Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network
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. Optima...
| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
MDPI AG
2022-05-01
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| coleção: | ISPRS International Journal of Geo-Information |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/2220-9964/11/5/314 |
| _version_ | 1827668629322203136 |
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| author | Yen-Hsun Lin Yi-Chung Chen Sheng-Min Chiu Chiang Lee Fu-Cheng Wang |
| author_facet | Yen-Hsun Lin Yi-Chung Chen Sheng-Min Chiu Chiang Lee Fu-Cheng Wang |
| author_sort | Yen-Hsun Lin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:46:25Z |
| format | Article |
| id | doaj.art-6ed23080236440f69630b29435ead160 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T03:46:25Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-6ed23080236440f69630b29435ead1602023-11-23T11:20:05ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-05-0111531410.3390/ijgi11050314Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road NetworkYen-Hsun Lin0Yi-Chung Chen1Sheng-Min Chiu2Chiang Lee3Fu-Cheng Wang4Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701401, TaiwanDepartment of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin 640301, TaiwanDepartment of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701401, TaiwanDepartment of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701401, TaiwanDepartment of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin 640301, TaiwanSpatial 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.https://www.mdpi.com/2220-9964/11/5/314road networkoptimal location selectioncheck-in datauser profileG-tree |
| spellingShingle | Yen-Hsun Lin Yi-Chung Chen Sheng-Min Chiu Chiang Lee Fu-Cheng Wang Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network ISPRS International Journal of Geo-Information road network optimal location selection check-in data user profile G-tree |
| title | Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network |
| title_full | Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network |
| title_fullStr | Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network |
| title_full_unstemmed | Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network |
| title_short | Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network |
| title_sort | applying check in data and user profiles to identify optimal store locations in a road network |
| topic | road network optimal location selection check-in data user profile G-tree |
| url | https://www.mdpi.com/2220-9964/11/5/314 |
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