Green space accessibility helps buffer declined mental health during the COVID-19 pandemic

doi:10.21203/rs.3.rs-1725197/v1

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Green space accessibility helps buffer declined mental health during the COVID-19 pandemic

https://doi.org/10.21203/rs.3.rs-1725197/v1

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published 17 Feb, 2023

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Accumulating evidence has highlighted a negative effect of COVID-19 pandemic on public mental health. By using data from 2 million mobile phone users within London over the period of January 2019–December 2020, as well as the multi-wave data from the UK Household Longitudinal Survey (UKHLS), we examined green space accessibility as a potential remedy for such an effect. We found that, after the COVID-19 outbreak and during lockdowns, individuals who live close to the green spaces (i.e., within 800 meters from the nearest green space) were more likely to pay visits to those spaces, and these individuals also experienced a much less mental distress than those who live farther away from green spaces. Our findings provide suggestive evidence that accessibility to green spaces is important to the population’s mental health, especially when their mobility is restricted. Enhancing green space accessibility for more residential neighbourhoods should thus be important to city planners and policymakers as they help citizens become more resilient to a similar future pandemic.

Since the first known cases in late December 2019, the COVID-19 pandemic, as a stressful event, has posed unprecedented challenges for everyone worldwide. Due to the increased risk factors for public health, many countries have subsequently adopted policies to reduce mobility such as lockdowns and safe-distancing measures. Although these policies are known to be effective both to alleviate the burden on intensive care units and to reduce the number of new COVID-19 infections,^1,2 recent research has identified them as a cause of many negative outcomes. For example, COVID-19 lockdowns pushed many individuals to experience high levels of adversities arising from social isolation, inactivity, decreased family and social support, and financial hardship. Besides the negative economic and social consequences of population mobility restrictions due to COVID-19,³ research also highlights the heightened issues of public mental health.^4,5

Evidence produced from representative cohort studies provided us with a clear comparison of individuals’ pre- and in-pandemic psychological state, which showed a heightened psychological distress and a rise in the proportion of people experiencing significant levels of mental illness.^5–8 These findings are not completely surprising, considering similar findings in past epidemic events. For example, individuals experienced symptoms of depression and post-traumatic stress disorder during the quarantine order for the epidemic of severe acute respiratory syndrome (SARS).^9–14 Nevertheless, given the dilemma between the policy needs of mobility restrictions and their negative outcomes, we urgently need to identify potential tools to mitigate such outcomes and maintain the level of public (mental) health even during lockdowns.

Based on the review of 3,166 papers, scholars have pointed out a few solutions and highlighted that we should keep quarantine orders as short as possible while providing quarantined individuals with more information and support (e.g., reducing the boredom with improved communication) and encouraging people participate in voluntary precaution and mobilityrestriction.⁷ Although the investigation of these quarantine orders is important, lockdown policies (i.e., stay-at-home orders) and compulsory social-distancing measures pose a substantial threat to a much larger number of people as their daily activities are fully or partially restricted. Up until now, however, we know relatively little about how to successfully mitigate the detrimental effect of these measures.

To accomplish this goal, we explore research in environmental psychology, particularly on the effect of green space exposure on public mental health. Exposures to green spaces have been widely recognized to promote mental health and well-being for a variety of populations in different circumestances.^15–17 Existing evidence suggests the negative associations between green space exposure and psychological distress.^18–21 Nonetheless, it is unknown whether such beneficial effects of green spaces can be observed after COVID-19 and during lockdowns when the mobility of the general public is restricted on a compulsory or voluntary basis. Similarly, we do not know much about changes in public mobility to green spaces after COVID-19 and during lockdowns. A better understanding of these changes in public mobility is directly related to the evaluation of how previously identified beneficial effects of green spaces on mental health can be translated into those in lockdowns. Findings of these questions are expected to provide crucial information for policymakers and local government, as well as institutions in the domain of public health.

Meanwhile, research showed that living closer to green spaces helps support mental and general health and also prevents depression in young adults.¹⁶ COVID-19 lockdowns highlighted the need for outdoor walks to the nearby parks. In fact, Google searches for “go for a walk” significantly increased right after lockdown orders in many countries.²² However, the surge in interest in going for short walks is not always matched with the supply of green spaces (e.g., parks and gardens) especially in high-density urban areas.^23,24 This highlights another important question: Is the mental health of some individuals more adversely affected by the COVID-19 pandemic and lockdowns than others due to unequal accessibility to green spaces? In fact, recent BMJ commentary highlighted the importance of equal access to green spaces during the COVID-19 pandemic, and call for more academic and political attentions to this issue.²⁵ Previous research focused on examining how the characteristics of individuals—such as gender, age, educational attainment and socioeconomic position, racial and ethnic backgrounds, and living conditions (e.g., living alone)—affected their mental health during periods of isolation during previous epidemics⁷ and the COVID-19 pandemic.^26–28 Although these are important factors to consider, our research highlights accessibility to green spaces as another crucial feature, which has important implications for not only the mental health services but also urban planners and policymakers. We choose to examine this question in London, UK, as it provides us with a great opportunity to carry out large-scale, quasi-experimental analyses by uniquely utilizing mobile phone–based mobility data and multi-wave surveys during and after lockdown periods.

2.1. Data and participants.

Our data come from several sources. The first is a dataset on weekly bilateral flow information of 2 million mobile phone users from their residence (origin) to destination locations within London over the period of January 2019–December 2020. We were granted access to mobility data from Telefónica that do not include any personally identifiable information. The data consist of the number of trips and travel frequency among antenna points within London as well as the time spent in destination location, where they stayed more than 1 hour to exclude temporary movements through car and/or public transportation. The data include activity information for both work hours (08:00–16:00 on working days) and non-work hours (16:00–24:00 on working days and 08:00–24:00 on weekends and bank holidays). To perform the analysis with resident characteristics at the temporally stable geographic level, we perform spatial interpolation from the origin-destination flows between antenna points to flows between the Lower layer Super Output Areas (LSOAs), which are the smallest geographic units available for the major UK administrative data. An LSOA, on average, contains about 15,000 residents or 650 households. A more detailed explanation of the mobility data processing is presented in Appendix 1.

Second, we mark the location information of a broad range of planned open spaces including registered public parks, cemeteries, and town squares in the London provided by Historic England in August 2021. The data contain 1,699 entries in England as of August 2021. By identifying whether the destination locations of mobility flows belong to these green spaces, we obtain the ratio of the number of green space travellers relative to the number of total travellers from each residential LSOA in a given week. We use this ratio during non-work hours for our main analyses to prevent potential underestimation of short-term travellers before COVID-19 (i.e., frequent travels within 1 hour) and overestimation of frequent travellers during COVID-19 and lockdown periods (e.g., delivery services). Results using the frequency measure is consistent with those using the current measure. To account for demographic and socioeconomic characteristics at the LSOA level, we use the LSOA-level data on population characteristics for the years of 2019 and 2020, provided by the Office for National Statistics (ONS), as well as the 2020 data on ethnic composition from the Consumer Data Research Centre (CDRC), which was obtained with a special permission. We also use the 2019 English indices of deprivation for the 4,835 LSOAs in London provided by the Ministry of Housing, Communities and Local Government. The data include diverse neighbourhood indicators on income, employment, education, health, crime, barriers to housing affordability and local services, living environment, income deprivation affecting children and older people.

Our final dataset is generated from the UK Household Longitudinal Survey (UKHLS) in 2020 and 2021, provided by Understanding Society, to assess individuals’ psychological distress levels. The data contain various information of respondents such as age, gender, race, family composition, income, and physical health conditions across 8 different waves of COVID-19. After cleaning up the data with no or missing responses, we have unique respondents of 20,468. The basic nature of this survey is a longitudinal study that follows the same sample of people over time although each wave adds a small number of people into the sample with cross-sectional sampling weights. For our main analyses, we use the panel sample with longitudinal sampling weights. We also obtain the residence LSOA information of respondents through a special permission, which enables us to measure the distance of one’s residence to the nearest parks and gardens to proximate their probability to travel to green spaces. Full details on the recruitment, sampling, retention, and weighting of the sample are available in the UKHLS user guide (see Appendix 2).

2.2. Procedures

Figure 1A shows how lockdown periods overlap with our other datasets of cell phone–based mobility in London as well as the UKHLS. During our sample period, the UK government imposed three national lockdowns that placed restrictions on movement; no person was allowed to leave the place where they live without a reasonable excuse. Lockdown laws in the UK encompass restrictions on movement and gatherings, as well as the closure of and restrictions on businesses. As restrictions on movement are the strongest regulation that affects travel behaviours, we focus on these phases for our research and call them lockdowns.²⁹ During these lockdowns, non-essential street businesses (e.g., cafes, restaurants, bars and pubs) were closed and people were asked to stay at home as much as possible and were strictly banned from gathering. People were, however, still allowed to do outdoor exercises either alone or with other household members or seek medical assistance.

For our statistical model of trips to green spaces, we examine the threshold distance to green spaces that determines the significant changes in travel patterns after the COVID-19 pandemic (vs. before) and during the lockdown (vs. non-lockdown). We utilize straight-line distances between the centroid of green spaces and residence LSOAs using a Geographic Information System (GIS). Figure 1B presents the gradient of the proportion of the number of park travellers out of all travellers by the distance to the nearest park. This suggests that the increases in this proportion after COVID-19 exist in LSOAs that are located only up to an 800-meter radius to the nearest park. We generate a binary variable that captures the proximity to the nearest park (i.e., whether the nearest park is located within 800 meters from the residential LSOA). The 800-meter cutoff coincides with a walkable distance.

Furthermore, to minimize potential confounding issues and provide a causal interpretation for our model of the role of proximity to green space to mental distress, we adopt a standard logic of a counterfactual causal inference design. More specifically, we identify green space proximity using the distance limit of 800 meters to the nearest park. Our potential treatment group is comprised of all UKHLS survey respondents residing in LSOAs within 800 meters from the nearest park, and those who reside in LSOAs that are farther belong to the potential comparison group. Among the pool of 13,296 survey respondents in the comparison group, we select the closest match for each individual residing within 800 meters to the nearest park by using the propensity score matching (PSM) procedures. Our final sample size after matching is 2,496 individuals for the treatment group and 2,496 for the comparison group, both of which are highly homogeneous with respect to age, race, family composition, earnings, and drinking/smoking habits (see Appendix 2 for the detailed matching process and the quality of the matched sample).

Finally, we pay attention to the UKHLS data where psychological distress was measured using 12 questions from the General Health Questionnaire (GHQ), a well-validated tool used to screen and diagnose generalized anxiety disorder in clinical practice and research.³⁰ It uses a 4-point scale, where higher point values indicate a more deteriorated condition. We convert this into the measure with the total 12-point scores (asymptomatic [score 0], sub-clinically symptomatic [1–3], symptomatic [4–6], and highly symptomatic [7–12]).³⁰

2.3. Empirical Methodology

First, we adopt a difference-in-difference (DID) analytical approach using variations in the distance from green spaces and in pre-post periods of the COVID-19 outbreak or lockdown event. We employ the DID model as follows:

\({V}_{jt}=\beta {D}_{jt}^{800}+\theta {D}_{jt}^{800}\times {Post}_{jt}+{\delta Post}_{jt }{+ X}_{j}^{{\prime }}\gamma +{\phi }_{m}+ {\alpha }_{l}+{\epsilon }_{jt}\) . [1]

where V_jt is the proportion of the number of travellers to green spaces out of all travellers in LSAO j in week t, \({D}_{jt}^{800}\) is a binary indicator of whether there is a park within an 800-meter radius of LSAO j, and \({Post}_{jt}\)is a binary indicator that trip behaviours in week t occurred after the COVID-19 outbreak or during lockdown periods.\(\theta\)picks up how the proportion of park travellers in LSOAs located within 800 meters from the nearest park changes after COVID-19 or during lockdown periods compared to the pre-pandemic or non-lockdown periods. X is a control vector of LSOA-specific characteristics such as population size, gender (men vs. women), racial groups (White, Asian, Black, others), age groups (under 18 years,18–29, 30–45, 46–59, 60–89, over 89), as well as a wide range of neighbourhood indices related to income, employment, education, health disability, crime, barriers to housing and services, and living environment. We include month fixed effects and local authority fixed effects to control for unobserved heterogeneity.\({\epsilon }_{jt}\)is an error term. To account for the potential serial correlation of residuals within an LSOA, we cluster standard errors at the LSOA level.

Next, we attempt to compare the level of mental distress between individuals residing in LSOAs that are within 800 meters of the nearest green spaces and those residing in farther LSOAs but within the same city. We employ a DID model similar to equation [1] as follows:

\({M}_{iw}=\beta {D}_{iw}^{800}+\theta {D}_{iw}^{800}\times {LD}_{iw}+{\delta LD}_{iw }{+ X}_{i}^{\text{'}}\gamma +{\phi }_{w}+ {\alpha }_{c}+{\epsilon }_{iw}\) . [2]

where M_iw is the mental distress score of individual respondent i from the matched sample in wave w, \({D}_{iw}^{800}\) is a binary indicator of whether there is a park within an 800-meter radius from the residence of respondent i, and \({LD}_{iw}\) is a binary indicator that a mental health survey in wave w is conducted during lockdown periods. \(\theta\) picks up how the mental health score changes for those reside within 800 meters from the nearest park changes during lockdown periods. X is a control vector of demographic and economic variables such as age, gender (men vs. women), racial groups (White, Black, other), number of household members, number of children, whether living with a partner, household/individual monthly earning, and whether having financial difficulties as well as health status such as whether having long-term health issues, whether infected with COVID-19, number of days doing moderate activities, whether drinking moderately or heavily, and whether smoking. We include wave fixed effects and city fixed effects to control for unobserved heterogeneity and uneven distribution of green spaces across cities\(. {\epsilon }_{iw}\)is an error term.

With a sample of mobile location records of 4,835 LSOAs, Fig. 1A shows that the total number of travellers within London significantly decreased after the COVID-19 outbreak by 25% as compared to the pre-COVID19 period. The most significant drop of 55% was seen when the first lockdown was imposed. Meanwhile, starting from April 2020, the individual’s probability to travel to green space relative to other places increased compared to the same week in 2019. Interestingly, even during lockdowns, more people tended to travel to green spaces than to other spaces while the general mobility showed a significant downward trend. We attempt to perform spatial analyses and identify which LSOAs within London experienced higher or lower increases in the proportion of park travellers during lockdowns compared to the average amount in 2019. Figure 2 shows that LSOAs closer to parks and gardens were much more likely to see the increase in travellers to such green spaces than other LSOAs. Residential neighbourhoods in the red colour (increasing probability of park trips during lockdown) mainly cluster around major green spaces in London.

Table 1 provides summary statistics for the sample of LSOAs in London for our mobility analysis. Residents in neighbourhoods located within 800 meters of the nearest green spaces (treatment group) generally travelled more than those residing in neighbourhoods that were farther (comparison group). In line with Fig. 1B and Fig. 2, those in the treatment group were also much more likely to travel to green spaces, and their probability of visiting parks instead of non-park places within London was doubled compared with residents residing in other LSOAs. This difference in park trip behaviours by proximity to parks was most significant when people were stuck at home. After the COVID-19 outbreak, residents in the treatment group increased their travel to parks while those in the comparison group decreased park trips. During the lockdowns, those living closer to the park travelled more to parks while those living farther travel less to parks compared to non-lockdown periods. While demographic characteristics were quite similar for the treatment and comparison groups, residents in treated neighbourhoods were more likely to be whites. Deprivation indices suggest that while neighbourhoods in the treatment group were less deprived in general than a comparison neighbourhood, they were slightly worse in terms of health desirability, crime, and living environment, potentially because planned green spaces were more likely to be located in higher density areas.

Table 1

Descriptive Statistics of Travel Data in London
Variables	LSOAs within 800m from the nearest green space (Treatment)		LSOAs farther from the nearest green space (Comparison)
	Mean	Std. Dev	Mean	Std. Dev
Total number of travellers during non-work hours	1,316·92	1559·24	1,234·88	1431·24
Total number of park travellers during non-work hours	78·33	287·04	36·27	102·09
Proportion of the no. park travellers out of all travellers during non-work hours	5·96%	10·95%	2·90%	2·90%
Proportion of the no. park travellers out of all travellers before COVID-19	5·67%	5·67%	3·01%	4·84%
Proportion of the no. park travellers out of all travellers after COVID-19	6·31%	12·85%	2·77%	5·70%
Proportion of the no. park travellers out of all travellers during non- lockdown periods after COVID-19	6·26%	12·07%	2·84%	2·84%
Proportion of the no. park travellers out of all travellers during lockdown periods after COVID-19	6·46%	14·94%	2·58%	6·46%
Demographic characteristics (2019, 2020)
Population	1,867·71	441·81	1,849·58	527·33
Share of male population	0·46	0·50	0·43	0·50
Share of population under 18 years old	0·22	0·05	0·24	0·05
Share of population of 18 to 39 years old	0·35	0·10	0·32	0·09
Share of population of 40 to 59 years old	0·26	0·04	0·26	0·03
Share of population of 60 to 89 years old	0·16	0·06	0·17	0·06
Share of population over 89 years old	0·01	0·01	0·01	0·01
Share of White	0·73	0·15	0·68	0·18
Share of Asian	0·16	0·14	0·21	0·17
Share of Black	0·06	0·05	0·07	0·06
Share of other races	0·04	0·02	0·03	0·02
Socioeconomic characteristics (English indices of deprivation 2019)
Index of multiple deprivation (IMD) score	20·67	10·77	22·16	10·97
Income score (rate)	0·13	0·08	0·14	0·07
Employment score (rate)	0·08	0·05	0·09	0·05
Education, skills and training score	10·25	8·84	15·31	10·26
Health deprivation and disability score	-0·44	0·79	-0·34	0·63
Crime score	0·27	0·58	0·25	0·56
Barriers to housing and services score	30·03	9·18	32·89	9·99
Living environment score	31·81	10·96	27·15	10·37
Income deprivation affecting children index (IDACI) score (rate)	0·17	0·10	0·18	0·09
Income deprivation affecting older people index (IDAOPI) score (rate)	0·23	0·14	0·23	0·13
Number of LSOAs	2,149		2,686
Number of LSOAs*weeks (96 weeks)	206,063		257,198
Note 1: Non-work hours are defined as 16:00–24:00hr on working days and 08:00–24:00hr on weekends and bank holidays.
Note 2: The English indices of deprivation 2019 comprises 7 different domains which are combined and weighed to generate the LSOA’s IMD score out of 100. The higher score means more deprivation. IDACI and IDAOPI are supplementary indices for income. For more detailed descriptions of the data, refer to online guidance of the data (https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019).

Our empirical model results report the effect of being located closer to green spaces on the probability of traveling to green spaces. Estimates of the cross-section model show that there is about a 1.04–1.65 percentage point difference in the proportion of park travellers out of all travellers within London between neighbourhoods within 800 meters from the nearest green space and neighbourhoods located farther (Table 2, columns 1 and 3). Results also suggest that people increased their trips to parks than other places after the COVID-19 outbreak (column 1). Table 2 then shows the estimation results of the DID specification introduced in Eq. (1). The results shown in column 2 demonstrate that the COVID-19 shock caused a significant increase in the proportion of travellers to green spaces relative to other places from neighbourhoods within 800 meters of the nearest green space. After the COVID-19 outbreak, the probability of traveling to parks from these neighbourhoods was approximately 0.86 percentage points higher compared to other London neighbourhoods with similar characteristics but located farther from green spaces. In addition, during the lockdowns, neighbourhoods that were closer to parks experienced an additional increase of 0.46 percentage points in the proportion of park travellers (Table 4, column 4). This increase is much greater than the reduction in the proportion of park travellers during the lockdowns. Given that the average proportion of park travellers out of all travellers in London was about 6% (see Table 1), these increases after COVID-19 and during lockdown periods are significant. Our results suggest that people residing closer to green spaces were much more likely to travel to green spaces than those residing farther even when they faced strict restrictions on movement. Note that for our robustness test, we use the ratio of the number of trips to green spaces out of total trips and the results are consistent (Appendix 3).

Table 2

Empirical Estimation of Impact of Proximity to Parks on Park Trip Behaviours
	(1)	(2)	(3)	(4)
Dependent Variable	Proportion of Park Travellers	Proportion of Park Travellers	Proportion of Park Travellers (post-COVID-19 periods)	Proportion of Park Travellers (post-COVID-19 periods)
LSOAs located within 800 meters from the nearest park	0·0144***	0·0104***	0·0165***	0·0153***
LSOAs located within 800 meters from the nearest park	(0·0022)	(0·0020)	(0·0025)	(0·0024)
After COVID-19 outbreak	0·0007	-0·0029***
After COVID-19 outbreak	(0·0006)	(0·0005)
Lockdown			-0·0004**	-0·0024***
Lockdown			(0·0002)	(0·0003)
LSOAs within 800 meters from the nearest park & After COVID-19		0·0086***
		(0·0010)
LSOAs within 800 meters from the nearest park & lockdown				0·0046***
LSOAs within 800 meters from the nearest park & lockdown				(0·0008)
Observations	463,261	463,261	212,411	212,411
R-squared	0·2389	0·2371	0·2368	0·2369
Demographic characteristics	Yes	Yes	Yes	Yes
Socioeconomic characteristics	Yes	Yes	Yes	Yes
Local authority fixed effects	Yes	Yes	Yes	Yes
Month fixed effects	Yes	Yes	Yes	Yes
Notes: * indicates significance at the 1% level, indicates significance at the 5% level, and * indicates significance at the 10% level.

After establishing the evidence on the significant role of proximity to green spaces to the higher probability of traveling to green spaces after the COVID-19 outbreak and during lockdowns, we move to our next analysis of the mental distress of residents. With the sample of 4,998 individuals matched from the original sample of 19,020 survey respondents and their residential location information, Table 3 shows that people reported higher mental distress during the lockdowns compared with post-COVID-19 non-lockdown periods. In particular, residents living farther than 800 meters from green spaces (comparison group) experienced a more substantial increase in mental distress score during the lockdowns. Their mental distress score was 0.5 higher on average during the lockdown compared with post-COVID-19 non-lockdown periods, while those living closer to green spaces (treatment group) experienced an average increase of 0.35 in the score. As individuals in the treatment and comparison groups showed quite homogenous characteristics after matching (Table 3), this difference in the change in mental destress scores is likely to be attributable to the proximity of one’s residence to green spaces. The treated individuals tended to earn more than their matched counterparts even after matching and this is potentially because green spaces in our data are planned spaces that are more likely to be concentrated in cities with higher productivity.

Table 3

Descriptive Statistics of Matched Household Survey Data in the UK
Variables	Individuals living within 800m from the nearest green space (Treatment)		Individuals living farther from the nearest green space (Comparison)
	Mean	Std. Dev	Mean	Std. Dev
Total score of mental distress	2·36	3·32	2·26	3·29
Total score of mental distress during lockdown periods	2·49	3·33	2·45	3·35
Total score of mental distress during non-lockdown periods	2·14	3·30	1·95	3·17
Demographic characteristics
Age	54·21	16·94	54·96	16·33
Gender (male vs· female)	0·43	0·49	0·42	0·49
White	0·03	0·16	0·02	0·13
Black	0·87	0·34	0·91	0·29
Number of household members	1·63	1·25	1·64	1·22
Number of children	0·38	0·80	0·35	0·73
Having a partner	0·71	0·45	0·72	0·45
Economic characteristics
Household monthly earning	4,189·67	14,503·48	3,415·81	12,780·11
Individual monthly earning	1,716·01	6,298·52	1,414·13	5,310·86
Having financial difficulties	0·04	0·19	0·05	0·21
Health status
Having long-term health issues	0·40	0·49	0·41	0·49
COVID-19 infection	0·01	0·08	0·01	0·09
Number of days doing moderate activities	2·69	2·60	2·61	2·57
Moderate/heavy drinker	0·46	0·50	0·47	0·50
Smoker	0·07	0·25	0·07	0·26
Number of individuals	2,496		2,496
Number of individuals × survey waves	15,529		15,730

Table 4 first shows that after controlling for individual demographic, economic and health attributes, the mental distress score is substantially higher during lockdowns compared to non-lockdown periods. This average increase of 0.55 in mental distress score is substantial given the average score is only 2.31 for our matched sample. Nonetheless, compared with residents who lived farther from green spaces, those residing close to green spaces were able to do a much better job in offsetting this increased mental distress during lockdowns (column 2, Table 4). After including city- and wave-specific fixed effects, during non-lockdown periods, the mental health scores of these individuals were not significantly different from matched counterparts that have similar attributes and reside farther from green spaces (column 3). Furthermore, during lockdowns, the increase in the psychological distress score in the treatment group was 0.11 lower compared to those in the comparison group (column 3). Finally, we also observe that treated individuals had less volatile psychological distress scores between lockdown vs. non-lockdown periods than their matched counterparts. In particular, in the last wave of the UKHLS, which falls in the lockdown period, the treatment group showed a much lower median mental distress score than the comparison group, suggesting that people living closer to green spaces were able to better stabilize their mental status during lockdowns (see Appendix 4 for boxplot of the score).

Table 4

Empirical Estimation of Impact of Living Closer to Green Spaces on Psychological Distress
	(1)	(2)	(3)
	Cross-sectional	Difference-in-Difference	Difference-in-Difference
Living within 800 meters from the nearest park	0·0601	0·1338*	0·1291
Living within 800 meters from the nearest park	(0·0736)	(0·0789)	(0·0805)
Lockdown	0·5522**	0·3794***	0·6050***
Lockdown	(0·0534)	(0·0383)	(0·0589)
Living within 800 meters from the nearest park & Lockdown		-0·1031**	-0·1059**
Living within 800 meters from the nearest park & Lockdown		(0·0502)	(0·0501)
Observations	31,259	31,259	31,259
R-squared	0·1117	0·0958	0·1118
Demographic characteristics	Yes	Yes	Yes
Economic characteristics	Yes	Yes	Yes
Health status	Yes	Yes	Yes
City fixed effect	Yes	No	Yes
Wave fixed effect	Yes	No	Yes
Notes: * indicates significance at the 1% level, indicates significance at the 5% level, and * indicates significance at the 10% level

This study first examined how London’s population mobility to green spaces changed after COVID-19 and during lockdowns and then explored how better access to green spaces could affect psychological distress with a longitudinal cohort sample during lockdowns. The overall number of travellers within London has significantly decreased after the COVID-19 outbreak as compared to the same weeks in 2019, and the most significant drop was seen when the first lockdown was imposed. This trend was similarly discovered by many studies in other contexts. For example, the average time spent in non-residential locations decreased by 40% in response to various mobility restriction policies across 80 countries globally.³¹ Our study built an empirical model that considers population mobility patterns derived from anonymous mobile phone data. This approach is arguably better than using other available mobility data (including air and rail travel records, GPS loggers, Google records, apps, or other social media sources) as the latter could only capture the trajectories of subpopulations who use specific transport tools or mobile applications.³² By contrast, the comprehensive coverage of mobile phone users aged 15–65 years with a market share of 25% can help obtain a more representative sample for the whole population in London, and an accurate reflection of movement patterns between their residences and high-frequency destinations. Additionally, different from prior investigations that used mobile data,^33,34 this study integrated the location information of registered green spaces in London and examined whether the destination of each mobile phone data point falls in green spaces. Such investigation unveiled that an individual’s probability to travel to green spaces than other places has increased as compared to the same period in 2019, and the tendency to increase trips to greenspaces continued during lockdowns even when the general mobility decreased.

While the COVID-19 outbreak has substantially affected population mobility patterns within London due to people’s voluntary precautionary behaviours, lockdown orders have brought an even higher reduction in mobility. When people were ordered not to leave home without a reasonable excuse, they immediately reduced more than half of their trips.^35,36 Even with this reduced mobility, the probability of travelling to green spaces relative to other places showed a quick recovery about one week after the first lockdown order. Also, lockdown measures appeared to have different effects on populational mobility across different stages of the outbreak. In the period of our sample, we observed a much higher reduction in the year-over-year mobility changes during the first lockdown order than during the second lockdown. A potential reason could be that the second order (27 days) was much shorter than the first (91 days), and moving around would have been a more appealing option if allowed during the first order when the weather was more suitable for movement and outdoor activities (i.e., summertime) than the second (i.e., wintertime). In addition, people likely adjusted their travel behaviours from the continuing pandemic by the time they reached the second order. In terms of travels to green spaces, we found a more stable trend during the second lockdown, which could again be attributable to travel behaviour adjustment. The probability of traveling to green spaces instead of other places during the second lockdown is consistently higher than in the same weeks in 2019.

More importantly, this study examined how the proximity from residence to green spaces can affect individuals’ mobility during the lockdowns. After the COVID-19 outbreak and during the lockdowns, individuals who lived close to green spaces were more like to visit those spaces than other non–green spaces. These findings echo what urban and environmental studies have suggested: stressed individuals like to access green spaces more than other spaces.³⁷ The unequal access to green spaces presents a troubling picture to policymakers, as individuals who live more than 800 meters away from green spaces tended to travel less to green spaces than those who have better access. This by itself might not be an issue as the mobility restriction measures were meant to reduce social interactions and population mobility. However, this study highlighted the potential issue in regards to impaired psychological well-being.

By using the longitudinal cohort sample in the UK to track temporal changes in national mental health from before COVID-19 to the subsequent lockdown period, this study examined how individuals’ psychological well-being was affected during the pandemic with detailed time series data. Unlike previous inquires with similar data,^5,38 we associated the survey data with the individuals’ residence information and further investigated whether living close to green spaces helped individuals battle the negative influence of lockdown on their psychological well-being. Similar to previous studies,⁵ we found that individuals were significantly distressed during the lockdowns (vs. non-lockdown period) after accounting for all relevant factors. Specially, we observed a 0.55 increase in the psychological distress score.

Importantly, supporting our hypothesis, we found that, during lockdowns, individuals who lived close to the green spaces (i.e., within 800 meters) saw a much smaller increase in the distress score than those who lived farther away from green spaces (i.e., more than 800 meters) after controlling for all other potential determinants of mental health known in the literature. We also found that psychological states, as represented by the GHQ scores, were much more stable for individuals who lived close to green spaces than those who lived farther away. This is particularly interesting as we identified a potential group of the population that had a higher volatility of psychological distress during lockdowns. Unlike the prior studies that focused on examining effects of the individual characteristics—such as gender, age, educational attainment and socioeconomic position, Black, Asian, and minority ethnic (BAME) backgrounds, and living conditions (e.g., living alone)—this study identified an environmental factor that can provide policymakers with an opportunity to intervene. Building from the seminal work that has shown the positive effect of simply having a window view of a natural setting on the speed of recovery and quality of postoperative experiences, ³⁹ this study suggested that residing in a place close to public green spaces could also have a great beneficial impact on individuals’ mental health, especially when their mobility is restricted by lockdown orders.

Additionally, lockdown measures in the COVID-19 pandemic provided a better context to examine the effects of green space accessibility than quarantines in previous epidemics. For the majority of people in London during lockdowns, certain trips outside of the home were permissible so they could travel to green spaces. Because quarantines of previous epidemics posed stronger mobility restrictions and the number of affected individuals was typically small compared to an entire city lockdown, they provided no opportunity to observe population mobility, especially to green spaces. To our knowledge, this is one of the first studies to combine population mobility data with the longitudinal population survey and systematically examine how lockdowns affect the level of population psychology distress with a focus on exposure to green spaces. The unique datasets enabled the long-term tracking of population mobility and mental health before and during COVID-19. Although we didn’t test this proposition directly for those who cannot travel outside of their residences at all during lockdowns (e.g., someone with quarantine orders), we have a reason to believe that providing a place with a view of green spaces could be beneficial for the mental well-being of such individuals.³⁹

This work has limitations, which could spark future research. Because of the data limitations, our two analyses were performed with different samples and at different geographic scales (i.e., mobility analysis in London and mental distress analysis in the UK). Ideally, we would have wanted to have travel information of individual survey respondents so that we could directly associate their travel patterns to green spaces with their mental well-being. Also, we have done our best to identify individual mobility and associate that with park location, but there still could be some miscalculation on the location of the individuals given that the size of the antenna could potentially cover areas with both green and non–green spaces.

Overall, these findings suggested that when mobilities were restricted by lockdown measures during the COVID-19 pandemic, London residents increased their trips to green spaces. That is especially true for residents who lived closer to green spaces. This echoes prior research on how green spaces can help restore psychological distress and even reduce pain. Furthermore, individuals could greatly benefit from visiting green spaces, as such activities reduce the chance of experiencing psychological distress during lockdowns. We observed a higher level of aggravating psychological distress for those who cannot access green space easily (i.e., those living in areas not within walking distance). As countries and cities around the world face the risks of future lockdowns, these findings emphasize the importance of supporting and paying attention to individuals who have inferior access to green spaces. Some cities like Paris and Singapore have already begun plans to enhance accessibility to green spaces for more residential neighbourhoods and they are likely to be in a good position to prevent excessive mental illnesses during similar future pandemics. ⁴⁰ Besides creating more quality green spaces, policymakers can consider expanding trails and green networks to provide better environments for walking to larger green spaces and to ensure more equal access to green spaces for all citizens during lockdowns. It is a key to supporting more vulnerable groups during the current pandemic and those in the future.

Declaration of interests

We declare no competing interests.

Data sharing

Ethics approval for UKHLS was granted by the University of Essex Ethics Committee for the COVID-19 web and telephone surveys (ETH1920-1271), and we obtained approval from the University of Essex to access this data. Anonymous data can be requested from the University of Essex. Due to the non-disclosure agreement with Telefónica, we are not allowed to share the mobility data. Other anonymous datasets can be requested from the relevant agencies.

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There is NO Competing Interest.

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Green space accessibility helps buffer declined mental health during the COVID-19 pandemic

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