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Geographical and temporal huff model calibration using taxi trajectory data

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Abstract

The Huff model is designed to estimate the probability of shopping centre patronage based on a shopping centre’s attractiveness and the cost of a customer’s travel. In this paper, we attempt to discover some general shopping trends by calibrating the Huff model in Shenzhen, China, and New York, USA, using taxi trajectory GPS data and sharing bikes GPS data. Geographical and Temporal Weighted Regression (GTWR) is used to fit the model, and calibration results are compared with Ordinary Least Squares (OLS) regression, Geographical Weighted Regression (GWR), and Temporal Weighted Regression (TWR). Results show that GTWR gives the highest performance due to significant geographical and temporal variation in the Huff model parameters of attractiveness and travel cost. To explain the geographical variation, we use residential sales’ and rental prices in Shenzhen and New York as a proxy for customers’ wealth in each region. Pearson product-moment correlation results show a medium relationship between localised sales’ and rental prices and the Huff model parameter of attractiveness: that is, customer wealth explains geographic sensitivity to shopping area attractiveness. To explain temporal variation, we use census data in both Shenzhen and New York to provide job profile distributions for each region as a proxy to estimate customers’ spare leisure time. Regression results demonstrate that there is a significant linear relationship between the length of spare time and the parameter of shopping area attractiveness. In particular, we demonstrate that wealthy customers with less spare time are more sensitive to a shopping centre’s attractiveness. We also discover customers’ sensitivities to travel distance are related to their travel mode. In particular, people riding bikes to shopping areas care much more about trip distance compared with people who take taxi. Finally, results show a divergence in behaviours between customers in New York and Shenzhen at weekends. While customers in New York prefer to shop more locally at weekends, customers in Shenzhen care less about trip distance. We provide the GTWR calibration of the Huff model as our theoretical contribution. GTWR extends the Huff model to two dimensions (time and space), so as to analyse the differences of residents’ travel behaviours in different time and locations. We also provide the discoveries of factors affecting urban travel behaviours (wealth and employment) as practical contributions that may help optimise urban transportation design. In particular, the sensitivity of residents to the attraction of shopping areas has a significant positive linear relationship with the housing price and a significant negative linear relationship with the residents’ length of spare time.

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Notes

  1. For example, Bayesian Average is used by Internet Movie Data Base (IMDB; imdb.com), the world’s most popular source for movie, TV and celebrity content.

  2. A multiplicative model was also considered, such that attractiveness, S, is calculated as a linear function of size, reviews, and number of journeys. However, results showed that the three variables are colinear, thus rendering the model unusable.

  3. These data were collected from an open source website fang.com, which is the largest and most comprehensive open source repository for house price sales in China.

  4. The data of the 6th national population census is available open source from http://www.stats.gov.cn/ztjc/zdtjgz/zgrkpc/dlcrkpc/dlcrkpczl.

  5. The open source website is: sz.58.com.

  6. The open source questionnaire website is: https://opendata.cityofnewyork.us

  7. https://www1.nyc.gov/assets/finance/downloads/pdf/rolling_sales/rollingsales_manhattan.pdf

  8. https://www.rentcafe.com

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Acknowledgements

This research was supported by the International Doctoral Innovation Centre, Ningbo Education Bureau, the University of Nottingham, Zhejiang Natural Science Foundation (Grant No. LR17G010001), Ningbo Science and Technology Bureau (Grant No. 2014A35006, 2017D10034), UK Engineering and Physical Sciences Research Council (Grant No. EP/L015463 /1), the National Science Foundation of China (No. 71471092), the Zhejiang Lab’s International Talent Fund for Young Professionals, Shenzhen Scientific Research and Development Funding Program (No. CXZZS20150504141623042). John Cartlidge is sponsored by Refinitiv.

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Authors and Affiliations

  1. International Doctoral Innovation Centre, University of Nottingham Ningbo China, Ningbo, China

    Shuhui Gong

  2. Department of Urban Informatics, School of Architecture and Urban Planning Shenzhen University, Shenzhen, China

    Shuhui Gong

  3. Department of Computer Science, University of Bristol, Bristol, UK

    John Cartlidge

  4. School of Computer Science, University of Nottingham, Ningbo, China

    Ruibin Bai

  5. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Research Institute for Smart Cities Department of Urban Informatics, School of Architecture and Urban Planning Shenzhen University, Shenzhen, 518060, China

    Yang Yue & Qingquan Li

  6. School of Computer Science, University of Nottingham, Nottingham, NG8-1BB, UK

    Guoping Qiu

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  1. Shuhui Gong
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  2. John Cartlidge
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  4. Yang Yue
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Correspondence to Ruibin Bai.

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Gong, S., Cartlidge, J., Bai, R. et al. Geographical and temporal huff model calibration using taxi trajectory data. Geoinformatica 25, 485–512 (2021). https://doi.org/10.1007/s10707-019-00390-x

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