research-article

Predicting Commercial Activeness over Urban Big Data

Authors:

Jiulong ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 119, Pages 1 - 20

https://doi.org/10.1145/3130983

Published: 11 September 2017 Publication History

Abstract

This study aims at revealing how commercial hotness of urban commercial districts (UCDs) is shaped by social contexts of surrounding areas so as to render predictive business planning. We define social contexts for a given region as the number of visitors, the region functions, the population and buying power of local residents, the average price of services, and the rating scores of customers, which are computed from heterogeneous data including taxi GPS trajectories, point of interests, geographical data, and user-generated comments. Then, we apply sparse representation to discover the impactor factor of each variable of the social contexts in terms of predicting commercial activeness of UCDs under a linear predictive model. The experiments show that a linear correlation between social contexts and commercial activeness exists for Beijing and Shanghai based on an average prediction accuracy of 77.69% but the impact factors of social contexts vary from city to city, where the key factors are rich life services, diversity of restaurants, good shopping experience, large number of local residents with relatively high purchasing power, and convenient transportation. This study reveals the underlying mechanism of urban business ecosystems, and promise social context-aware business planning over heterogeneous urban big data.

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Cited By

Wang WYang SZhang W(2023)Customer Volume Prediction Using Fusion of Shared-private Dynamic Weighting over Multiple ModalitiesACM Transactions on Intelligent Systems and Technology10.1145/357982614:3(1-16)Online publication date: 24-Mar-2023
https://dl.acm.org/doi/10.1145/3579826
Wang ZChen SJi SPan ZMeng CZhang JLi TZheng Y(2023)Housing rental suggestion based on e-commerce dataKnowledge-Based Systems10.1016/j.knosys.2023.110474268(110474)Online publication date: May-2023
https://doi.org/10.1016/j.knosys.2023.110474
Zhou JHuang TLi SHu RLiu YFu YXiong H(2022)Competitive Relationship Prediction for Points of Interest: A Neural Graphlet Based ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.306323334:12(5681-5692)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TKDE.2021.3063233
Show More Cited By

Index Terms

Predicting Commercial Activeness over Urban Big Data
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

Issue’s Table of Contents

Copyright © 2017 ACM.

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

New York, NY, United States

Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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Cited By

Wang WYang SZhang W(2023)Customer Volume Prediction Using Fusion of Shared-private Dynamic Weighting over Multiple ModalitiesACM Transactions on Intelligent Systems and Technology10.1145/357982614:3(1-16)Online publication date: 24-Mar-2023
https://dl.acm.org/doi/10.1145/3579826
Wang ZChen SJi SPan ZMeng CZhang JLi TZheng Y(2023)Housing rental suggestion based on e-commerce dataKnowledge-Based Systems10.1016/j.knosys.2023.110474268(110474)Online publication date: May-2023
https://doi.org/10.1016/j.knosys.2023.110474
Zhou JHuang TLi SHu RLiu YFu YXiong H(2022)Competitive Relationship Prediction for Points of Interest: A Neural Graphlet Based ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.306323334:12(5681-5692)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TKDE.2021.3063233
Yannan ZLu ZXinhuan Z(2020)The spatial distribution of retail outlets in Urumqi: The application of points of interestOpen Geosciences10.1515/geo-2020-014912:1(1541-1556)Online publication date: 28-Nov-2020
https://doi.org/10.1515/geo-2020-0149
Tsubouchi KTeraoka TGomi HShimosaka M(2020)Parasitic Location Logging: Estimating Users’ Location from Context of Passersby2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom45495.2020.9127381(1-10)Online publication date: Mar-2020
https://doi.org/10.1109/PerCom45495.2020.9127381
Zhao YWang XLi JZhang DYang Z(2019)CellTransProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33512833:3(1-26)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1145/3351283
Li QYu ZGuo BXu HLu X(2019)Housing Demand Estimation Based on Express Delivery DataACM Transactions on Knowledge Discovery from Data10.1145/333252213:4(1-25)Online publication date: 26-Jul-2019
https://dl.acm.org/doi/10.1145/3332522
Zhao YZhou ZWang XLiu TLiu YYang Z(2019)CellTradeMap: Delineating Trade Areas for Urban Commercial Districts with Cellular NetworksIEEE INFOCOM 2019 - IEEE Conference on Computer Communications10.1109/INFOCOM.2019.8737564(937-945)Online publication date: Apr-2019
https://doi.org/10.1109/INFOCOM.2019.8737564
Guo BLiu YOuyang YZheng VZhang DYu Z(2019)Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A SurveyIEEE Access10.1109/ACCESS.2019.29010277(26606-26630)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2901027
He ZYang S(2018)Multi-view Commercial Hotness Prediction Using Context-aware Neural Network EnsembleProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32870462:4(1-19)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1145/3287046
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