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  • 标题:Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network
  • 本地全文:下载
  • 作者:Yen-Hsun Lin ; Yi-Chung Chen ; Sheng-Min Chiu
  • 期刊名称:ISPRS International Journal of Geo-Information
  • 电子版ISSN:2220-9964
  • 出版年度:2022
  • 卷号:11
  • 期号:5
  • 页码:314
  • DOI:10.3390/ijgi11050314
  • 语种:English
  • 出版社:MDPI AG
  • 摘要:Spatial information analysis has gained increasing attention in recent years due to its wide range of applications, from disaster prevention and human behavioral patterns to commercial value. This study proposes a novel application to help businesses identify optimal locations for new stores. Optimal store locations are close to other stores with similar customer groups. However, they are also a suitable distance from stores that might represent competition. The style of a new store also exerts a significant effect. In this paper, we utilized check-in data and user profiles from location-based social networks to calculate the degree of influence of each store in a road network on the query user to identify optimal new store locations. As calculating the degree of influence of every store in a road network is time-consuming, we added two accelerating algorithms to the proposed baseline. The experiment results verified the validity of the proposed approach.
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