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

Using a large and longitudinal dataset of GPS location records in four major metropolitan areas in the US across more than three years, we analyze how experienced income diversity of urban encounters has changed during different periods of the COVID-19 pandemic. Specifically, we analyze the dynamics of income diversity of encounters at the level of individual places (points-of-interest; POIs) and individual users in cities. We seek to identify behavioral changes that were the cause of such long-term changes, and we further unravel the sociodemographic, economic, and behavioral characteristics that explain the spatial heterogeneity in decreased diversity. Mobility data was provided by Spectus, who supplied anonymized, privacy-enhanced, and high-resolution mobile location pings for more than 1 million devices across four U.S. census core-based statistical areas (CBSAs) (Supplementary Table S2). All devices within the study opted-in to anonymized data collection for research purposes under a GDPR and CCPA compliant framework. Our second data source is a collection of 433K verified places across four CBSAs, obtained via the Foursquare API. The robustness of the results on experienced income diversity against the choice of places dataset was checked using the ReferenceUSA Business Historical Data⁴² (Supplementary Note 1). Post-stratification techniques were implemented to ensure the representativeness of the data across regions and income levels (Supplementary Note 2).

To analyze the income diversity of urban encounters, each anonymized individual user in the dataset was assigned a socioeconomic status (SES) proxy, estimated from their home census block group (CBG) using the 2016-2020 5-year American Community Survey (ACS) (Supplementary Note 1). The approximate home area of each individual user was estimated by Spectus at the granularity of CBGs using their most common location during the nighttime, between 10 p.m. and 6 a.m. every week. Individuals were then categorized into four equally sized SES quantiles according to the median household income of their home CBG. The results on decreased experienced income diversity were robust against the number of income quantile categories used (Supplementary Note 1). Only users who were observed more than 300 minutes each day were used for the analysis to remove users with substantial missing data. Stays (stops) longer than 10 minutes and shorter than 4 hours were then extracted from the dataset, and each stay was spatially matched with the closest place locations within 100 meters to infer stays at specific POIs. The results on experienced income diversity were robust against the choice of data filtering parameters and spatial threshold parameters for visit attribution (Supplementary Note 1).

Given the estimated SES quantiles of individual users and the visited POIs, we measured the experienced income diversity at each place α (denoted as D_α) and experienced by each individual i (denoted as D_i). D_α measures the evenness of the time spent by people from different income quantiles at each place, and D_i measures the evenness of time spent with people from different income quantiles for each individual (see Methods and Supplementary Notes 3.1 and 3.2). For places, D_α = 1 when the place is fully diverse, with 25% of time spent by people from each of the four income quantiles, and D_α = 0 when the place is visited by members of only a single income quantile. Similarly, to calculate the diversity of individuals D_i, we measure the exposure of the individual i to each income quantile q across all the places α the individual has visited. The robustness of the results to the choice of diversity metric was tested (Supplementary Note 3). The diversity measures were computed for each 2-month moving window to ensure a sufficient number of visits to POIs, and were deseasonalized using monthly trends observed in 2019. The panels in Fig. 1a show how experienced income diversity at places around the Boston and Cambridge area substantially decreased during the first wave of the pandemic. The diversity of encounters gradually recovers, however, not fully even after more than 1 and a half years from the lockdown, in October 2021. Similar patterns can be observed in all three other cities in the study. The maps highlight the significant spatial heterogeneity of experienced income diversity (e.g., Back Bay area is more diverse compared to the suburban areas), which is further investigated in the later sections.

**Fig. 1: Diversity of urban encounters has decreased during COVID-19.**

Diversity of urban encounters has decreased during the pandemic

The pandemic substantially changed people’s mobility patterns in the early waves, as documented in previous studies using mobility data (e.g.,⁴³). However, several individual mobility metrics indicate that individual-based mobility patterns have returned back to pre-pandemic levels by late 2021. Figure 1b shows monthly average values of several individual mobility metrics across the two years of 2020 and 2021. Mobility metrics, more specifically the daily number of visits per individual, daily amount of time spent at POIs per individual, average dwell time spent per visit, and number of visited unique POIs per individual, have all returned back to pre-pandemic levels (annotated by horizontal dashed lines) by late 2021 in all four CBSAs. The drop in the rate of visits to POIs as well as the duration of visits to POIs during the earlier stages of the pandemic agree with the findings in previous studies⁴⁴, however, our analysis extends the analysis to two years into the pandemic and confirms how activity patterns have recovered back to pre-pandemic levels by October 2021. The mobility data confirms that people have resumed spending time outside their homes and visiting different POIs, similar to before the pandemic.

Given the recovery of aggregate mobility metrics, one could expect the income diversity of encounters to also return back to pre-pandemic levels by late 2021. However, as shown in Fig. 1c, the income diversity experienced at places and by individuals is consistently lower than the pre-pandemic levels for all four cities even after 2 years into the pandemic. Absolute values of D_α and D_i are shown in Supplementary Fig. S15. Cities experience the most decrease in diversity in April 2020, 30% lower than pre-pandemic levels during the lockdown. A second peak in the loss of diversity is observed in late 2020, which corresponds to the increase in cases due to the first SARS-CoV-2 variant. Despite the recovery of individual mobility metrics as shown in Fig. 1b, income diversity of encounters is still around 10% less than pre-pandemic levels even by late 2021. D_i is a convoluted version of the D_α for all places α visited by i, which considers the ratio of the sum of stay duration across all income quantiles. Thus, although each place α could significantly lose income diversity during the pandemic due to a decrease in visits, individuals are likely to visit multiple places in a given month, which results in a higher D_i than D_α. The decrease in experienced income diversity was robust to the choice of diversity metrics, such as the entropy of income quantiles for encounters at places and for individuals (Supplementary Note 3.3).

Dissecting the place-based diversity results into POI categories, we further observe that diversity in places in Boston decreased in all POI categories both in the short-term (e.g., April 2020) and long-term (e.g., October 2021) in Fig. 1d. Especially, ‘Museums’, ‘Leisure’, ‘Transportation’, and ‘Coffee’ places had the largest decrease in diversity, while ‘Grocery’ places consistently experienced the least effects of the pandemic. This is consistent with the fact that we observe the number of visits to follow similar patterns, where we see a decrease during the early stages of the pandemic and gradual recovery by late 2021 for all POI categories, except grocery stores, which experienced no reduction in the number of visits even during the first waves. This suggests that the reduction in the number of visits indeed is one of the factors that cause the decrease in the diversity of encounters. In the following section, we employ a counterfactual analysis approach to further understand why the diversity of encounters has consistently decreased during the pandemic.

Behavioral changes decreased experienced income diversity in cities

To investigate the behavioral factors that led to the consistent decrease in income diversity experienced at places and by individuals, we consider three possible hierarchical levels of changes in the behavior of individuals due to the pandemic. As illustrated in 2A, the pandemic led, especially during the beginning of the pandemic, to a (i) reduction in the total amount of time spent at places outside homes and workplaces. Moreover, due to stay-at-home orders and also reluctance towards long-distance trips compared to before, we also consider (ii) changes in travel distances for each income quantile. Since some types of activity categories were particularly affected by social-distancing policies, we also consider changes in visits to major activity categories and traveled distances for each income quantile, shown in Supplementary Note 4. Finally, we also consider the possibility of (iii) microscopic changes in place preferences, including changes in exploration behavior and visitation patterns across place subcategories.

To disentangle the relative impacts of these behavioral changes, we created different counterfactual mobility datasets. For example, to estimate the effects of the reduction of total activity time on the loss of diversity, we randomly removed visits from pre-pandemic data (e.g., 2019 April) to create a counterfactual mobility dataset that contains the same total visit duration at places during the pandemic (e.g., 2020 April) (see Methods and Supplementary Note 4). The resulting generated counterfactual data can answer the question of ‘how would the income diversity change if people just simply decreased the number of visits to places from pre-pandemic (2019) levels?’. By comparing the place and individual-based diversity measures computed from the actual and the counterfactual mobility datasets, we are able to delineate the effects of activity reduction on the decrease in diversity. Similarly to measure the effects of (ii) changes in traveled distances by income quantiles, we extended the previous counterfactual to have the same total visit duration by distance ranges for each income quantile (see Methods and Supplementary Note 4). Simulations were run 10 times each to ensure robust results.

Figure 2b shows the decreased diversity experienced at places and by individuals decomposed into the three behavioral factors (full results shown in Supplementary Fig. S23). The counterfactual simulations show that (i) reduction in total activities caused around 50% of the decrease in diversity during the first pandemic wave, however, decreases to almost 2% by late 2021 when mobility metrics have recovered back to normal, as shown in Fig. 1b. Although we observe different rates of dwell time decrease and recovery across income quantiles, where the higher income populations disproportionately reduce dwell times at places than lower income populations, the overall diversity measures are not affected since the relative mixing of population groups across income groups is consistent (Supplementary Note 4.2). Changes in distance distributions, where people prefer trips to closer places during the pandemic, have slight negative effects on the income diversity of encounters. Surprisingly, changes in dwell time duration at major activity categories had no effects on the experienced income diversity metrics (Supplementary Note 4.2).

**Fig. 2: Behavioral changes decreased experienced income diversity in cities.**

Heterogeneity in activity reduction rates across income quantiles and changes in traveled distances explain around 55% of the decreased diversity during the first wave of the pandemic, however, the remaining 45% is due to more microscopic, place-based preference changes. These effects become the single dominant factor in the later stages of the pandemic. To identify the changes in the mobility behavior during the pandemic, we fit the social exploration and preferential return (Social-EPR) model^19,45 to the data for each period and assess the model parameters (see Supplementary Note 4.3). Among the parameters of the social-EPR model, the parameter which changed the most between before and during the pandemic was the social exploration parameter σ_s, as shown in Fig. 2c and Supplementary Fig. S26. Social exploration σ_s measures the probability of an individual visiting a place where their income group is not the majority income quantile group when they decide to explore a new place. During the pandemic, people’s willingness to socially explore substantially decreased compared to the 2019 levels (horizontal dashed line) in all four cities, leading to less experienced diversity.

Furthermore, we observe changes in place level preferences across POI subcategories. Sub-category popularity f_r is measured by computing the probability that a POI sub-category is included in an individual’s top r most frequently visited places. Figure 2d and Supplementary Fig. S27 show the POI subcategories which were more (and less) visited in different periods during the pandemic compared to 2019 levels. Hardware stores, big box stores, and grocery stores (in October 2020 and 2021) were POI subcategories that gained popularity during the pandemic, and gyms, movie theaters, and American food places were subcategories that were less visited frequently. Taken together with the results that controlling by major activity categories did not explain additional decreased diversity to scenario (ii) as shown in Supplementary Note 4.2, this result shows that people have not changed their proportion of time spent for major activity categories, but have changed which specific types of places they visit within each major activity (e.g., less time at American restaurants, but more time at fast food and donut stores). To summarize, not only a reduction in activity, but also microscopic behavioral changes especially during the later stages of the pandemic, including less exploration and shift in preferences, led to decreased diversity in urban encounters.

Spatial and socioeconomic heterogeneity in decreased diversity

Which sociodemographic groups and areas were more affected by the decrease in experienced income diversity? To understand the heterogeneity in decreased diversity, the mean CBG-level experienced income diversity of all individuals living in the CBG were computed for each CBG in the four CBSAs, thus (D_CBG=frac1sum _iin N_CBGD_i), where N_CBG denotes the set of individuals living in the corresponding CBG. By visualizing (Delta D_CBG=100%times (D_CBG-D_CBG^2019)/D_CBG^2019) in the Boston-Cambridge-Newton CBSA in Fig. 3a (and other CBSAs in Supplementary Fig. S28), we observe spatial heterogeneity in the changes in diversity in the early stages of the pandemic, however, more homogeneity in the long term. The insets also show the magnitude of ΔD_CBG decreasing as cities recover from the pandemic. The correlation between D_CBG in April 2020 and D_CBG in April 2019 is much smaller (R² = 0.37) than for October 2021 and October 2019 (R² = 0.71), indicating a larger heterogeneity in ΔD_CBG during the earlier stages of the pandemic (Supplementary Fig. S29).

To understand the spatial and sociodemographic heterogeneity in the decreased diversity of encounters during the pandemic compared to 2019, we model D_CBG and its difference ΔD_CBG, using a simple regression model (see Methods and Supplementary Note 5). We include variables describing the places visited by the residents in the CBG (in 2019), mobility metrics including the average total traveled distance and radius of gyration (in 2019), and sociodemographic and economic characteristics of the CBG, including its population density, median income, age and race composition, and transportation behavior (e.g., public transportation usage), all of which were standardized (Supplementary Table S3). Regression analysis was conducted for each month, including all four cities. To control for the difference between areas across and within the metropolitan areas, we include geographical fixed effects at the level of Public Use Microdata Areas (PUMAs), which typically span around 20km and contain a residential population of 150 thousand people. Detailed summary statistics, collinearity and correlations between variables, variance inflation factor analysis, and full regression results can be found in Supplementary Note 5.

Figure 3b shows the adjusted R² of regression models for D_CBG and ΔD_CBG, respectively, across different periods. The three groups of variables (places visited, geographical mobility, and residence and demographics) explain around 60% to 70% of the variance of experienced income diversity (D_CBG), which agrees with previous findings¹⁹ (Supplementary Tables S4–S6). However, the difference in diversity from 2019 levels (ΔD_CBG) has lower explained variance (at most R² = 0.31), and also decreases where there is no pandemic outbreak. In the long-term (October 2021), the regression model has low explained variance (R² = 0.11), indicating that regions homogeneously became less diverse, irrespective of sociodemographic or behavioral characteristics of the areas. Figure 3c shows the factors that were most important in explaining the variance of ΔD_CBG in the months where R² was relatively high (April, May, December 2020 and January 2021) (Supplementary Tables S7–S10). The highlighted regression coefficients suggest that whenever there is an outbreak, areas with a higher population density and higher proportion of working-age populations (age 25–64), higher reliance on public transport, and larger movement range (radius of gyration) experience the largest decrease in income diversity of encounters.

Trade-off between income diversity of encounters and stringency of policy measures

From a public policy perspective, an important and interesting question is to understand how COVID-19 containment measures, including lockdowns, school and workplace closures, and restrictions on public gatherings, have affected resulted in the loss of diversity in urban encounters. To measure the relationship between the stringency of COVID-19 measures and experienced income diversity, we utilize the COVID-19 Stringency Index⁴⁶ (Supplementary Fig. S37), which is a composite measure of nine response metrics, including school and workplace closures, restrictions and cancellation of public events and gatherings, and restrictions on movement and travel (See Supplementary Note 6).

Figure 4 shows the relationship between the stringency of COVID-19 policies and the decrease in the diversity of urban encounters. In all four cities we observe statistically significant (p < 0.01) and strong negative correlation (ρ(SI_CBSA, ΔD_CBSA) ∈ [−0.9,−0.73]). The robust negative correlations suggest a strong trade-off relationship between experienced income diversity and COVID-19 policy and outbreak intensity in all cities. The decrease in diversity become pronounced during COVID-19 outbreaks, especially during the first pandemic wave (red plots) in Boston and Seattle, and during the second pandemic wave (orange plots) in Los Angeles, where the number of cases and deaths were substantial in the respective cities. Moreover, for Boston, Seattle, and Los Angeles, even though the Stringency Index has decreased to around 20 in late 2021 (which indicates already less strict policies in place), the decrease in experienced income diversity is positive, suggesting that the COVID-19 pandemic may have had a long-lasting decreasing effect on the income diversity of urban encounters. Regression results using additional exogenous variables such as the number of COVID-19 cases and deaths on the federal and local (CBSA) levels are shown in Supplementary Note 6.1 and Supplementary Fig. S38. Since ΔD_CBSA(t) is temporal data with autocorrelation, we tested ARIMA type models as well. For Boston and Seattle, the moving average component was significant, however, the estimated coefficients of the stringency index were found to be robust. The temporal effects for Los Angeles and Dallas were insignificant (see Supplementary Note 6.2).

**Fig. 4: Trade-off between decreased income diversity of encounters and stringency of COVID-19 policies.**

Published on The Perfect Enemy at https://bit.ly/40AJNUe.

Julio Marchi © Speaks Out Network

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