Automated prediction of shopping behaviours using taxi trajectory data and social media reviews

Shuhui Gong, John Cartlidge, Ruibin Bai, Yang Yue, Qingquan Li, Guoping Qiu

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
256 Downloads (Pure)


The Huff model is a well used mathematical abstraction for predicting shopping centre patronage. It considers two factors: shopping centre attractiveness, and customers’ travel costs. Here, we use taxi trajectory data (more than three million journeys) and social media data (more than eight thousand customer reviews) to calibrate the Huff model for five primary shopping centres in the rapidly expanding metropolitan city of Shenzhen, China. The Huff model is calibrated in two ways: globally, to find the single pair of best-fit parameters for attractiveness and travel cost; and locally, using Geographical Weighted Regression to find the best-fit parameters at each spatial location. Results demonstrate that customer reviews on social media provide relatively high prediction accuracy for weekend shopping behaviours when the Huff model is calibrated globally. In contrast, customer footfall, calculated directly from number of taxi journeys, provides higher prediction accuracy when the Huff model is calibrated locally. This suggests that, at weekends, sensitivity to footfall has greater spatial variance (i.e., customers living in some areas have greater preference for shopping at popular centres) than sensitivity to customer reviews (i.e., regardless of where customers live, positive reviews on social media are equally likely to affect behaviour). We present this geographical homogeneity in review sensitivity and heterogeneity in footfall sensitivity as a novel discovery with potential applications in urban, retail, and transportation planning.
Original languageEnglish
Title of host publication2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA 2018)
Subtitle of host publicationProceedings of a meeting held 9-12 March 2018, Shanghai, China
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538647943
ISBN (Print)9781538647950
Publication statusPublished - Jun 2018
EventIEEE International Conference on Big Data Analysis - East China University of Science and Technology, Shanghai, China
Duration: 9 Mar 201812 Mar 2018
Conference number: 3


ConferenceIEEE International Conference on Big Data Analysis
Abbreviated titleICBDA 2018
Internet address


  • Social media data
  • Taxi trajectory data
  • Huff model
  • Geographically weighted regression

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