research-article

Mining heterogeneous urban data for retail store placement

Authors:

Jian Zeng,

Bo TangAuthors Info & Claims

ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China

Article No.: 53, Pages 1 - 5

https://doi.org/10.1145/3321408.3322834

Published: 17 May 2019 Publication History

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Abstract

Retail store placement problem has been extensively studied in both academic and industry as it decides the retail success of a business. Existing methods exploited either consumer studies (e.g., consultant-based solutions) or geographic features (e.g., points of interests) to settle it. However, due to the limitations of data sources (i.e., costly in time and labor), none of these methods could provide an accurate and timely solution.

In this paper, we rethink retail store placement problem by mining heterogeneous urban data. In particular, unlike existing works which only used geographic features or consumer studies solely, we extract three categories of features (i.e., human movement features, commercial area features and geographic features) from heterogeneous urban data, and integrate them into various machine learning models to predict the popularity of a prospective retail store in the candidate area. We conduct a case study with real data in Shenzhen to demonstrate the predictive power of our proposal.

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Cited By

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Han SJia XChen XGupta SKumar ALin Z(2022)Search well and be wise: A machine learning approach to search for a profitable locationJournal of Business Research10.1016/j.jbusres.2022.01.049144(416-427)Online publication date: May-2022
https://doi.org/10.1016/j.jbusres.2022.01.049
You JMuhammad AHe XXie TWang ZFan XYu ZChen LWang C(2022)PANDA: predicting road risks after natural disasters leveraging heterogeneous urban dataCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-022-00095-54:4(393-407)Online publication date: 7-Mar-2022
https://doi.org/10.1007/s42486-022-00095-5
Liu YGuo BZhang DZeghlache DChen JZhang SZhou DShi XYu Z(2021)MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge TransferACM Transactions on Intelligent Systems and Technology10.1145/344727112:3(1-23)Online publication date: 21-Apr-2021
https://dl.acm.org/doi/10.1145/3447271
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ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China

May 2019

963 pages

ISBN:9781450371582

DOI:10.1145/3321408

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 17 May 2019

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Guangdong Natural Science Foundation
the National Science Foundation of China
the Science and Technology Innovation Committee of Shenzhen

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ACM TURC 2019

ACM TURC 2019: ACM Turing Celebration Conference - China

May 17 - 19, 2019

Chengdu, China

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Han SJia XChen XGupta SKumar ALin Z(2022)Search well and be wise: A machine learning approach to search for a profitable locationJournal of Business Research10.1016/j.jbusres.2022.01.049144(416-427)Online publication date: May-2022
https://doi.org/10.1016/j.jbusres.2022.01.049
You JMuhammad AHe XXie TWang ZFan XYu ZChen LWang C(2022)PANDA: predicting road risks after natural disasters leveraging heterogeneous urban dataCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-022-00095-54:4(393-407)Online publication date: 7-Mar-2022
https://doi.org/10.1007/s42486-022-00095-5
Liu YGuo BZhang DZeghlache DChen JZhang SZhou DShi XYu Z(2021)MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge TransferACM Transactions on Intelligent Systems and Technology10.1145/344727112:3(1-23)Online publication date: 21-Apr-2021
https://dl.acm.org/doi/10.1145/3447271
Liu YGuo BZhang DZeghlache DChen JHu KZhang SZhou DYu Z(2021)Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site RecommendationACM Transactions on Knowledge Discovery from Data10.1145/344220315:3(1-27)Online publication date: 21-Apr-2021
https://dl.acm.org/doi/10.1145/3442203

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Omnichannel Retail Operations with Buy-Online-and-Pick-up-in-Store

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