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Decoding the work-from-home phenomenon: insights from location-based service data

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  • Ka Shing Cheung
  • I.-Ting Chuang
  • Chung Yim Yiu

Abstract

The global pandemic has catalysed a shift in the job market, with remote work evolving from being an option to a widespread practice. This profound change goes beyond a temporary response to an extraordinary crisis; it could potentially mark the beginning of a new era in employment. In this featured graphic, we evaluate and visualise the work-from-home (WFH) trend in Auckland, the most populous metropolis in New Zealand. Applying a modified open-source machine learning algorithm on location-based service (LBS) data, we have created a visualisation to compare the individual work locations. The results reveal a significantly dispersed workplace distribution following the COVID-19 pandemic. Our visualisation, coupled with entropy analysis, provides prima facie evidence of the WFH trend. This finding holds implications for productivity and carries broader implications for the global workforce.

Suggested Citation

  • Ka Shing Cheung & I.-Ting Chuang & Chung Yim Yiu, 2023. "Decoding the work-from-home phenomenon: insights from location-based service data," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 10(1), pages 873-875, December.
    • Handle: RePEc:taf:rsrsxx:v:10:y:2023:i:1:p:873-875
    DOI: 10.1080/21681376.2023.2278577
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