 Imputing relevant information from multi-day GPS tracers for retail planning and management using data fusion and context-sensitive learningAnastasia Moiseeva and Harry Timmermans Journal of Retailing and Consumer Services, 2010, vol. 17, issue 3, 189-199 Abstract: It is well known that the right location of shopping centres is of paramount importance. Unless stores succeed in attracting their own clientele, they rely to a large extent on the impulse behaviour of shoppers. To evaluate alternative locations, models of pedestrian behaviour may be useful. Modern technologies such as GPS and RFID offer new possibilities providing data on routes and stops, which are required as input for such models. An automatic interpretation of GPS tracers with respect to the activities being conducted could enhance the applicability of such technologies to retail management applications. This paper reviews this rapidly growing literature, and shows how automatic data imputation can be established by using Bayesian belief networks and how GPS traces can be fused with land use data of retail location. Keywords: Multi-day GPS tracers; Data fusion; Retail planning; Shopping behaviours (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:17:y:2010:i:3:p:189-199 DOI: 10.1016/j.jretconser.2010.03.011 Access Statistics for this article Journal of Retailing and Consumer Services is currently edited by Harry Timmermans More articles in Journal of Retailing and Consumer Services from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.