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Applying Check-in Data and User Profiles to Identify Optimal Store Locations in a Road Network
| Content Provider | MDPI |
|---|---|
| Author | Lin, Yen-Hsun Chen, Yi-Chung Chiu, Sheng-Min Lee, Chiang Wang, Fu-Cheng |
| Copyright Year | 2022 |
| 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. |
| Starting Page | 314 |
| e-ISSN | 22209964 |
| DOI | 10.3390/ijgi11050314 |
| Journal | ISPRS International Journal of Geo-Information |
| Issue Number | 5 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-16 |
| Access Restriction | Open |
| Subject Keyword | ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Information and Library Science Road Network Optimal Location Selection Check-in Data User Profile G-tree |
| Content Type | Text |
| Resource Type | Article |
