Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters

Abstract

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

Detecting behavioural bias in GPS location data collected by mobile applications

“…There are further opportunities to use this information to inform statistical re-weighting of in-app mobility data, by increasing or suppressing the contributions of specific mobile applications through poststratification. Similar techniques are already used to reduce the effect of demographic bias in inapp mobility data (26,36,37). The challenge of applying post-stratification techniques, however, arises from the scarcity of representative information on the behavioural characteristics in a given population of interest.…”

“…This is a well-documented limitation of mobile phone location data including in-app data of the type used in this study (40). We took three steps to account for this bias in the dataset: (1) we based our clustering of travel modes around ‘days’ of travel, rather than individual devices by splitting visits crossing midnight (00:00 GMT) into distinct visits (ending 23:59:59 and beginning 00:00:01); (2) We filtered the location dataset for days of high quality location sampling, defined as days with at least 300 minutes of recorded activity, based on previous work using similar mobile-phone in-app location data (41). We performed a further sensitivity analysis to understand the effect that this threshold had on the size of the sample used in subsequent analysis (see Supplemental Figure 1.2); (3) We normalised measures of daily activity that depended on the sampling window of a particular device for comparison across devices with different sampling intervals.…”

“…The uneven geodemographic sampling in this dataset highlights a tradeoff between the strengths and limitations of in-app mobile phone location data. On one hand, these data contain a variety of documented limitations including uncertainty regarding the correspondence between individuals and devices, intermittent observation windows across individuals, data collection driven by individuals' interaction with a specific application, the potential for 'opt-in' bias for those willing to consent to location sharing, or biassed sampling caused by applications marketed towards specific consumer segments (34,35). On the other hand, this type of large scale mobility data is well suited to capturing observations in the 'tails' of the behavioural distribution, which would otherwise be costly or impossible to detect using traditional survey methods.…”

Crime, inequality and public health: a survey of emerging trends in urban data science

“…In general, Moro et al ( 2021 ) highlights the importance of considering mobility patterns when we aim at measuring income segregation. Yabe et al ( 2023 ) investigated how social interactions (e.g., encounters) changed during the COVID-19 pandemic with respect to income diversity. The authors relied on a dataset of millions of mobile phone users in multiple US cities for a period of three years before and during the pandemic.…”

Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion

“…[1][2][3][4][5]. All these measures have considerably impacted people's lives, social relationships, work and economy [6][7][8][9][10]. Indeed, if these measures can represent possible solutions to reduce disease spreading, the other side of the coin is that they imply a severe slowdown, or even interruption, of all the social exchanges and face-to-face interactions which prove fundamental for the proper functioning of a society and, specifically, for fruitful collaborative interactions.…”