Validating the Use of Wi-Fi Signals to Estimate Hyperlocal Urban Populations

Nicholas E. Johnson, Pablo Mandiola, Cyrus Blankinship, Bartosz Bonczak, Constantine E. Kontokosta

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

Abstract

The widespread adoption and use of mobile phone and Wi-Fi enabled devices has created new sources of big data useful for understanding urban mobility and real-time population activity. Wi-Fi networks in particular are being increasingly deployed throughout dense urban areas providing new possibilities to study the dynamics of urban populations. However, there is little known about the ability of Wi-Fi networks to quantify urban populations in real-time, including the accuracy, coverage and capture rate of these networks. In this research, we compare three methods for generating population estimates using Wi-Fi probe request data and validate these approaches with manual count data for a two month period. Despite concerns related to data bias and representativeness, we find that Wi-Fi data can be a viable source of information for estimating urban populations.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1650-1655
Number of pages6
ISBN (Electronic)9781728108582
DOIs
StatePublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
Country/TerritoryUnited States
CityLos Angeles
Period12/9/1912/12/19

Keywords

  • Wi-Fi signals
  • population modeling
  • urban big data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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