other shops most likely to be visited by a customer given a particular shop that was already visited as an input. In addition, the authors' methodology can consider all transaction activities conducted by customers for a whole city. This approach enables the authors to uncover not only simple linked trips such as transition movements between stores but also the edge weight for each linked trip in the specific district. Thus, the proposed methodology can complement conventional research methods. Enhancing understanding of people’s shopping behaviors could be useful for city authorities and urban practitioners for effective urban management. The results also help individual retailers to rearrange their services by accommodating the needs of their customers’ habits to enhance their shopping experience.

Language

  • English

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  • Accession Number: 01856654
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 29 2022 3:52PM