A Novel Approach for Company Real Workplace Identification via E-commercial Data

Huimin Ren; Sijie Ruan; Ye Yuan; Yanhua Li; Jie Bao; Tianfu He; Huajun He; Chuishi Meng; Yu Zheng

doi:10.1145/3589132.3625589

A Novel Approach for Company Real Workplace Identification via E-commercial Data

Huimin Ren
, Sijie Ruan^*
, Ye Yuan
, Yanhua Li^*
, Jie Bao
, Tianfu He
, Huajun He
, Chuishi Meng
, Yu Zheng

^*Corresponding author for this work

School of Computer Science and Technology

Worcester Polytechnic Institute
JD iCity
JD Technology
Southwest Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
Editors	Maria Luisa Damiani, Matthias Renz, Ahmed Eldawy, Peer Kroger, Mario A. Nascimento
Publisher	Association for Computing Machinery
ISBN (Electronic)	9798400701689
DOIs	https://doi.org/10.1145/3589132.3625589
Publication status	Published - 13 Nov 2023
Event	31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023 - Hamburg, Germany Duration: 13 Nov 2023 → 16 Nov 2023

Publication series

Name	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference	31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
Country/Territory	Germany
City	Hamburg
Period	13/11/23 → 16/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

geographic information system
location detection
spatial-temporal data mining

Access to Document

10.1145/3589132.3625589

Cite this

Ren, H., Ruan, S., Yuan, Y., Li, Y., Bao, J., He, T., He, H., Meng, C., & Zheng, Y. (2023). A Novel Approach for Company Real Workplace Identification via E-commercial Data. In M. L. Damiani, M. Renz, A. Eldawy, P. Kroger, & M. A. Nascimento (Eds.), 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023 Article 46 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). Association for Computing Machinery. https://doi.org/10.1145/3589132.3625589

Ren, Huimin ; Ruan, Sijie ; Yuan, Ye et al. / A Novel Approach for Company Real Workplace Identification via E-commercial Data. 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023. editor / Maria Luisa Damiani ; Matthias Renz ; Ahmed Eldawy ; Peer Kroger ; Mario A. Nascimento. Association for Computing Machinery, 2023. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

@inproceedings{c7621ede3a714ce7aa78d9caaf7d7f4f,

title = "A Novel Approach for Company Real Workplace Identification via E-commercial Data",

abstract = "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.",

keywords = "geographic information system, location detection, spatial-temporal data mining",

author = "Huimin Ren and Sijie Ruan and Ye Yuan and Yanhua Li and Jie Bao and Tianfu He and Huajun He and Chuishi Meng and Yu Zheng",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023 ; Conference date: 13-11-2023 Through 16-11-2023",

year = "2023",

month = nov,

day = "13",

doi = "10.1145/3589132.3625589",

language = "English",

series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",

publisher = "Association for Computing Machinery",

editor = "Damiani, \{Maria Luisa\} and Matthias Renz and Ahmed Eldawy and Peer Kroger and Nascimento, \{Mario A.\}",

booktitle = "31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023",

}

Ren, H, Ruan, S, Yuan, Y, Li, Y, Bao, J, He, T, He, H, Meng, C & Zheng, Y 2023, A Novel Approach for Company Real Workplace Identification via E-commercial Data. in ML Damiani, M Renz, A Eldawy, P Kroger & MA Nascimento (eds), 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023., 46, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Association for Computing Machinery, 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023, Hamburg, Germany, 13/11/23. https://doi.org/10.1145/3589132.3625589

A Novel Approach for Company Real Workplace Identification via E-commercial Data. / Ren, Huimin; Ruan, Sijie; Yuan, Ye et al.
31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023. ed. / Maria Luisa Damiani; Matthias Renz; Ahmed Eldawy; Peer Kroger; Mario A. Nascimento. Association for Computing Machinery, 2023. 46 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 - https://www.scopus.com/pages/publications/85182505993

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 -

Ren H, Ruan S, Yuan Y, Li Y, Bao J, He T et al. A Novel Approach for Company Real Workplace Identification via E-commercial Data. In Damiani ML, Renz M, Eldawy A, Kroger P, Nascimento MA, editors, 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023. Association for Computing Machinery. 2023. 46. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). doi: 10.1145/3589132.3625589

A Novel Approach for Company Real Workplace Identification via E-commercial Data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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