Publication Type

Journal Article

Version

acceptedVersion

Publication Date

9-2020

Abstract

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data and social network check-in data, MFRs can model customer mobility patterns comprehensively at urban scale. CellTradeMap extracts robust location information from the irregularly sampled, noisy MFRs, adapts the generic trade area analysis framework to incorporate cellular data, and enhances the original trade area model with cellular-based features. We evaluate CellTradeMap on two large-scale cellular network datasets covering 3.5 million and 1.8 million mobile phone users in two metropolis in China, respectively. Experimental results show that the trade areas extracted by CellTradeMap are aligned with domain knowledge and CellTradeMap can model trade areas with a high predictive accuracy.

Keywords

cellular networks, crowdsensing, trade area analysis, human mobility

Discipline

OS and Networks | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ACM Transactions on Sensor Networks

Volume

16

Issue

4

First Page

1

Last Page

21

ISSN

1550-4859

Identifier

10.1145/3412372

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3412372

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