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

Ka Shing Cheung^a, I.-Ting Chuang^b and Chung Yim Yiu^a
a. Department of Property
b. School of Architecture and Planning

https://doi.org/10.1080/21681376.2023.2278577

In this study, we applied the R ‘homelocator’ package (Chen & Poorthuis, 2021), a set of machine learning algorithms that use frequency and timestamps of activity to determine a home-like area. The workplace can be inferred using a slightly adjusted set of parameters, including the filtering of timestamps to focus on workdays and working hours rather than week- ends and off-work hours. Our analysis focused on the Auckland Region, approximately 1.7 million residents from 2019 to 2021, comparing pre-pandemic and post-pandemic periods. The visualisation of LBS data unravels a notable trend in home-work patterns: a marked shift towards a more dispersed arrangement of workplaces in 2021 compared to 2019. Remark- ably, 54.5% of individuals altered their work locations, transitioning from the central business district (CBD) to the city fringe, while 23.2% shifted away from the industrial centre of Papatoetoe to other areas. An entropy analysis provides additional insights to quantify the extent of such transitioning, with higher entropy (E) signifying a greater workplace dispersion. The entropy comparison revealed a significant difference (E2019: 7.20, E2021: 7.77; p-value = 1.90e-06), suggesting that workplace dispersion was higher in 2021. The transition to remote work, as depicted by the LBS data, could herald a new chapter in our understanding of work – a trend likely to endure in the post-pandemic world. Its lasting impact or temporary nature war- rants further observation. Businesses navigating this change must adapt strategies to ensure a productive and satisfied.

Figure 1. Spatial patterns of the work-from-home trend in Auckland, New Zealand. Note: The visu- alisation of LBS (location-based services) data on an Auckland region map (left-hand side) showcases a remarkable shift in workplace locations from 2019 to 2021. Approximately 54.5% of individuals changed their work locations, moving from the central business district (CBD) to the city fringe, while 23.2% shifted away from the industrial centre of Papatoetoe to other areas, as indicated by the arrows in the graph. The arrows depict the direction of workplace relocations, and their thickness represents the intensity of such relocations. The thickest arrow corresponds to the upper quartile (Q4), indicating the highest intensity of relocations, while the arrow for the lower quartile (Q1) rep- resents the below 25th percentile. Each 300-metre hexagonal grid on an Auckland region map (right- hand side) is coloured with a deeper purple hue to represent a higher density of work locations. An entropy analysis (right-hand side vertical bar) provides further insights by quantifying the extent of this transitioning, with higher entropy (E) signifying a greater dispersion of workplaces. The entropy comparison revealed a significant difference (E2019: 7.20, E2021: 7.77; p-value = 1.90e-06), suggesting that the workplace locations were much more dispersed in 2021.