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Review

Daily Green Exposure, Mobility, and Health: A Scoping Review

by
Tong Liu
1,
Winifred E. Newman
2,* and
Matthew H. E. M. Browning
3,*
1
Glenn Department of Civil Engineering, Clemson University, S Palmetto Blvd, Clemson, SC 29634, USA
2
School of Architecture, Clemson University, 323 Fernow St, Clemson, SC 29634, USA
3
Department of Parks, Recreation and Tourism Management, Clemson University, 515 Calhoun Drive, Clemson, SC 29634, USA
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(8), 3412; https://doi.org/10.3390/su16083412
Submission received: 15 January 2024 / Revised: 11 April 2024 / Accepted: 16 April 2024 / Published: 19 April 2024
(This article belongs to the Special Issue Geographic Information Engineering and Geoenvironmental Sustainability)
Browse Figures
Versions Notes

Abstract

:
Mounting evidence suggests urban greenery promotes physical activity and human health. However, scholars have differing views on defining or measuring the terms related to green mobility behavior (MB). Therefore, evaluating how green MB impacts health is challenging. After an initial review of the literature on mobility, greenness, and health, we proposed “daily greenness exposure” (DGE) to define people’s exposure to natural/green settings. This approach lets us review and compare general and emerging measures of greenery exposure and differentiate study outcomes in MB and health. We identified 20 relevant Web of Science Core Collection studies during a scoping review completed in November 2021. Three types of DGE assessments were observed: ecological momentary, effect, and spatiotemporal. Four relationships were noted between DGE, MB, and health: moderation, mediation, independence, and undifferentiated. Incorporating these assessments and DGE modeling relationships contributes to better analysis and communication of environmental factors promoting health to environmental designers and policymakers.

1. Introduction

Physical inactivity is the fourth leading risk of mortality globally [1]. In the U.S., only one in five adults follows the recommended amount of physical activity—causing an annual cost of USD 100 billion in healthcare annually [2]. People need access to safe and convenient outdoor activity spaces to support health. Promoting physical activity requires public and private effort toward designing and constructing accessible, secure, and comfortable environments where daily green exposure, like shade trees for thermal comfort and parks for recreational activities, plays an essential role. Varied types of green exposure encourage physical activities related to daily living, like walking, jogging, and biking. These activities constitute people’s mobility behavior (MB) [3]. Most, but not all, studies agree that access to greenspace is important in determining MB. This scoping review looks at published studies up to 2021 to evaluate measures of greenspace like size, proximity, and quality. We also looked at the terms used to describe “greenspace” and connections with MB and health. The significance of this study is that it discloses how MB, health, and exposure to greenspaces correlate across the literature. A note about word usage. Throughout this article we opted to use the term “greenspace” rather than “green space” when referring to areas classified as parks, open areas including plant life, and water features considered part of a natural environment. As needed, there are additional explanations provided relative to the papers reviewed to qualify how researchers classified the “greenspace” in their study.
The impact of daily exposure to greenery on health is an old idea. The nineteenth-century landscape architecture pioneer, Frederick Law Olmsted, asserted that natural agents, like sunshine and foliage, provide positive sanitation and disinfection effects on the urban environment [4]. He saw that congestion, pollution, sanitation, and other issues associated with rapid urbanization threaten people’s quality of life and health. He further believed that overexposure to artificial city scenes damaged people’s mental health and social bounds; as a result, Olmstead was a strong advocate for integrating natural locations within urban built environments [5].
Several theories support links between daily green exposure and health. The naturalist Edward O. Wilson proposed the biophilia hypothesis—“the innately emotional affiliation of human beings to other living organisms”—based on thousands of years of human interaction with nature [6]. Other theories emerged following this hypothesis, like attention restoration theory—the vital function of the heart in recovering from mental fatigue [7]; stress reduction theory—the role of natural landscapes in alleviating stress [8]; and biophilic urbanism—cities’ strategic integration of natural elements [9]. Research expanded the mechanisms underlying nature exposure and health to encourage MB, build capabilities, mitigate harmful environmental vulnerabilities, and restore other health-promoting capacities [10].
However, green exposure is not always associated with MB [11,12,13]. Only around 40% of studies on green exposure (measured by the accessibility of greenspace) and MB published between 2000 and 2010 showed positive associations [12]. Negative associations might be due to divergent measures of greenspace, like size, proximity, and quality of MB [11]. Myriad terms apply to these topics, including “greenspace exposure” and “greenness exposure”, adding to the findings’ divergence.
Further complicating the measurement of green exposure are the definitions of the area where natural exposure occurs. Figure 1 illustrates the different definitions of the neighborhood found in the studies, from (1) the non-spatial population-based unit (census-defined area), or spatial definitions where (2) a circular area is measured around the primary home (home-centered area), or (3) a buffered area surrounding individual movement (path-based area). Chaix [14] defined experiential or ego-centered neighborhood as capturing “objectively experienced environmental exposures”. Existing work often uses census-defined areas (Figure 1a) or residential buffers (Figure 1b) to define possible areas to measure environmental exposure; however, recent smartphone applications with GPS services provide rich spatiotemporal mobility data and show significant potential in assessing exposures [15]. One study utilized smartphone tracking data to explore nature exposure via individual-level walking paths. The authors showed that estimating nature exposure based on path-based areas (Figure 1c) was a more precise measurement than relying on residences or those determined by census data [16].
Tracking-based exposure assessment provides a promising direction to assess dynamic, individual-level information on daily environmental exposure. However, we propose that a better understanding of the relationship between natural exposure and MB, especially regarding contributions to health, is still needed; thus, we suggest using an overarching model linking nature exposure, MB, and health outcomes to help refine terms and specify exposure measurements to assess the relevance of the study literature to MB, health, and greenness. We adopted a tool that Chaix et al. (2012) used to illustrate the neighborhood–mobility–health triad [26]. In a critical article, Chaix et al. outlined an interactive mapping tool (Figure 2) used to assess individual mobility patterns. The nmb triad is based on activity and place data, spatial behavior, and generalized built environments. They introduced a web-based application to visualize and evaluate route itineraries, travel destinations, and activity spaces (VERITAS) to geolocate activity and routes for individuals. Their approach, however, does not consider potential human–nature interactions (i.e., how nature attracts people to move). We modified the nmb triad to include “daily green exposure”. We used the new triad between daily green exposure, health outcomes, and mobility behavior to evaluate whether human–nature interactions are included or excluded from study claims regarding health and mobility.
By incorporating “daily green exposure” (DGE), the neighborhood–mobility–health triad adapts to current discussions of nature exposure (Figure 2). DGE refers to the dynamic exposure to greenery an individual is locally exposed to, measured by MB. This framework posits that daily green exposure promotes or inhibits MBs, influencing their daily directions. Daily green exposure may affect health through MB or static exposure. MBs outside of green settings also influence health outcomes. Finally, health conditions constrain daily MBs and the total greenness experienced.
To evaluate evidence for this model, we focused on the relationships between DGE, MB, and health outcomes. Our primary objective was to examine methods for assessing DGE in greenness-mobility and mobility-related health studies. Secondarily, we investigated how to model DGE in greenness-mobility and mobility-related health studies.

2. Methods

Given the diversity of relevant terms and concepts, we selected a scoping review approach [27]. A scoping review presents an overview of a large and diverse body of literature around a broad topic. We retrieved relevant studies by conducting keyword searches up to 26 November 2021 in the Web of Science Core Collection. We refined query search results under selected categories: Public Environmental, Occupational Health, Environmental Studies, Geography, Urban Studies, and Regional Urban Planning, and we conducted two searches with or without MB. The first search included the following: (Spatiotemporal OR dynamic OR daily) (greenness exposure OR exposure to nature OR green exposure OR contact with nature OR greenspace encounter) AND (MB OR mobility pattern OR walking path OR walking trajectory OR activity space OR activity place). The second search included the following: (Spatiotemporal OR dynamic OR daily) (greenness exposure OR exposure to nature OR green exposure OR contact with nature OR greenspace encounter) AND health.
Table 1 shows the inclusion criteria for eligible articles. We did not restrict study populations by age, gender, race, or health. We focused on urban contexts by excluding studies in rural or suburban areas. Study designs could be experimental, observational, or ecological. Analyses needed to consider objectively measured urban greenery (i.e., greenness, “greenspace”) and objectively or subjectively assessed MBs. There were no limitations to the analysis approach or publication date.
Table 2 includes definitions by study. The pertinent data source, size, and measurement for the type of greenspace per study are also included. The table shows the inclusion criteria for eligible articles. We did not restrict study populations by age, gender, race, or health. We focused on urban contexts by excluding studies in rural or suburban areas.
The initial search yielded 183 records, including 179 documents from the Web of Science and four other sources (Figure 3). After removing 16 duplicates, 167 records remained. After screening, we included 20 relevant articles [16,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44].
Studies published since 2018 have mostly been from developed countries (Figure 4). The United States (n = 7) and China (n = 6) are common countries where studies occurred. Other countries included Australia, the United Kingdom, Germany, Spain, and other European countries.
All 20 studies used observational designs. Only two had longitudinal data collection [31,38]; the remainder had cross-sectional data. The studied populations included adults and specific age ranges, like children/adolescents [28,30,36,39] and cognitive aging in later life in older adults [31]. Studies varied along other characteristics, like focusing on white-collar workers [42], college students [33], or pregnant women [40]. Sample sizes ranged from 32 [34] to 24,601 [39].
Assessments of DGE were on individuals’ spatiotemporal behaviors. For ease of interpretation, we classified them into three major categories: (1) ecological momentary assessment, (2) effect assessment, and (3) spatiotemporal assessment. We also generated subcategories for each significant type (Table 3). Summaries of each are in the following sections.

3. Results

3.1. Ecological Momentary Assessment

Three studies applied ecological momentary assessments (EMAs) of DGE. An essential article by Saul Shiffman, “Designing Protocols for Ecological Momentary Assessment”, addresses research design with EMA [45]. EMA is widely used in environmental studies to minimize recall bias by tracking subjects’ real-time behaviors, moods, and experiences. Shiffman emphasizes that studies must carefully consider the arrangements for collecting EMA data: when and how to assess samples. Shiffman’s Geographic EMA (or GEMA) simultaneously follows the subjects’ momentary locations with EMA reports. Other researchers used EMA to determine different outcomes. Mennis et al. used GEMA with generalized estimating equations (GEE) and a normalized difference vegetation index (NVDI) from Landsat imagery to asses psychological conditions for African American adolescents in Richmond, Virginia [39].
Almanza et al. used EMA and NVDI to study greenness exposure and free-living physical activity to quantify children’s greenness exposure [28]. This study included a 30 s epoch accelerometer and GPS data points to create a linear mixed model with kernel density to analyze neighborhood activity data. EMA was positively associated with physical activity, especially for smart growth residents who experienced a 39% increase in odds of physical activity. Children who experienced at least 20 min of daily exposure to greenness had five times greater daily rates of physical activity than children with almost zero daily exposure to greenness.
In a 2013 EMA study using momentary subjective well-being (mSWB), MacKerron and Mourato found that a smartphone app coordinated with satellite positioning (GPS) to signal participants to participate in a brief questionnaire at random moments [37]. Their results showed that, on average, participants were happier outdoors in partial or all-natural greenspaces than in urban environments. Generally, advanced GPS tracking technology, like smartphone tracking, obtains real-time individual-level geographical data. A critical setting of EMA is the daily repeat rate, and three reviewed studies applied varying frequencies of participant notifications. Specifically, Almanza tracked participants five times daily via GPS and wearable accelerometer devices to measure greenness levels and physical activity intensities [28]. MacKerron and Mourato asked participants to choose their preferred frequency and time range for receiving random questionnaires [37]. Mennis et al. adopted web-based surveys sent via text message three to six times per day [39].

3.2. Effect Assessment: Usage, Activity Space, and Travel Route Assessment

Effect assessments investigate regular daily behavioral patterns, like usage patterns, location of activities, or travel routes, and consider the regularity of individual-level mobility patterns, different from random momentary surveys and location tracking in EMA. According to MB attributes in previous studies, we classified effect assessments into three types: usage-based exposure, activity space-based, and travel route-based assessment (see Table 2).
Usage assessment measures individuals’ usage patterns of greenspace. Usage means frequency of visits, time spent, or passive/active level of engagement with surrounding environments. Activity space assessments use indices of activity space exposure weighted by time spent in each location an individual stays. For example, Li et al. used GPS tracking data at equal time intervals to identify time spent by tracking density, quantified via a density-weighted index, like the green view index [36]. They calculated the concentration of greenness exposure for participating adolescents using street view images of the locations they visited daily according to the GPS data. Multi-level modeling indicated strong associations between greenness exposure and daily mood.
Wang et al. studied the dynamic greenness exposure and the mental health of residents in Guangzhou, China. They used travel route assessment focusing on GPS-derived green exposure, quantifying nature exposure buffered by travel routes [41]. To examine the actual greenspaces in residential neighborhoods the study used eye-level, overhead, street view greenness (SVG), and self-reported resident area greenness exposure. The four measures were distinct; however, although all four correlated with mental health, compared to participant survey data, only SVG quality positively correlated to mental health, indicating that eye-level greenness is the most critical element in the correlation and association with mental health.

3.3. Spatiotemporal Assessment: Integrated Assessment

Integrated spatiotemporal assessments capture the dynamicity of an individual’s exposure to greenness. For example, Wang et al. combined static DGE at home/workplace and dynamic DGE during travel. The authors further captured static DGE from nature availability (measured by the normalized difference vegetation index [NDVI]), visibility (measured by the green view index), accessibility, and dynamic DGE via travel routes, with the assessment based only on nature visibility [44]. Their study examined green justice inequalities between areas with high and low greenspace environments. They used surveys of Beijing residents’ daily travel environments, remote sensing images, street view images, and machine learning to assess whether commuting exposure (dynamic DGE during travel) alleviates green inequality for residents with low static green exposure. The study concluded that high levels of dynamic green exposure are associated with individuals living or working in a satisfactory greenspace, which suggests that differences in greenness distribution by community impact dynamic green exposure.

3.4. Modeling Relationships between DGE, Mobility Behavior, and Health

The reviewed literature identified four relationships between DGE, MB, and health. First, green exercise increased the positive moderating effects of daily green exposure on health outcomes (Figure 5a). Second, DGE mediated increased MB and health outcomes (Figure 5b). Third, DGE and MB acted independently on health outcomes (Figure 5c). Fourth, DGE and MB were modeled without differentiating their individual effects on health outcomes (Figure 5d).
No evidence suggested one modeling relationship was more reasonable. Regarding moderation, Kajoraari and Pasanen reported additional restoration benefits of outdoor green exposure when exercising [46]. Their study applied a spatial approach using public participation GIS (PPGIS) methods to explore the perceived restorative outcomes of diverse outdoor physical activity environments. Findings indicated that exercising in greenspaces and significant natural areas provides additional therapeutic benefits compared to exercise undertaken in built outdoor environments. Ambrey showed that DGE and MB impact health independently [29]. Using survey data and GIS data from the Household, Income, and Labor Dynamics in Australia, this study found that greenspace and physical activity are independently and positively associated with life satisfaction and mental health and negatively related to psychological distress. Zhang et al. and Van den Berg et al. confirmed the mediating role of MB between DGE and health [43,47]. Zhang et al. use individual activity space (IAS) to measure exposure to greenspace, which considers a person’s actual movements and activities throughout the day rather than just their residential location. They also use structural equation modeling (SEM) with survey data collected in Guangzhou, China, and high-resolution remote sensing images to model individual activity space for a weekday and measure participants’ daily greenspace exposure to analyze the relationships between greenspace exposure, physical activity, and health outcomes.
In comparison, Van den Berg et al. used questionnaires and satellite data on residential greenness from four European cities. Their findings offered little evidence to support a link between mental health and purposeful greenness exposure. Still, they did not rule out the connections between unintentional disclosure like streetscape greenery, e.g., small greenspaces like gardens and street trees visible from home. Many studies, such as Maes et al. and Robinson et al., did not differentiate the individual effects of MB and green exposure on health outcomes but found positive overall health effects [38,40]. Maes et al. focused on adolescents aged 9 to 15 in London, UK, and studied associations between natural environment types and adolescents’ cognitive development, mental health, and overall well-being. This study identified environments by type in three tiers: natural space, subdivided into green and blue and greenspace further divided into woodland and grassland. In short, although evidence for these four relationships was available, there was insufficient evidence to make comparisons for future research or policy implications. Finally, Robinson et al. looked at greenspace exposure for pregnant women with 28 environmental indicators (weather, air pollutants, traffic noise, etc.) across 9 urban areas using GIS, remote sensing, and spatiotemporal modeling. The study showed significant variability with mixed results.

4. Discussion and Future Directions

Studies evaluating the relationship between green exposure and MB face challenges in measuring exposure and mobility. Researchers have used various measures to define and quantify green exposure, including objective measures like remote sensing and GIS data and subjective measures like self-reports and perceived measures. Similarly, measures of MB vary widely, including self-reporting, accelerometer-based measures, and GPS tracking. These differences in measurement make it difficult to compare findings across studies and to draw conclusions about the relationship between green exposure and MB. Early studies in green exposure, mobility, and health do not rely on measuring dynamic human behavior patterns or green exposure from spatiotemporal mobility.
For this study, we used a scoping review. Scoping reviews are preliminary literature assessments that identify knowledge gaps, scope the literature, and clarify concepts. The value of the scoping review versus a systematic review is that it allows evidence synthesis. Future studies would include a systematic literature review on mobility, greenspace, and urban habitats.
Our study indicates that 60% of studies published between 2000 and 2010 show little association between green exposure and MB. Possible explanations are that negative associations might be due to divergent measures of greenspace, like size, proximity, and quality of MB. It is difficult to determine how to study this phenomenon given this diversity, and there is little to no consistency in the terms applied to these topics, including “greenspace exposure” and “greenness exposure”, adding to the findings’ divergence.
After we reviewed the literature, we proposed a generalizable term, daily greenness exposure (DGE), specifying the exposure to greenness in a person’s everyday environment to reduce inconsistencies and promote meaningful cross-comparisons. Next, we reviewed 20 relevant studies published since 2010 that considered dynamic MB and addressed behavioral or spatiotemporal attributes of greenspace. We reported three categories of assessments identified in the literature for daily green exposure (DGE): (1) ecological momentary assessment, (2) effect assessment, and (3) spatiotemporal assessment. The assessments include subcategories that qualify how the exposure occurs and in what timeframe, e.g., daily, during a travel route, or real-time self-reported by participants. We identified four modeling relationships in the research studies: (a) moderation, (b) mediation, (c) independent, and (d) undifferentiated.
Assessing and modeling DGE has advantages for research and policy. Exposure to greenspace uses many terms and approaches. DGE will not solve this, but it provides a unifying framework to consider how daily exposure and mobility impact health outcomes. Referencing this framework when defining exposures and mechanistic pathways helps.
Utilizing DGE as a concept in environmental design and policymaking contributes to managing evidence of how greenery and mobility promote human health. Future directions include continued literature assessment in mobility and greenspace, policy, and planning. A better understanding of the impact of the research on policy decisions could incentivize the use of DGE as a tool for assessing greenness exposure for existing urban conditions. DGE, as a generalizable term, offers an improvement over current terminology. DGE, like the neighborhood–mobility–heath triad, creates a framework linking disparate concepts in multiple disciplines. When applied as an analytical tool with modeling relationships, it gives policymakers and stakeholders a way to categorize the elements and methods in each study. The design of greenspaces is always evolving, and one of the limitations of DGE is the utility of the framework when confronted with new greenspace typologies. In addition, current technical tools used to collect data may offer new forms of empirical data.
Future research questions include the following: how we can standardize the measure of greenness exposure to facilitate comparative studies across different regions and populations? What role could policymakers play in promoting the integration of natural elements in urban built environments? And how can emerging technologies like remote sensing and machine learning contribute to better assessment of greenspace exposure and health outcomes? New technological tools will come with new techniques and likely yet foreseen elements of the use and perception of the built environment. One of the questions not addressed in the literature directly, but of increasing importance, as sustaining greenspaces in increasingly fragile ecosystems or with limited resources becomes more difficult, is the question of equity and access. We need to ensure that our definitions of greenspace are inclusive and representative of diverse communities. Access to greenspace and its health benefits should be available to various socio-economic conditions and communities. Policymakers need tools like DGE to ensure marginalized communities have equal access to greenspace. Greenspace planning and design will play a significant role in mitigating the negative impacts of climate change, especially on vulnerable populations. DGE may additionally serve as a resource for public health communication and a way to leverage community engagement and participation in promoting awareness of the health benefits of access to greenspace.
Despite these challenges, mounting evidence suggests that exposure to greenspaces can promote physical activity and improve health outcomes. This scoping review aims to contribute to our understanding of this relationship by proposing a new measure of green exposure, daily greenness exposure (DGE), and by reviewing and comparing existing measurements of green exposure and their relationship with MB and health outcomes. By incorporating these assessments and DGE modeling relationships, this review contributes to better analysis and communication of environmental factors promoting health to environmental designers and policymakers.
We cannot predict how much natural habitat we will lose to urbanization in the future. We do know the health benefits of physical activity and exposure to greenspace. Mobility behavior does not happen in a vacuum. Environmental context requires analysis of the dynamic interplay between behavior and environmental exposure. Green exposure is similarly dynamic and incorporates travel mode with assessments of space and time. Without a better understanding of the specific characteristics of dynamic exposures to greenspace that attract and encourage health-promoting activities, designers and policymakers cannot leverage avenues to overcome the ill effects of urbanization.

Author Contributions

Conceptualization, T.L., M.H.E.M.B. and W.E.N.; methodology, T.L. and W.E.N.; writing—original draft preparation, T.L.; writing—review and editing, M.H.E.M.B. and W.E.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We acknowledge the contribution of Nazanin Hatami from the Georgia Institute of Technology for her assistance with proofreading.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Areas commonly used to measure daily exposure to nature were reviewed in 10 studies [16,17,18,19,20,21,22,23,24,25]. (a). Census-defined exposure area. (b). Home-centered exposure area. (c). Path-based exposure area. See Table 2 for detailed exposure measurements of each piece of the literature.
Figure 1. Areas commonly used to measure daily exposure to nature were reviewed in 10 studies [16,17,18,19,20,21,22,23,24,25]. (a). Census-defined exposure area. (b). Home-centered exposure area. (c). Path-based exposure area. See Table 2 for detailed exposure measurements of each piece of the literature.
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Figure 2. A conceptual framework linking “daily green exposure” with mobility behavior and health, adapted from the neighborhood–mobility–health triad [26].
Figure 2. A conceptual framework linking “daily green exposure” with mobility behavior and health, adapted from the neighborhood–mobility–health triad [26].
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Figure 3. Identification of relevant articles.
Figure 3. Identification of relevant articles.
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Figure 4. Year (a) and country where the study was conducted (b) in reviewed articles (n = 20).
Figure 4. Year (a) and country where the study was conducted (b) in reviewed articles (n = 20).
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Figure 5. Modeling relationships between DGE and mobility behavior on health outcomes: (a) moderation, (b) mediation, (c) independent, and (d) undifferentiated.
Figure 5. Modeling relationships between DGE and mobility behavior on health outcomes: (a) moderation, (b) mediation, (c) independent, and (d) undifferentiated.
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Table 1. Criteria for studies included in the review.
Table 1. Criteria for studies included in the review.
ParameterCriteria
PopulationAny age, gender, race, and health condition
InterestPeer-reviewed articles in English. Reviews and Protocols
excluded
ContextUrban
Study DesignExperimental, observational, ecological study designs with objectively measured urban greenness and objectively or subjectively assessed mobility behaviors
TimingNo restrictions on date of publication
Table 2. A list of the literature (* PWC: population-weighted centroid; MCD: minor census division; CCD: census county division).
Table 2. A list of the literature (* PWC: population-weighted centroid; MCD: minor census division; CCD: census county division).
Author, YearDescriptionData SourceMeasurementSize
Richardson et al., 2013 [22]They included natural areas (e.g., parks, beaches, and fields). Marine areas and private gardens were excluded.3 Land use datasetsThe proportion of greenspace within each census area unit (n = 1927) in New Zealand≥0.02 ha (200 m2)
Richardson and Mitchell, 2010 [23]A wide range of greenspace, from transport, verges, and neighborhood greens to parks, playing fields, and woodlands.2 Land use datasetsThe proportion of greenspace within each UK Census Area Statistic ward (n = 6432).≥5 m2
McCracken et al., 2016 [20]Included all vegetated open space areas. Water areas were excluded.Central Scotland Green Network datasetGreenspace availability within a 500 m buffer radius around the residence (n = 276; clustered in 46 spatial groups).Not specified
Coppel and Wüstemann, 2017 [18]Inclusion: city administration’s definition of urban green areas. Exclusion: street trees or private backyards/allotment gardens1 Land use dataset (Urban Atlas)Proximity (Euclidean distance between home locations and the nearest edge of green
spaces); Coverage of greenspace within a 250 m buffer around the residence (n = 874) in Berlin, Germany.		≥0.5 ha (5000 m2)
Nutsford et al., 2013 [21]They categorized total greenspace (including privately owned land) and usable greenspace (like parks and sports fields). Private gardens were excluded.Greenspace dataset (generated from 2008 Land Class Database II)Proximity (distance between each PWC * and nearest greenspace): the proportion of total and usable greenspace within 300 m and 30 km buffer areas around each PWC * in Auckland, New Zealand.≥500 m2
Vich et al., 2019 [16]Street trees, parks, beaches, squares, and boulevards were included.City’s land use map, street tree map, and street network shapefileTree density: the proportion of green areas with a 20 m buffer around the walking trajectories (Barcelona, Spain)≥16.88 m2
Schipperijin et al., 2010 [24]Greenspaces, parks, woodlands, and nature areasGreenspace datasets from the Municipality of OdenseProximity (self-reported distance, Euclidean, and network distance to nearest green
space). (Odense, Denmark)		>5 ha; 1–5 ha; <1 ha
Heo et al., 2020 [19]Parks (state parks and local-scale parks) and forestsSatellite imagery with 250 m resolution; park data from Esri and OpenStreetMapEnhanced Vegetation Index (EVI) for each MCD * in Maryland and each CCD * in California, US; greenspace locations.Not specified
Akpinar, 2016 [17]Seven selected UGS (3 neighborhood parks, one urban park, and three urban greenways).Aydin Metropolitan Municipality Master PlanProximity (distance from home to nearest UGS); Quality factors (like aesthetics and maintenance). (Aydin Turkey).Not specified
Venter et al., 2020 [25]Tree cover, vegetation greenness, and trail remotenessLand use and land cover datasets; DSM and DTM with 1 m resolution; satellite imagery with 10 m resolutionTree cover; normalized difference vegetation index (NDVI) in Oslo, NorwayNot specified
Table 3. Daily green exposure (DGE) assessments in reviewed articles (n = 20).
Table 3. Daily green exposure (DGE) assessments in reviewed articles (n = 20).
Assessment CategorySubcategoryDescription
Ecological momentary assessmentReal-time assessmentCollecting real-time self-reported or recorded data in daily life
Effect assessmentUsage assessmentCalculating the “dose” of greenspace exposure based on usage patterns, like frequency and duration of active and passive use of greenspace
Activity space assessmentCalculating average daily exposure to environments from the proportion of time spent in different activity locations
Travel route assessmentCalculating people’s actual route exposure from their mobility patterns
Spatiotemporal assessmentIntegrated assessmentCalculating people’s spatiotemporal exposure with varied exposure types, like time-weighted mobility route exposure in combination with activity space exposure
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Liu, T.; Newman, W.E.; Browning, M.H.E.M. Daily Green Exposure, Mobility, and Health: A Scoping Review. Sustainability 2024, 16, 3412. https://doi.org/10.3390/su16083412

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Liu T, Newman WE, Browning MHEM. Daily Green Exposure, Mobility, and Health: A Scoping Review. Sustainability. 2024; 16(8):3412. https://doi.org/10.3390/su16083412

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Liu, Tong, Winifred E. Newman, and Matthew H. E. M. Browning. 2024. "Daily Green Exposure, Mobility, and Health: A Scoping Review" Sustainability 16, no. 8: 3412. https://doi.org/10.3390/su16083412

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