Forecasting the rise and fall of volatile point-of-interests

Xinjiang Lu, Zhiwen Yu, Chuanren Liu, Yanchi Liu, Hui Xiong, Bin Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Volatile Point-of-Interests (vPOIs) refer to those small businesses which appear and disappear quickly in cities. How to maintain and incubate small business in the urban area is a big concern for both business owners and government administrators. Therefore, the prediction task for the rise and fall of vPOIs is valuable for both shopkeepers and administrators by supporting a variety of applications in urban economics. In this paper, we propose a framework, named FRFP, to predict the prosperity of vPOIs over time. Specifically, due to the data sparsity and skewness of the individual vPOIs, we first aggregate vPOIs prosperities at focal areas w.r.t. each vPOI category. Then we develop the dynamic-continuous CRF (DC-CRF) model to integrate the association between input and output as well as the correlations between outputs from temporal, spatial and contextual perspectives. Finally, we conduct empirical experiments on real-world data from Google Maps and NYC OpenData. The evaluation results demonstrate that our proposed approach outperforms baseline algorithms with considerable margins. In addition, we explore the predictability of different explanatory variables and provide actionable insights for both shopkeepers and urban planners.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1307-1312
Number of pages6
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Externally publishedYes
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
Country/TerritoryUnited States
CityBoston
Period12/11/1712/14/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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