TY - JOUR
T1 - Applying Check-In Data and User Profiles to Identify Optimal Store Locations in a Road Network
AU - Lin, Yen Hsun
AU - Chen, Yi Chung
AU - Chiu, Sheng Min
AU - Lee, Chiang
AU - Wang, Fu Cheng
N1 - Funding Information:
Funding: This study was supported by the Research Assistantships funded by the Ministry of Science and Technology, Taiwan (grant number MOST 110-2121-M-224-001, to Y.-C.C.; MOST110-2221-E-006 -176, to C. L.)
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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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U2 - 10.3390/ijgi11050314
DO - 10.3390/ijgi11050314
M3 - Article
AN - SCOPUS:85130704804
SN - 2220-9964
VL - 11
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 5
M1 - 314
ER -