https://orcid.org/0000-0003-4392-7167
Correction
2 Apr 2025: Bar-Ad R, Vigo M, Caruso G, Quboa Q, Pinto N (2025) Correction: Evaluating the impact of added greenery on perceived factors of an urban environment in virtual reality. PLOS ONE 20(4): e0321871. https://doi.org/10.1371/journal.pone.0321871 View correction
Figures
Abstract
The wellbeing effects of urban greenspace are well established, but may be more attributable to pedestrians’ perceptions than objective levels of greenery. An immersive virtual environment was designed with three levels of roadside greenery: no trees, 200 trees, and 400 trees. Participants were asked to rate each for several perceived and objective factors, and gave their years lived experience in urban, rural, and suburban environments. Trees impacted perceptions of beauty and greenness, and, slightly, building heights. Controlling for urban experience significantly lessened the impact of trees, but showed that perceived greenness had higher significant correlations to all other outcomes.
Citation: Bar-Ad R, Vigo M, Caruso G, Quboa Q, Pinto N (2025) Evaluating the impact of added greenery on perceived factors of an urban environment in virtual reality. PLoS ONE 20(2): e0316195. https://doi.org/10.1371/journal.pone.0316195
Editor: Sara Eloy, University of Antwerp, BELGIUM
Received: May 14, 2024; Accepted: December 8, 2024; Published: February 6, 2025
Copyright: © 2025 Bar-Ad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data is available at https://figshare.com/s/f6b27959d8a8ec8dbc24 Reimplimentation of MRAT can be found at https://vr-analytics.github.io/.
Funding: Economic and Social Research Council Grant 2493360, Turing-Manchester Sandpit Project Funding for project 'A VR analytics framework to assess the user experience of digital twins' The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
As more and more of the world’s population moves to urban area [1], and with growing evidence of the connections between urban environments and poor wellbeing [2–8], research is being undertaken on the elements of urban environments thought to have the most impact—in particular, green space. The idea that urban green space can be strongly correlated to resident activity and wellbeing [9] has generated an interest in the efficacy of urban greenness as a tool for the promotion of physical activity and public health [9–25].
However, some papers cast doubt on these findings, suggesting it is the perception of greenness that affects the wellbeing of residence more than the objective measure of greenness does in itself (see [26–28]). While research has posited an impact on perceptions of urban spaces from personal background [29], and from community attributes [30–34], there is little research verifying the impact of personal experience on these perceptions [33,34].
To evaluate the perceptions of urban space, the use of Immersive Virtual Environments (IVEs) has gained popularity in recent years (see [35–38]). The use of virtual reality gives researchers the benefit of both the closest possible sense of presence to really being somewhere, and the highest possible level of control over the environment [35,39–44].
However, if data is to be collected on participants in an IVE, it should not interrupt their immersion or disrupt their sense of presence. One way to combat this is to rely on physiological and discrete-choice data [36,45,46], another is the use of in-VR questionnaires (IVQs), which do not require the user to exit the virtual environment [47,48].
Three research gaps can therefore be identified: one in the implementation of ecologically valid IVEs; one in the effect of urban greenness on perceptions of space; and one in the confounding role of personal background in the perceptions of urban greenness.
This article aims to address these gaps, by answering three fundamental research questions.
- How can immersive virtual environments be utilised in user-centred urban environment research?
- How does the inclusion of greenery impact pedestrian perceptions of urban space?
- How does lived experience of urban areas affect said perceptions?
To address these questions, we present an experiment wherein participants in an IVE are shown a procedurally generated city street with and without roadside trees, and are asked to rate each version for beauty, safety, density, greenness, building height, and road width.
The Literature Review section will pertain to perceptions of urban greenspace and its link to wellbeing, as well as the use of virtual reality in researching this field. The Review of Existing Approaches section will further discuss existing uses of IVEs to research perceptions of the urban environment. The Methods section will cover this study’s approach and design, Findings will cover the results of our experiment, and Discussion and Conclusions will give a final discussion and conclusion.
Literature review
This review will cover the effects of urban green space and its perception in subsection Perceptions of Urban Green Space, and the use and validity of IVEs in subsection Immersive Virtual Environments.
Perceptions of urban green space
Past approaches to the examination of the effects of urban green space rely on different measures of greenery, from park proximity to Geographic Information System (GIS) models of land use to presence of road-side vegetation and many more [49]. Nevertheless, the literature generally suggests a link between resident wellbeing and greenery, in one form or another [50]. Park proximity is an established predictor of physical activity, which is highly related to physical health [20,25,51–55]. Greenery and green spaces are strongly connected to psychological wellbeing [13,19,21,23,24,56–60], green spaces are associated with social connections [16,56], and other studies show strong connections between urban greenspace and morbidity [61], stroke mortality risk [62], and general health [12,15,18,63].
However, [28] casts a doubt on these findings, showing that the perception of greenness on its own improves mental health and encourages walking, which improves physical health. [26] supports this in finding that perceived friendliness of an urban environment is directly connected to self-rated health. [27] similarly showed that people tend to be happier in more scenic locations, scenic-ness being a perceived and subjective quality. Further, [64]‘s concept of ‘just green enough’ urban planning, that attempts to help working class areas without opening them up to gentrification, finds a balance between as much of the health benefits of green space as possible with as little possible effect on perceived property value (also see [63]‘s discussion of the necessity of ‘just green enough’ design).
This is supported by studies more adjacent to urban greenery. Several studies have shown an apparent mismatch in the variations of urban biodiversity and its perception [65,66] showed that it’s the mixed land use that most impacts land prices, rather than the presence of greenery itself. [67] found that the well-being of greenspace visitors was connected to their perception of the space’s species richness, but found no consistent pattern with actual, measured species richness. [68] found that, while visitors expressed a preference for species richness, they did not notice increased flower diversity when it was present.
The perception is not only affected by present environment, but also by the individual and their background. [65] found that highly urban-oriented and highly nature-oriented individuals perceived more urban greenery than their less-oriented counterparts. [69,70] show that cultural and personal attitudes can affect an individual’s perception of an urban environment, [71] found that childhood use of greenspace is an important predictor of adult use of greenspace, [72] finds that children raised in city environments are more predisposed to have a positive view of urban greenspace, and [73] finds that childhood perceptions of nature can predict some adult greenspace visitation patterns.
This is underpinned by [74]‘s finding that visitation of greenspace reflects its perception, specifically its perceived safety.
The idea that perception of the urban area may be as key to wellbeing as its objective qualities is not specific to urban green space. [75–77] all described the different ways urban density can be perceived and evaluated differently by people from different cultures and countries, finding that people can ‘read’ an environment as ‘dense’ or ‘not dense’ irrespective of (or partly independently of) the actual number of people per unit area. [29] theorised a notable number of personal and environmental factors that may influence an individual’s perceptions of an urban environment; and more recently [78] found that, above a certain threshold, acceptance of denser areas was mostly affected by participant perceptions (for example their perception of the politics of urban densification). [38]‘s finding, that the effects of increased urban density can to some extent be mitigated by mixed land use and urban greenery, would suggest that these fields are strongly connected. It is not only that density and greenery are both values that can be interpreted separately in their objective and perceived forms, but that both form part of the larger impact of the perception of urban environments.
These studies together would suggest that it is not simply the objective measure of greenness in an urban space that improves the wellbeing of residents, but the perception the residents have of the space that makes the difference. This would imply that the elements that influence an inhabitant’s perception of that greenness can be controlled to improve their wellbeing, without necessarily changing the actual, objective greenness of the area.
Immersive virtual environments
Considerable research has already been undertaken trying to understand the validity of VEs in general and IVEs specifically. [79] found that experiments showing an immersive 360-degree video on a head-mounted display can substitute field experiments, and show no significant difference in user self-reports. [80] found that pedestrian responses to an environment in immersive 360-videos are largely similar to those in the real world, and induce less distortion than still images. [81] found that perceptions of an environment did not differ between a field study at a real-life location and a lab study of an interactive IVE representation. Other studies found that participants in a virtual environment behaved similarly to real life, with a variety of approaches to the VE itself [82–86].
All these works together establish a pattern of consistency between in-VR behaviour and in-real-life behaviour, creating a foundation of ecological validity from which VR studies can continue. IVEs specifically are fully immersive, in that the user is perceptually surrounded by the virtual environment. This includes head-mounted displays (HMDs) and Cave Automatic Virtual Environment (CAVE) systems, which are both shown to be highly immersive [87], though CAVEs require stereoscopic glasses and large spaces. They can show results even closer to reality than traditional choice experiments or 360-degree still images [88,89], and can provide more realistic feelings and more useful data [90,91] found that, for landscape planning, “the immersion, motion, and sound offered by virtual worlds may greatly extend the ecological validity of environmental representations and allow for deeper and more meaningful study of the effects of the real world on human experience.” (pp. 139).
The ecological validity of IVEs can be assessed through subjective response surveys, cognitive performance tests, and physiological responses [46], usually on the metrics of immersion and presence [36]. Immersion can be expressed as the extent to which the IVE and the hardware delivering it create an “inclusive, extensive, surrounding and vivid illusion of reality” [92] (pp. 606). Presence is considered to be a sense of being in the virtual environment, despite not physically being there [36,93–95]. The higher the measures of presence and immersion, the more ecologically valid an IVE is considered to be [82,96].
Presence and immersion can be increased through higher levels of realism [48,82,96,97], which can be expressed through photo-realism [98], soundscape realism [99,100], or interactivity [92]. However, they can be interrupted by disrupting the participant’s connection to the virtual environment, for example by removing their headset [47,48] or even using teleportation locomotion (a method of traversing the virtual environment by pointing at a location and appearing there) [101].
As noted above, ways to avoid breaks in presence and immersion are relying on physiological and discrete-choice data [36,45,46] and using in-VR questionnaires (IVQs), which do not require the user to exit the virtual environment [47,48].
Review of existing approaches
VR has long been used in urban planning and architecture research, often with the intention of evaluating the simulation itself as a research method [102].
An example of IVEs specifically is [36], who created a highly immersive environment by expertly combining Unity store assets in the style of a typical urban area, rather than replicating a specific location or relying on existing models. They measured both physiological metrics such as electrodermal activity and heartrate, and user self-reports in the form of IVQs. They tracked user position at all times and gaze fixation on two virtual objects. The IVE was experienced through a HMD and interacted with through a walking controller (a VR treadmill).
Another IVE study to consider is [38], who used a large concave screen with a 75-degree field of view to project short videos simulating a walk down a virtual replica of a real street. This approach is similar to the CAVE system, in which a set of screens, projectors and motion-detection cameras are set up within a designated room [36], the key difference appearing to be that CAVE systems are generally interactive. In terms of immersion, there does not seem to be a significant difference between CAVE systems and HMD systems [90,103,104].
Both [36,38] used hand-made virtual environments, one with custom assets chosen and assembled to create the effect of a familiar urban setting, and one using repeated patterns to replicate an existing street layout in different configurations. This level of control allows the potential for more immersive environments, for example by using high-quality assets or expert environment designers. Both these approaches require the experience and time to design a virtual environment. [105] lists the steep learning curve and experience necessity of using VE development tools as one of the two most significant drawbacks of VR research, alongside the large space requirement.
In terms of hardware, [38] has the larger space cost by requiring a room for the 2.4x7.5-metre screen, but can include participants in groups in order to save on time. [36] only needs enough room for the walking controller, but can only immerse one participant at a time. This introduces a sort of time-cost in terms of sample participation—a larger sample has a direct and linear relationship to the time required for the data collection in both cases, but one is decidedly lower than the other.
The equipment required for both approaches is expensive, one using a large screen and two projectors and another using a walking controller and HMD. [36] also utilises physiological sensors, one for EDA, cardiovascular activity and skin temperature, and another for gait analysis. [38] also has the upfront expense of the venue required for screenings.
We can therefore identify four metrics by which we can compare the two approaches:
- Ease of Development—the experience required and difficulty associated with the development of the VE and integration of the hardware;
- Money-Cost of Development—the financial expense of the hardware and software used;
- Time-Cost of Experiment—the number of participants who can be immersed in the VE at a time;
- Level of Immersion—the amount the participant can really feel they are physically present in the VE.
Level of Immersion, as discussed in the Literature Review, can be measured by [92]‘s objective framework, by physiological and psychological measures of presence, or by qualitative analysis. [92] identifies six objective elements of an IVE that can measure its impact on immersion: Inclusive (the extent to which physical reality is shut out), extensive (the range of sensory modalities accommodated), surrounding (extent of field of view—panoramic vs narrow), vivid (resolution, fidelity, variety of energy simulated, richness and information content), matching (accuracy of system responsiveness to user interaction, e.g. turning head should turn perspective in a 1:1 relationship), and plot (extent to which VE presents a narrative of internal events distinct from those happening in the real world). For example, a setup like [36]‘s might score highly on inclusivity, extensiveness, and surrounding thanks to the use of a HMD, and vividness, matching, and plot thanks to a hand-made virtual environment and high-spec hardware.
Time-Cost of Experiment is a simple evaluation of the number of participants able to engage with the VE at a time. This may also include the amount of time required to train participants in the use of the hardware, to monitor them to ensure their safety, and even simply the average length of their participation.
Money-Cost of Development is the financial cost of every piece of equipment, of the space they occupy, for the time they are used. This will be affected of course by the Time-Cost of Experiment—prolonged use of the equipment may cost more on loan, or simply create wear that will require maintenance—and by Ease of Development—the more time is taken to develop the VE the more the hardware must be used or loaned or worn in testing.
Finally, Ease of Development may be the most subjective of these measures. Does one need to consider the experience of the researcher as they begin development as part of their training? Or should one only consider the time from the start of development to the end? Do different tools have different learning curves in VE design? Unfortunately, these questions, to the best of our knowledge, are yet to be answered.
We have then identified four fields for improvement when developing our own IVE approach to examining perceived qualities of urban environments. We must maximise Ease of Development and Level of Immersion, while minimising Money-Cost of Development and Time-Cost of Experiment.
Methods
Ethics
This research was approved by the University of Manchester School of Social Science Ethics Committee under review reference 2022-13711-24657.
Sampling
This study employed a convenience sampling method, with a sample of n = 34 participants, recruited between 10/10/2022 and 19/10/2022. Informed written consent was obtained for all participants. No demographic data was collected aside from their experience living in urban, suburban and rural environments, measured by number of years. Many more participants had lived in urban areas for most of their lives, with 18 having more than 20 years of experience living in urban areas, compared to just 1 participant with more than 20 years in suburban areas and no participants at all with more than 20 years in rural areas.
Fig 1 shows a breakdown of the demographic results. No other information was collected on the participants.
VR setup
Using the metrics outlined in the above section, an IVE was designed to best balance the four requirements specified. To maximise Ease of Development, we did not replicate a real-world location, nor did we hand-design a virtual location to resemble reality. Instead, we used a 3D street model procedurally generated by ESRI’s ArcGIS CityEngine [106]. This required minimal learning to use in its basic settings, though future iterations can be improved alongside understanding of the tool.
To minimise Money-Cost, the hardware used was a Meta Quest 2, one of the cheaper high-functioning HMD sets. It was chosen for its high specifications (1832x1920 pixel resolution per eye, 120Hz refresh rate) and its ubiquity, at time of writing making up 38% of VR devices used on Steam [107]. This decision was made with reproducibility in mind, as the most ubiquitous headset may be the easiest for future researchers to access. The hardware was integrated into the VE using Microsoft’s Mixed Reality ToolKit [108], a system-independent package that would theoretically allow the program to run on any VR hardware.
A questionnaire was designed for participants to answer as they walked through the environment, and to maximise Level of Immersion it was taken within the VE, so that no HMD-exiting break in presence would occur while answering questions.
The virtual environment
The VE, imported to Unity from CityEngine, consisted of a handful of city blocks abreast a main road with increasing urban density, such that buildings at one end of the road were much taller than those at the other end. A bird’s eye view can be seen in Fig 2.
CityEngine generated the city these blocks were part of through a procedural generation process based on the street network of Berlin, then generated the building outlines for each block and extruded buildings to a height and shape based on their distance from the centre of the city. The program then gives each building a facade texture, and projects satellite imagery onto the ground to represent grass or vegetation.
Three such environments were made, NoTrees with no trees whatsoever (those seen in Fig 2 are in fact satellite projections and part of the grass), SomeTrees with trees as they would normally be generated by the program, and MoreTrees with some buildings replaced with groups of trees. The three environments can be seen side-by-side in Fig 3, and a ground-level view in Fig 4.
An asset count reports the number of trees in each environment, as seen in Table 1.
The realism of the system was potentially limited by three disadvantages: (1) Sound was not incorporated in this system; (2) the photorealism was limited by the assets imported from CityEngine; and (3) there were no moving or interactable objects outside of the questionnaire. These factors may affect matching and plot elements of the [92] framework, but points (1) and (2) may also have added effects on the findings that are outside the scope of this paper.
Experiment design
The study itself was straight-forward. Participants would put on the headset with assistance from the researcher, then be instructed on how to use the interface to open a program. They would then open the in-built introduction program First Steps, which would walk them through the different buttons on their controllers and acclimatise them to the experience of being in VR. During this time the researcher would be monitoring them for signs of nausea or disorientation (e.g. sweating, imbalance, fidgeting with HMD) [104].
After the participant has had time to get used to the headset and the researcher was satisfied they were not at an obvious risk, they were instructed to open the study program and walk around to get a feel of the environment. Interaction outside of movement was limited to the slider and buttons of the questionnaire panel. When the participants pressed the grip of their controller to bring up the questionnaire, they could move the slider or press the buttons using a cursor based on motion control, and a button on the controller. Locomotion involved mixed methods: participants could physically walk about the room, up to the virtual boundary set up by the researcher to ensure their safety from obstacles and walls, or they could push a thumbstick on the Quest’s handheld controller to move in the direction they were facing. The former method is known as ‘real walking’ or ‘real locomotion’ and is considered the locomotion method with the highest interaction fidelity, which is associated with immersion [109]. The use of mixed locomotion is uncommon, but [110] found that it was preferred by participants in a musical composition context for its allowance of control. Participants could utilise both forms of motion to explore their surroundings however they were most comfortable, though the physical walking was much more limited in range.
The task participants were given was to consider the tallest buildings and the shortest buildings, the nicest areas and the least nice areas, and, when they were ready, press the button on their controller that brings up the questionnaire. They could take as long as they wanted to end the task and begin the questionnaire, though they were encouraged to begin if more than 10 minutes had passed.
Participants would then answer a questionnaire which asked them to rate the environment on a series of qualities, on a scale of 1 to 7. These qualities were safety, greenness, density, beauty, building height, and road width.
Once all questions were answered, participants were moved to the next environment (e.g. from NoTrees to SomeTrees) and the process would be repeated.
Participant monitoring
Virtual experiments including navigation tasks with unsupervised samples, for example those performed remotely, have shown little difference in completion and correctness of answers [111] and performance [112]. In order to ensure participant safety while in VR, this experiment employed a semi-supervised approach, in which participants were observed externally but their in-VR behaviour was not monitored.
The researcher stayed in the room and continually observed participants for any signs of disorientation, if any were found the participant would be asked if they needed to sit down or take a break.
The researcher could not see what the participants were seeing, as the Quest 2’s ‘casting’ feature was found to create latency that would influence perceptions of the environment and introduce unwanted physical symptoms [113], but regularly checked in on participant progress. Each participant had up to 30 minutes total, including the tutorial, environments, and questionnaires. They were encouraged to leave the tutorial after 10 minutes if they showed no signs of disorientation and had not already asked to move on themselves. This left at most 20 minutes per participant for the experiment.
Participants had to trigger the questionnaire themselves, ending the task. Some participants employed a think aloud approach, informing the researcher of their progress in the task, some also chose to engage with the real walking locomotion method, both of these factors giving the researcher a clear start and end point for the tasks between the questionnaires.
Questionnaire
The questionnaire contains six questions, each answerable on a scale of 1 to 7. Fig 5 shows the change in view when the questionnaire is triggered.
The questions were phrased plainly, as in Fig 5, they simply read “How beautiful is this area?”, “How tall are the buildings in this area?”, etc., followed by a reminder of the 1–7 scale.
The qualities assessed were chosen based on existing research of urban environment perception. Safety was chosen for its link to use of urban green space (see [19]). Beauty for its link to wellbeing [27]. Density for its association with wellbeing as well as its link to greenery [38]. Road width and building height to examine the effect of greenery on objective measures, but also for their associations with walkability and wellbeing (see [38,114,115]). Finally, greenery is included to compare the changes in ratings of greenness against the actual objective difference in number of trees.
Pilot study
Before the experiment was carried out, a pilot study tested the feasibility and use of the approach with n = 11 participants. The study used an earlier version of the same program, the one difference was the questionnaire not yet being taken within the VE. Instead, the researcher gave participants the choice of either being read the questions out loud and answering out loud, or waiting to answer the questionnaire directly after they had seen each environment. Most participants chose the first option.
The pilot also asked participants to rate their experience living in urban, suburban, and rural areas on a 7-point scale from ‘None at all’ to ‘A great deal’, rather than reporting years lived.
The pilot found significant causal connections between the presence and number of trees and participants’ ratings of greenness, beauty, and, to a small extent, road width, but was limited by its small sample size and out-of-VR questionnaire.
Findings
The outcomes of interest for this study were perceived safety, perceived beauty, perceived density, perceived greenness perceived building height, and perceived road width.
For a better idea on the structure of the data, a given participant’s responses can be seen in Table 2.
Fig 6 shows the mean values and standard deviations for each outcome variable across the three environments.
An already visible trend seems to show that increasing the number of trees in an environment will increase ratings of perceived safety, beauty, and greenness and reduce perceptions of road width, but will also increase perceptions of density and building height. These trends are small for most variables, staying mostly within the 4–5 range of ratings, and showing that the MoreTrees environment had a somewhat more notable impact on the ratings than the SomeTrees environment. For perceived beauty and greenness, the boxplots show a large increase in ratings between NoTrees and SomeTrees. Ratings of beauty begin to diminish by MoreTrees, but greenness continues near-linearly.
A multivariate single analysis of variance model was fitted to determine the impact of the environment on all of these outcomes collectively, and univariate linear regression models were fitted to evaluate its effect on the outcomes individually.
Table 3 shows the results of the multivariate single analysis of variance model, which measures the contribution made by the environment on the variance of the outcome variables. A Pillai trace value closer to 1.0 indicates a larger effect of the variable on the model. Hotelling-Lawley and Roy’s Largest Root work similarly, with larger values indicating greater effect. Wilk’s Lambda is inverse, a value closer to 0.0 indicates a larger effect. These results together show that the number of trees has a significant impact on the outcome variables.
Each outcome was then fitted against the environment variable in a univariate linear regression. Fig 7 shows the ratings regardless of environment were generally above median for all outcomes, and that the absence of trees seemed to have a large impact on responses than the number of trees once they were present.
Detailed linear model results table (top) and significant findings of linear regression fitted to environment against all variables (bottom).
Focus should then shift to the lived experience variables, the participants’ years lived in urban, suburban, and rural environments. Linear regression models were fitted against the experience variable for each outcome. Fig 8 shows the significant findings with the largest coefficients from each model.
Detailed results of effects of background variables on perceptions (top) and visualisation of significant findings (bottom).
Many results did not have a p-value <0.05, likely reflecting the very small proportion of participant lived experience for suburban and rural areas.
The impact of number of trees when controlling for years lived in urban environments is considerably lessened for safety, density, and road width, so much so that they have no significant correlation between environment and outcome when controlling for lived experience.
Building heights, beauty, and greenness, however, are significantly impacted by number of trees even when lived experience is controlled for. Fig 9 shows that significant findings from the initial linear model are unchanged when controlling for years lived in urban environments, with a difference in standard error as small as +-0.01.
Side-by-side: Significant results of linear model controlled for years lived experience (right) and comparison to uncontrolled levels (left).
However, when controlling for years lived and for environment, the relationship between perceived greenness and perceived qualities becomes one of predictor and outcome. Table 4 shows the impact of perceived greenness on perceived beauty, safety, and density.
In fact, beauty is impacted significantly by perceived greenness even when controlling for objective number of trees, results of a linear prediction model fitted against perceived greenness and perceived beauty with controls for number of trees and years lived in urban areas can be seen in Table 5. The full effect of perceived greenness on outcome variables safety, beauty, and density is presented in Fig 10.
We can further evaluate the importance and significance of perceived greenness as a predictor by comparing the p-values of linear models fitted for perceived greenness against each of the other outcome variables, compared to those for number of trees against outcomes. Fig 11 shows that for safety, density, and beauty, perceived greenness is the most significant predictor when controlling for lived urban experience and environment. For building heights, abundance of trees is the most significant predictor, as is presence of trees for road width. Note, though, that all predictors for building height and road width have a p-value greater than 0.05, as do environmental predictors for safety and density.
Finally, random forest prediction models were generated to explore variable importance for predicting each of the outcomes. Table 6 shows each outcome’s most important variable that is not in itself an outcome variable. Table 7 shows the most important variables including other outcome variables, to help identify correlations within the outcomes themselves.
Discussion and conclusions
This study has aimed to use IVE technology to explore the impact of added greenery on perceptions of an urban space. Our findings show that the inclusion of trees greatly increased ratings of greenness and beauty in the virtual environment, and very slightly increased ratings of density and building heights, two factors associated with negative elements of urban environments.
Findings also show a strong relationship between years lived in urban areas and ratings of safety, building heights and road width. 6+ years lived in urban areas significantly increase perceived safety, implying those with less urban experience may find the urban environment less safe. <1 year urban experience significantly increased perceived building height, but 11+ years also increased it. 6–20 years suburban experience significantly reduced perceived road width, as did 11–20 years urban. The inconsistencies in these results may be a product of the unrepresentative sample. The findings do suggest that individual experience in urban environments may impact what constitutes a ‘4 out of 7’ road width, implying that individual contexts and backgrounds are a large factor of environment perception.
When controlling for the effect of years lived in urban areas, the impact of added trees was lessened considerably, leaving perceived beauty as the only outcome variable still significantly impacted by trees alone. However, when examining the effect of perceived greenness—meaning the impact of the rating of greenness on the other outcome variables—beauty, density, and safety were all significantly predicted, even when accounting for years lived in urban areas. While perceived greenness is strongly impacted by the presence and abundance of trees, its relationship to beauty is significant even when number of trees is controlled for.
The virtual environment was successful in reducing time-cost and ease of development, as it required only a basic understanding of CityEngine and Unity to develop. However, the money-cost of VE development would be considered high. While Unity is free, CityEngine is proprietary software that not all researchers could afford.
Further evaluation is required on the system’s Level of Immersion, but it can be roughly assessed against [92]‘s six-point framework, for which it comes out reasonably inclusive (1832x1920 resolution per eye, 120Hz refresh rate, no lag, no cables, light headset), surrounding (wide field of view, accommodation for depth perception), and matching (head-tracking, physical walking movement available), though its vividness and plot could be considered lacking (no intentional plot included, shadows but no dynamic objects, high resolution but no subjective ratings of realism taken).
This paper’s findings present two opportunities for further exploration in the perceptions of urban space, the first being the isolation of perceived greenness as a factor of urban perceptions, either wholly or partly independent of objective greenness. The second is the use of IVEs for investigation of urban perceptions, with limited hardware and minimal time-cost, that can be easily reproduced as a testing tool for future study.
Further research could focus more directly on the effects of lived experience in different levels of urbanisation on perceptions of the urban environment, by including in its sample more participants with rural backgrounds. Future iterations of this study should include more robust questionnaire design, with definitions of terms and examples of the minimum and maximum ratings in each category.
This paper concludes that, while further study is needed, both the introduction and increase in quantity of urban road-side greenery will impact perceptions of beauty in an urban environment, but that the perception of greenness is in itself a separate predictor for perceived beauty.
Acknowledgments
Thanks to the Luxembourg Institute of Socio-Economic research for their advisement and guidance. Special thanks to Will Hain for his support.
References
- 1.
United Nations D of E& SAPD. World Urbanization Prospects: the 2018 Revision. United Nations; 2019.
- 2. Bardhan R, Kurisu K, Hanaki K. Does compact urban forms relate to good quality of life in high density cities of India? Case of Kolkata. Cities. 2015;48:55–65.
- 3. Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am J Health Promot. 2003;18[1]:47–57. pmid:13677962
- 4. Ewing R, Brownson RC, Berrigan D. Relationship between urban sprawl and weight of United States youth. Am J Prev Med. 2006;31[6]:464–74. pmid:17169708
- 5. Jerrett M, McConnell R, Chang CR, Wolch J, Reynolds K, Lurmann F, et al. Automobile traffic around the home and attained body mass index: a longitudinal cohort study of children aged 10–18 years. Prev Med. 2010;50:S50–8. pmid:19850068
- 6. Sturm R, Cohen DA. Suburban sprawl and physical and mental health. Public Health. 2004;118[7]:488–96. pmid:15351221
- 7. Timperio A, Salmon J, Telford A, Crawford D. Perceptions of local neighbourhood environments and their relationship to childhood overweight and obesity. Int J Obes. 2005;29[2]:170–5. pmid:15583699
- 8. Waśniewski K. The Puzzle of Urban Density And Energy Consumption. 2021.
- 9. Taylor L, Hochuli DF. Creating better cities: how biodiversity and ecosystem functioning enhance urban residents’ wellbeing. Urban Ecosyst. 2015;18:747–62.
- 10. Alcock I, White MP, Wheeler BW, Fleming LE, Depledge MH. Longitudinal effects on mental health of moving to greener and less green urban areas. Environ Sci Technol. 2014;48[2]:1247–55. pmid:24320055
- 11. Beyer KM, Kaltenbach A, Szabo A, Bogar S, Nieto FJ, Malecki KM. Exposure to neighborhood green space and mental health: evidence from the survey of the health of Wisconsin. Int J Environ Res Public Health. 2014;11[3]:3453–72. pmid:24662966
- 12. Brown SC, Lombard J, Wang K, Byrne MM, Toro M, Plater-Zyberk E, et al. Neighborhood Greenness and Chronic Health Conditions in Medicare Beneficiaries. Am J Prev Med. 2016;51[1]:78–89. pmid:27061891
- 13. Fong KC, Hart JE, James P. A review of epidemiologic studies on greenness and health: updated literature through 2017. Curr Environ Health Rep. 2018;5:77–87. pmid:29392643
- 14. Gascon M, Triguero-Mas M, Martínez D, Dadvand P, Forns J, Plasència A, et al. Mental health benefits of long-term exposure to residential green and blue spaces: a systematic review. Int J Environ Res Public Health. 2015;12[4]:4354–79. pmid:25913182
- 15. James P, Banay RF, Hart JE, Laden F. A review of the health benefits of greenness. Curr Epidemiol Rep. 2015;2:131–42. pmid:26185745
- 16. Kweon BS, Sullivan WC, Wiley AR. Green common spaces and the social integration of inner-city older adults. Environ Behav. 1998;30[6]:832–58.
- 17. Maas J, Verheij RA, Groenewegen PP, De Vries S, Spreeuwenberg P. Green space, urbanity, and health: how strong is the relation? J Epidemiol Community Health. 2006;60[7]:587–92. pmid:16790830
- 18. Mitchell R, Popham F. Effect of exposure to natural environment on health inequalities: an observational population study. The lancet. 2008;372[9650]:1655–60. pmid:18994663
- 19. Núñez M, Suzman L, Maneja R, Bach A, Marquet O, Anguelovski I, et al. Gender and sex differences in urban greenness’ mental health benefits: A systematic review. Health Place. 2022;76:102864. pmid:35853366
- 20. Roemmich JN, Epstein LH, Raja S, Yin L, Robinson J, Winiewicz D. Association of access to parks and recreational facilities with the physical activity of young children. Prev Med. 2006;43[6]:437–41. pmid:16928396
- 21. Stigsdotter UK, Ekholm O, Schipperijn J, Toftager M, Kamper-Jørgensen F, Randrup TB. Health promoting outdoor environments—Associations between green space, and health, health-related quality of life and stress based on a Danish national representative survey. Scand J Public Health. 2010;38[4]:411–7. pmid:20413584
- 22. Takano T, Nakamura K, Watanabe M. Urban residential environments and senior citizens’ longevity in megacity areas: the importance of walkable green spaces. J Epidemiol Community Health. 2002;56[12]:913–8. pmid:12461111
- 23. Van den Berg AE, Maas J, Verheij RA, Groenewegen PP. Green space as a buffer between stressful life events and health. Soc Sci Med. 2010;70[8]:1203–10. pmid:20163905
- 24. White MP, Alcock I, Wheeler BW, Depledge MH. Would you be happier living in a greener urban area? A fixed-effects analysis of panel data. Psychol Sci. 2013;24[6]:920–8. pmid:23613211
- 25. Wolch J, Jerrett M, Reynolds K, McConnell R, Chang R, Dahmann N, et al. Childhood obesity and proximity to urban parks and recreational resources: a longitudinal cohort study. Health Place. 2011;17[1]:207–14. pmid:21075670
- 26. Poortinga W. Perceptions of the environment, physical activity, and obesity. Soc Sci Med. 2006;63[11]:2835–46. pmid:16952415
- 27. Seresinhe CI, Preis T, MacKerron G, Moat HS. Happiness is greater in more scenic locations. Sci Rep. 2019;9[1]:1–11.
- 28. Sugiyama T, Leslie E, Giles-Corti B, Owen N. Associations of neighbourhood greenness with physical and mental health: do walking, social coherence and local social interaction explain the relationships? J Epidemiol Community Health. 2008;62[5]:e9–e9. pmid:18431834
- 29. Rapoport A. Toward a redefinition of density. Environ Behav. 1975;7[2]:133–58.
- 30.
Byrne J, Sipe N. Green and open space planning for urban consolidation–A review of the literature and best practice. Urban Research Program, Griffith University; 2010.
- 31. Jim CY, Shan X. Socioeconomic effect on perception of urban green spaces in Guangzhou, China. Cities. 2013;31:123–31.
- 32. Lo AY, Jim CY. Differential community effects on perception and use of urban greenspaces. Cities. 2010;27[6]:430–42.
- 33. Schindler M, Le Texier M, Caruso G. Spatial sorting, attitudes and the use of green space in Brussels. Urban For Urban Green. 2018;31:169–84.
- 34. Schindler M, Le Texier M, Caruso G. How far do people travel to use urban green space? A comparison of three European cities. Appl Geogr. 2022;141:102673.
- 35. Birenboim A, Dijst M, Ettema D, de Kruijf J, de Leeuw G, Dogterom N. The utilization of immersive virtual environments for the investigation of environmental preferences. Landsc Urban Plan. 2019;189:129–38.
- 36. Birenboim A, Ben-Nun Bloom P, Levit H, Omer I. The Study of Walking, Walkability and Wellbeing in Immersive Virtual Environments. Int J Environ Res Public Health. 2021;18[2]:364. pmid:33418896
- 37. Fisher-Gewirtzman D. Perception of density by pedestrians on urban paths: An experiment in virtual reality. J Urban Des. 2018;23[5]:674–92.
- 38. Trossman-Haifler Y, Fisher-Gewirtzman D. Urban Well-Being in Dense Cities-The influence of densification strategies, experiment in virtual reality. RE Anthr Des Age Hum. 2020;1:661–70.
- 39. Blascovich J, Loomis J, Beall AC, Swinth KR, Hoyt CL, Bailenson JN. Immersive virtual environment technology as a methodological tool for social psychology. Psychol Inq. 2002;13[2]:103–24.
- 40. Fox J, Arena D, Bailenson JN. Virtual reality: A survival guide for the social scientist. J Media Psychol. 2009;21[3]:95–113.
- 41. Loomis JM, Blascovich JJ, Beall AC. Immersive virtual environment technology as a basic research tool in psychology. Behav Res Methods Instrum Comput. 1999;31[4]:557–64. pmid:10633974
- 42. Ruotolo F, Maffei L, Di Gabriele M, Iachini T, Masullo M, Ruggiero G, et al. Immersive virtual reality and environmental noise assessment: An innovative audio–visual approach. Environ Impact Assess Rev. 2013;41:10–20.
- 43. Smith JW. Immersive virtual environment technology to supplement environmental perception, preference and behavior research: a review with applications. Int J Environ Res Public Health. 2015;12[9]:11486–505. pmid:26378565
- 44. Wilson PN, Foreman N, Tlauka M. Transfer of spatial information from a virtual to a real environment. Hum Factors. 1997;39[4]:526–31.
- 45. Patterson Z, Darbani JM, Rezaei A, Zacharias J, Yazdizadeh A. Comparing text-only and virtual reality discrete choice experiments of neighbourhood choice. Landsc Urban Plan. 2017;157:63–74.
- 46. Xu C, Oberman T, Aletta F, Tong H, Kang J. Ecological Validity of Immersive Virtual Reality (IVR) Techniques for the Perception of Urban Sound Environments. Acoustics. 2021;3[1]:11–24.
- 47.
Alexandrovsky D, Putze S, Bonfert M, Höffner S, Michelmann P, Wenig D, et al. Examining design choices of questionnaires in VR user studies. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020. p. 1–21.
- 48.
Schwind V, Knierim P, Haas N, Henze N. Using presence questionnaires in virtual reality. In: Proceedings of the 2019 CHI conference on human factors in computing systems. 2019. p. 1–12.
- 49. Taylor L, Hochuli DF. Defining greenspace: Multiple uses across multiple disciplines. Landsc Urban Plan. 2017;158:25–38.
- 50. Lachowycz K, Jones AP. Greenspace and obesity: a systematic review of the evidence. Obes Rev. 2011;12[5]:e183–9. pmid:21348919
- 51. Cohen DA, Ashwood JS, Scott MM, Overton A, Evenson KR, Staten LK, et al. Public parks and physical activity among adolescent girls. Pediatrics. 2006;118[5]:e1381–9. pmid:17079539
- 52. Cohen DA, McKenzie TL, Sehgal A, Williamson S, Golinelli D, Lurie N. Contribution of public parks to physical activity. Am J Public Health. 2007;97[3]:509–14. pmid:17267728
- 53. Coombes E, Jones AP, Hillsdon M. The relationship of physical activity and overweight to objectively measured green space accessibility and use. Soc Sci Med. 2010;70[6]:816–22. pmid:20060635
- 54. Evenson KR, Wen F, Hillier A, Cohen DA. Assessing the contribution of parks to physical activity using GPS and accelerometry. Med Sci Sports Exerc. 2013;45[10]:1981.
- 55. Roux AVD, Evenson KR, McGinn AP, Brown DG, Moore L, Brines S, et al. Availability of recreational resources and physical activity in adults. Am J Public Health. 2007;97[3]:493–9. pmid:17267710
- 56. Barton J, Pretty J. What is the best dose of nature and green exercise for improving mental health? A multi-study analysis. Environ Sci Technol. 2010;44[10]:3947–55. pmid:20337470
- 57. Gilchrist K, Brown C, Montarzino A. Workplace settings and wellbeing: Greenspace use and views contribute to employee wellbeing at peri-urban business sites. Landsc Urban Plan. 2015;138:32–40.
- 58. Grahn P, Stigsdotter UK. The relation between perceived sensory dimensions of urban green space and stress restoration. Landsc Urban Plan. 2010;94[3–4]:264–75.
- 59. Ulrich RS. Natural versus urban scenes: Some psychophysiological effects. Environ Behav. 1981;13[5]:523–56.
- 60. Ulrich RS, Simons RF, Losito BD, Fiorito E, Miles MA, Zelson M. Stress recovery during exposure to natural and urban environments. J Environ Psychol. 1991;11[3]:201–30.
- 61. Maas J, Verheij RA, de Vries S, Spreeuwenberg P, Schellevis FG, Groenewegen PP. Morbidity is related to a green living environment. J Epidemiol Community Health. 2009;63[12]:967–73. pmid:19833605
- 62. Hu Z, Liebens J, Rao KR. Linking stroke mortality with air pollution, income, and greenness in northwest Florida: an ecological geographical study. Int J Health Geogr. 2008;7[1]:1–22. pmid:18452609
- 63. Wolch JR, Byrne J, Newell JP. Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’. Landsc Urban Plan. 2014;125:234–44.
- 64. Curran W, Hamilton T. Just green enough: Contesting environmental gentrification in Greenpoint, Brooklyn. Local Environ. 2012;17[9]:1027–42.
- 65. Gunnarsson B, Knez I, Hedblom M, Sang ÅO. Effects of biodiversity and environment-related attitude on perception of urban green space. Urban Ecosyst. 2017;20:37–49.
- 66. Glaesener ML, Caruso G. Neighborhood green and services diversity effects on land prices: Evidence from a multilevel hedonic analysis in Luxembourg. Landsc Urban Plan. 2015;143:100–11.
- 67. Dallimer M, Irvine KN, Skinner AM, Davies ZG, Rouquette JR, Maltby LL, et al. Biodiversity and the feel-good factor: understanding associations between self-reported human well-being and species richness. BioScience. 2012;62[1]:47–55.
- 68. Shwartz A, Turbé A, Simon L, Julliard R. Enhancing urban biodiversity and its influence on city-dwellers: An experiment. Biol Conserv. 2014;171:82–90.
- 69. Knez I, Thorsson S. Influences of culture and environmental attitude on thermal, emotional and perceptual evaluations of a public square. Int J Biometeorol. 2006;50:258–68. pmid:16541241
- 70. Knez I, Thorsson S. Thermal, emotional and perceptual evaluations of a park: Cross-cultural and environmental attitude comparisons. Build Environ. 2008;43[9]:1483–90.
- 71. Ward Thompson C, Aspinall P, Bell S, Findlay C. “It gets you away from everyday life”: local woodlands and community use—what makes a difference? Landsc Res. 2005;30[1]:109–46.
- 72. Jim CY, Shan X. Socioeconomic effect on perception of urban green spaces in Guangzhou, China. Cities. 2013;31:123–31.
- 73. Bixler RD, Floyd MF. Nature is scary, disgusting, and uncomfortable. Environ Behav. 1997;29[4]:443–67.
- 74. Westover TN. Perceptions of crime and safety in three Midwestern parks. Prof Geogr. 1985;37[4]:410–20.
- 75. Churchman A. Disentangling the concept of density. J Plan Lit. 1999;13[4]:389–411.
- 76.
Pont MB, Haupt P. Spacematrix. Space, density and urban form. NAi Publishers; 2010.
- 77. McCrea R, Shyy TK, Stimson R. What is the strength of the link between objective and subjective indicators of urban quality of life? Appl Res Qual Life. 2006;1[1]:79–96.
- 78. Navarrete-Hernandez P, Mace A, Karlsson J, Holman N, Zorloni DA. Delivering higher density suburban development: The impact of building design and residents’ attitudes. Urban Stud. 2022 Oct;59[13]:2801–20.
- 79. Nishio S, Ito F. Statistical validation of utility of head-mounted display projection-based experimental impression evaluation for sequential streetscapes. Environ Plan B Urban Anal City Sci. 2020;47[7]:1167–83.
- 80. Rossetti T, Hurtubia R. An assessment of the ecological validity of immersive videos in stated preference surveys. J Choice Model. 2020;34:100198.
- 81. Luigi M, Massimiliano M, Aniello P, Gennaro R, Virginia PR. On the Validity of Immersive Virtual Reality as Tool for Multisensory Evaluation of Urban Spaces. Energy Procedia. 2015;78:471–6.
- 82. Feng Z, González VA, Amor R, Lovreglio R, Cabrera-Guerrero G. Immersive virtual reality serious games for evacuation training and research: A systematic literature review. Comput Educ. 2018;127:252–66.
- 83. Kinateder M, Warren WH. Social influence on evacuation behavior in real and virtual environments. Front Robot AI. 2016;3:43.
- 84. Kobes M, Helsloot I, de Vries B, Post J. Exit choice,(pre-) movement time and (pre-) evacuation behaviour in hotel fire evacuation—Behavioural analysis and validation of the use of serious gaming in experimental research. Procedia Eng. 2010;3:37–51.
- 85. Li H, Zhang J, Xia L, Song W, Bode NW. Comparing the route-choice behavior of pedestrians around obstacles in a virtual experiment and a field study. Transp Res Part C Emerg Technol. 2019;107:120–36.
- 86. Wang TH, Wu WH, Shen L, Cheng CK. Exploring the validity of using immersive virtual reality technique on perceived crowding of recreational environment. Landsc Ecol Eng. 2021;17:299–308.
- 87.
Thalmann D, Lee J, Thalmann NM. An evaluation of spatial presence, social presence, and interactions with various 3D displays. In: Proceedings of the 29th International Conference on Computer Animation and Social Agents. 2016. p. 197–204.
- 88. Arellana J, Garzón L, Estrada J, Cantillo V. On the use of virtual immersive reality for discrete choice experiments to modelling pedestrian behaviour. J Choice Model. 2020;37:100251.
- 89. Higuera-Trujillo JL, Maldonado JLT, Millán CL. Psychological and physiological human responses to simulated and real environments: A comparison between Photographs, 360 Panoramas, and Virtual Reality. Appl Ergon. 2017;65:398–409.
- 90. Feng Y, Duives DC, Hoogendoorn SP. Development and evaluation of a VR research tool to study wayfinding behaviour in a multi-story building. Saf Sci. 2022 Mar;147:105573.
- 91. Orland B, Budthimedhee K, Uusitalo J. Considering virtual worlds as representations of landscape realities and as tools for landscape planning. Landsc Urban Plan. 2001;54[1–4]:139–48.
- 92. Slater M, Wilbur S. A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence Teleoperators Virtual Environ. 1997;6[6]:603–16.
- 93. de Kort YA, Meijnders AL, Sponselee AA, IJsselsteijn WA. What’s wrong with virtual trees? Restoring from stress in a mediated environment. J Environ Psychol. 2006;26[4]:309–20.
- 94. Gorini A, Capideville CS, De Leo G, Mantovani F, Riva G. The role of immersion and narrative in mediated presence: the virtual hospital experience. Cyberpsychology Behav Soc Netw. 2011;14[3]:99–105. pmid:20649451
- 95. Slater M. How colorful was your day? Why questionnaires cannot assess presence in virtual environments. Presence. 2004;13[4]:484–93.
- 96.
Kinateder M, Ronchi E, Nilsson D, Kobes M, Müller M, Pauli P, et al. Virtual reality for fire evacuation research. In: 2014 federated conference on computer science and information systems. IEEE; 2014. p. 313–21.
- 97. Stanney KM, Mourant RR, Kennedy RS. Human factors issues in virtual environments: A review of the literature. Presence. 1998;7[4]:327–51.
- 98. Daniel TC, Meitner MM. Representational validity of landscape visualizations: the effects of graphical realism on perceived scenic beauty of forest vistas. J Environ Psychol. 2001;21[1]:61–72.
- 99. Li C, Liang W, Quigley C, Zhao Y, Yu LF. Earthquake safety training through virtual drills. IEEE Trans Vis Comput Graph. 2017;23[4]:1275–84. pmid:28129163
- 100. Meng F, Zhang W. Way-finding during a fire emergency: an experimental study in a virtual environment. Ergonomics. 2014;57[6]:816–27. pmid:24697193
- 101. Boletsis C, Cedergren JE. VR locomotion in the new era of virtual reality: an empirical comparison of prevalent techniques. Adv Hum-Comput Interact. 2019;2019.
- 102. Schneider S, Bengler K. Virtually the same? Analysing pedestrian behaviour by means of virtual reality. Transp Res Part F Traffic Psychol Behav. 2020;68:231–56.
- 103. Chignell M, Matulis H, Zhang B, Urakami J, Miyafuji S, Li Z, et al. Immersiveness and Perceptibility of Convex and Concave Displays. Proc Hum Factors Ergon Soc Annu Meet. 2019;63[1]:396–400.
- 104. Sharples S, Cobb S, Moody A, Wilson JR. Virtual reality induced symptoms and effects (VRISE): Comparison of head mounted display (HMD), desktop and projection display systems. Displays. 2008;29[2]:58–69.
- 105. Deb S, Carruth DW, Sween R, Strawderman L, Garrison TM. Efficacy of virtual reality in pedestrian safety research. Appl Ergon. 2017;65:449–60. pmid:28318502
- 106. ESRI. ESRI ArcGIS CityEngine. 2023.
- 107. Valve Corporation. Steam Hardware & Software Survey: June 2024 [Internet]. 2024 Jun. Available from: https://store.steampowered.com/hwsurvey/Steam-Hardware-Software-Survey-Welcome-to-Steam.
- 108. Microsoft. Mixed Reality ToolKit. 2023.
- 109. McMahan RP, Alon AJD, Lazem S, Beaton RJ, Machaj D, Schaefer M, et al. Evaluating natural interaction techniques in video games. IEEE Symp 3D User Interfaces 3DUI. 2010 Mar;11–4.
- 110. Tomasetti M, Turchet L. Handheld controller-based locomotion in Virtual Reality as an approach to interactive music composition: insights from composers’ preferences. Digit Creat. 2024;1–25.
- 111.
Mottelson A, Hornbæk K. Virtual reality studies outside the laboratory. In: Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology [Internet]. New York, NY, USA: Association for Computing Machinery; 2017. [VRST ‘17]. Available from: https://doi.org/10.1145/3139131.3139141.
- 112. Huber B, Gajos KZ. Conducting online virtual environment experiments with uncompensated, unsupervised samples. PLOS ONE. 2020 Jan;15[1]:1–17. pmid:31999696
- 113. Stauffert JP, Niebling F, Latoschik ME. Latency and cybersickness: Impact, causes, and measures. A review. Front Virtual Real. 2020;1:582204.
- 114. Anciaes PR, Jones P, Mindell JS. Community severance: where is it found and at what cost? Transp Rev. 2016;36[3]:293–317.
- 115.
Appleyard D, GERSON MS, LIN℡L M. Livable Streets. California University Press, Berkeley; 1981.