Hot time periods discovery for facility proportioning in urban commercial districts using POIs and mobile phone data.

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  • Additional Information
    • Author-Supplied Keywords:
      facility proportion analysis
      hot time period analysis
      K - means algorithm with constraints ( cK - means )
      mobile phone data
      Point of Interest (POI)
      self-adaptive DBS can algorithm ( DFB - saDBS can )
    • Abstract:
      It is becoming more and more important to make analysis on activity regularity of population in urban commercial districts, which would be beneficial to commercial infrastructure distribution and construction. The paper presents an approach to mining hot time periods in urban commercial districts using POI information and mobile phone data. First, a data field-based self-adaptive DBS can algorithm ( DFB - saDBS can ) is proposed to make clusters for commercial districts. Second, boundary determination algorithm is presented based on convex hull. Then, a K- means algorithm with constraints ( cK - means ) is proposed to make extraction of hot time periods from flow of people. On the other hand, we also propose an approach to regional POI proportion analysis via urban POI data. Taking advantage of structure measurement indexes of landscape ecosystem, we analyze relationship between population mobility and facility distribution in commercial districts. Taking a downtown area of Wuhan city, China as case study, our approach is verified to be effectiveness and accuracy. [ABSTRACT FROM AUTHOR]
    • Abstract:
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    • Author Affiliations:
      1International School of Software, Wuhan University, Wuhan 430079, China.
    • Full Text Word Count:
      7766
    • ISSN:
      2405-6456
    • Accession Number:
      10.3233/WEB-180375
    • Accession Number:
      130119352
  • Citations
    • ABNT:
      XIE, R. et al. Hot time periods discovery for facility proportioning in urban commercial districts using POIs and mobile phone data. Web Intelligence (2405-6456), [s. l.], v. 16, n. 2, p. 91–104, 2018. Disponível em: . Acesso em: 25 ago. 2019.
    • AMA:
      Xie R, Zhang H, Gong C, Zhang, Liu, Li. Hot time periods discovery for facility proportioning in urban commercial districts using POIs and mobile phone data. Web Intelligence (2405-6456). 2018;16(2):91-104. doi:10.3233/WEB-180375.
    • APA:
      Xie, R., Zhang, H., Gong, C., Zhang, Liu, & Li. (2018). Hot time periods discovery for facility proportioning in urban commercial districts using POIs and mobile phone data. Web Intelligence (2405-6456), 16(2), 91–104. https://doi.org/10.3233/WEB-180375
    • Chicago/Turabian: Author-Date:
      Xie, Rong, Hongyi Zhang, Chao Gong, Zhang, Liu, and Li. 2018. “Hot Time Periods Discovery for Facility Proportioning in Urban Commercial Districts Using POIs and Mobile Phone Data.” Web Intelligence (2405-6456) 16 (2): 91–104. doi:10.3233/WEB-180375.
    • Harvard:
      Xie, R. et al. (2018) ‘Hot time periods discovery for facility proportioning in urban commercial districts using POIs and mobile phone data’, Web Intelligence (2405-6456), 16(2), pp. 91–104. doi: 10.3233/WEB-180375.
    • Harvard: Australian:
      Xie, R, Zhang, H, Gong, C, Zhang, Liu & Li 2018, ‘Hot time periods discovery for facility proportioning in urban commercial districts using POIs and mobile phone data’, Web Intelligence (2405-6456), vol. 16, no. 2, pp. 91–104, viewed 25 August 2019, .
    • MLA:
      Xie, Rong, et al. “Hot Time Periods Discovery for Facility Proportioning in Urban Commercial Districts Using POIs and Mobile Phone Data.” Web Intelligence (2405-6456), vol. 16, no. 2, Apr. 2018, pp. 91–104. EBSCOhost, doi:10.3233/WEB-180375.
    • Chicago/Turabian: Humanities:
      Xie, Rong, Hongyi Zhang, Chao Gong, Zhang, Liu, and Li. “Hot Time Periods Discovery for Facility Proportioning in Urban Commercial Districts Using POIs and Mobile Phone Data.” Web Intelligence (2405-6456) 16, no. 2 (April 2018): 91–104. doi:10.3233/WEB-180375.
    • Vancouver/ICMJE:
      Xie R, Zhang H, Gong C, Zhang, Liu, Li. Hot time periods discovery for facility proportioning in urban commercial districts using POIs and mobile phone data. Web Intelligence (2405-6456) [Internet]. 2018 Apr [cited 2019 Aug 25];16(2):91–104. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=130119352&custid=s8280428