  In this article, we 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 our
large-scale and anonymized bank card transaction dataset enables us to output
linked trips for shopping all over the city: the method enables us 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, our methodology can consider
all transaction activities conducted by customers for a whole city in addition
to the location of stores dispersed in the city. This approach enables us 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.
