TY - GEN
T1 - A Novel Approach for Company Real Workplace Identification via E-commercial Data
AU - Ren, Huimin
AU - Ruan, Sijie
AU - Yuan, Ye
AU - Li, Yanhua
AU - Bao, Jie
AU - He, Tianfu
AU - He, Huajun
AU - Meng, Chuishi
AU - Zheng, Yu
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/11/13
Y1 - 2023/11/13
N2 - Urban growth benefits significantly from local business development. However, factors like traffic and labor shortages sometimes cause companies to operate away from their registered addresses, resulting in governance challenges. This paper introduces "LocRecognizer,"a data mining method that leverages e-commerce data to pinpoint companies' real-world operational locations. Based on the principle that areas with a high concentration of company-related users likely indicate actual workplaces, LocRecognizer combines hierarchical clustering with a deep learning model for accurate detection. When tested on datasets from Beijing and Nantong, it outperformed six baselines. A practical implementation of this system has been operational in Nantong since September 2021, attesting to its effectiveness.
AB - Urban growth benefits significantly from local business development. However, factors like traffic and labor shortages sometimes cause companies to operate away from their registered addresses, resulting in governance challenges. This paper introduces "LocRecognizer,"a data mining method that leverages e-commerce data to pinpoint companies' real-world operational locations. Based on the principle that areas with a high concentration of company-related users likely indicate actual workplaces, LocRecognizer combines hierarchical clustering with a deep learning model for accurate detection. When tested on datasets from Beijing and Nantong, it outperformed six baselines. A practical implementation of this system has been operational in Nantong since September 2021, attesting to its effectiveness.
KW - geographic information system
KW - location detection
KW - spatial-temporal data mining
UR - http://www.scopus.com/inward/record.url?scp=85182505993&partnerID=8YFLogxK
U2 - 10.1145/3589132.3625589
DO - 10.1145/3589132.3625589
M3 - Conference contribution
AN - SCOPUS:85182505993
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
A2 - Damiani, Maria Luisa
A2 - Renz, Matthias
A2 - Eldawy, Ahmed
A2 - Kroger, Peer
A2 - Nascimento, Mario A.
PB - Association for Computing Machinery
T2 - 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
Y2 - 13 November 2023 through 16 November 2023
ER -
