TY - GEN
T1 - Analysis of Co-Location Pattern in Surabaya City Convenience Franchise Store
AU - Hirzi, Naufal Muhammad
AU - Imanto, Teguh
AU - Arymurthy, Aniati Murni
AU - Setiyoko, Andie
AU - Yulianto, Fajar
N1 - Publisher Copyright:
© 2023 American Institute of Physics Inc.. All rights reserved.
PY - 2023/12/11
Y1 - 2023/12/11
N2 - The Indonesian retail industry continues to grow, especially modern retail owned by individuals, which needs to reflect on the successful franchise retail industry. This study discusses the analysis of location selection at one of the largest retailers in Indonesia through a spatial pattern analysis approach that is useful as a role model for individual retailers. The object of this research is Indomaret retail in Surabaya city, the second big city in Indonesia after Jakarta based on its population. Data are collected from OpenStreetMap and co-location mining is performed on relevant location attributes and then evaluated using the participation index (PI). PI is calculated against major attributes, combinations of 2 minor attributes, and combinations of 3 minor attributes, then sorted to get the top 3 attributes. The major attribute with the highest PI score is footfall, while the minor attribute with 2 combinations results is worship facility, and with 3 combinations results in food service. Spatial data analysis is proven to detect retail store location patterns. The results of this study can be applied as a reference for location selection in opening new retail stores, especially in the high-density population area which has a similar type to Surabaya city.
AB - The Indonesian retail industry continues to grow, especially modern retail owned by individuals, which needs to reflect on the successful franchise retail industry. This study discusses the analysis of location selection at one of the largest retailers in Indonesia through a spatial pattern analysis approach that is useful as a role model for individual retailers. The object of this research is Indomaret retail in Surabaya city, the second big city in Indonesia after Jakarta based on its population. Data are collected from OpenStreetMap and co-location mining is performed on relevant location attributes and then evaluated using the participation index (PI). PI is calculated against major attributes, combinations of 2 minor attributes, and combinations of 3 minor attributes, then sorted to get the top 3 attributes. The major attribute with the highest PI score is footfall, while the minor attribute with 2 combinations results is worship facility, and with 3 combinations results in food service. Spatial data analysis is proven to detect retail store location patterns. The results of this study can be applied as a reference for location selection in opening new retail stores, especially in the high-density population area which has a similar type to Surabaya city.
UR - http://www.scopus.com/inward/record.url?scp=85180390299&partnerID=8YFLogxK
U2 - 10.1063/5.0181449
DO - 10.1063/5.0181449
M3 - Conference contribution
AN - SCOPUS:85180390299
T3 - AIP Conference Proceedings
BT - AIP Conference Proceedings
A2 - Septanto, Harry
A2 - Adhynugraha, Muhammad Ilham
A2 - Vetrita, Yenni
A2 - Santosa, Cahya Edi
A2 - Sitompul, Peberlin Parulian
A2 - Yulihastin, Erma
A2 - Muhamad, Johan
A2 - Mardianis, null
A2 - Fitrianingsih, Ery
A2 - Batubara, Mario
A2 - Abadi, Prayitno
A2 - Restasari, Afni
PB - American Institute of Physics Inc.
T2 - 9th International Seminar on Aerospace Science and Technology, ISAST 2022
Y2 - 22 November 2022 through 23 November 2022
ER -