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Article

Street Vibrancy and Outdoor Activities under COVID-19 Psychological Distress: Lessons from Hong Kong

by
Mirna Zordan
1,*,
Jin Yeu Tsou
2 and
Hao Huang
2
1
School of Design, Southern University of Science and Technology, Shenzhen 518055, China
2
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(10), 1896; https://doi.org/10.3390/land12101896
Submission received: 9 September 2023 / Revised: 26 September 2023 / Accepted: 2 October 2023 / Published: 10 October 2023
(This article belongs to the Special Issue Impacts of the COVID-19 Pandemic on Urban Planning, Design, and Management)
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Abstract

:
The COVID-19 pandemic has had a distinctive impact on Hong Kong, especially given the city’s prior experience with the SARS outbreak. The resulting psychological distress has been exacerbated by Hong Kong’s extreme density and compactness, which places residents in proximity on the streets searching for outdoor activities. Streets are a precious source of space for social interactions, but, unfortunately, the pandemic has forced them to empty, leading to increased distress and challenging the psychological well-being of the urban population. In this study, we explore street vibrancy patterns in terms of outdoor activities, here addressed through a decision-making psychological perspective as volitional behaviors determined by psychological factors and essential for well-being, in one of the densest neighborhoods in Hong Kong. We statistically analyzed behavioral monitoring data in relation to spatial and morphological characteristics of their environments under COVID-19 psychological distress. The results highlighted the relevance of specific parcels of the streets acting as clusters and vibrancy initiators, stressing their significance in terms of affective and cognitive inputs determining context-based outdoor activities. The decision-making psychological perspective adopted here to address outdoor activities has relevant implications for design and planning strategies for post-pandemic scenarios, for psychological well-being, and for the future of urban density.

1. Introduction

The COVID-19 pandemic has had a negative impact on the mental health of urban populations globally [1,2,3,4,5,6]. The resulting psychological distress is characterized by an inability to cope with stressors and emotional turmoil [7,8,9]. This distress has been strongly linked to the COVID-19 pandemic [5,6,10,11] and is closely associated with risk perception and stress factors [7,12]. The health, safety, and well-being of individuals and communities are being impacted by the ongoing public health emergency [13]. In Hong Kong, COVID-19 has had a profound psychological impact due mostly to the earlier trauma of the SARS epidemic in 2003. During SARS, mortality rate reached an extremely high level of 17% [14], which left an indelible impression on the heart of Hong Kong people. The limited knowledge about transmission and lethality of the SARS virus in Hong Kong’s dense urban areas caused significant losses for healthcare workers and residents. Although the outbreak was short-lived due to the virus’s reduced infectivity at high temperatures, it greatly influenced the community’s perception of collective health. When the COVID-19 pandemic hit the city, fears and memories of the losses caused by earlier viruses were reignited. Moreover, COVID-19 resulting in extreme quarantine restrictions further compromised the psychological stability of the population, combined with vaccination in the region not yet being available at the time of this study and until 2021, exacerbating the situation. However, the psychological health of Hong Kong’s urban population was already precarious before the COVID-19 pandemic. With density at its highest, Hong Kong’s population is 6819.453 persons per square km (as of 1 July 2022) [15] and it is on a continuous growth, reaching extremes also in terms of intensity. Intensity, seen as ‘immaterial density’ [16], reaches its highest in Hong Kong, with a maximum pedestrian volume of 78 persons per minute, or 4680 persons/h according to data collected by Zacharias [17] by measuring the flow (p/m/s), walking speed, displacement from the outer edge, and density per hour in the sampled streets. Most of Hong Kong’s population is forced to live in precarious and small housing conditions, pushing residents to look for some fresh air and outdoor activities elsewhere. With an average of 1.6 sqm/capita in Hong Kong and 1.48 sqm/capita in Yau Tsim Mong [18], countable open space per person in some areas reaches extremely low ratios. In Mong Kok the value is 0.6 sqm/capita [19].

1.1. Outdoor Activities for Psychological Health

Outdoor activities have been extensively studied in urban studies as an expression of behavior benefitting the physical, social, and psychological well-being of urban populations. In recent years, many studies have shown that outdoor activities can increase the psychological health and well-being of urban populations, and can help them better cope with mental fatigue, anxiety, anger, sadness, and other emotions [20,21,22]. For example, outdoor activities such as walking [23,24] can effectively reduce psychological anxiety and improve mood. Some studies have proposed that outdoor engagement supports perceived social, mental, and physical well-being [25], and outdoor recreation helps build resilience to changes in subjective well-being before and during global crises like the COVID-19 pandemic [26]. In brief, the psychological health and well-being of urban populations is intricately associated with outdoor activities.
Outdoor activities are affected by space configuration [27,28]. Some spatial settings promote outdoor activities, while others hinder people’s activity plans. In many urban areas, people tend to congregate in specific locations, for example, high street shops [28]. Previous studies have shown that the openness of a space and people’s familiarity with that space [29,30], a relaxed and happy environment to go out [31], neighborhood support [32,33], land use diversity [34], street layout and connectivity [35,36], access to recreational facilities [37], public health and air quality [38], aesthetics [33,34,36], and transportation [36] can promote more frequent outdoor activities. Given the importance of outdoor activities for the psychological and social well-being of urban populations, many scholars have been studying the criteria that support the socio-spatial vitality of urban spaces [39,40,41,42,43,44,45].
However, previous studies seemed to relate outdoor activities to either small scale spatial configurations, emotional and place attachment perspectives, or planning approach-related variables, while a comprehensive element anchoring spatial and vibrancy characteristics could become a potential key factor for future city design and planning. Moreover, how urban spaces can be related to outdoor activities when users experience major psychological distress is still unexplored.
In fact, the spread of COVID-19 has affected the way people use and perceive urban environments and public space [46,47,48,49]. Emerging questions on the future of public space, design solutions, and policy implications are the focus of current debates in the field of urban studies [46,50].

1.2. The Hong Kong Case

Given the compactness of Hong Kong’s urban fabric and due to the unbalanced distribution of urban public space, most of the public space is far away from the densely populated low-income communities [51]; this condition seriously affects the outdoor activities of the residents. The most accessible space to engage in outdoor activities are the streets.
The potential impact of urban streets on people’s physical, social, and psychological well-being is an important issue in high-density and multi-layered urban environments [52], especially where there is a lack of public space culture [53]. Densification related to demographic growth and the scarcity of available land in these environments mean that accessible open public space is limited in both quantity and quality, and is often found lacking in design, distribution, and management. Being extremely important for the well-being of urban populations, especially in Hong Kong, streets fulfill a precious role in being an accessible source of open-air, alongside their additional cultural value as settings for public life. They simultaneously perform both mobile and stationary functions [54]. Urban streets, as part of a city’s texture, are public spaces that have the potential to shape urban life and social interactions [52,55,56,57,58,59,60]. However, with the advent of COVID-19, streets were forced to empty.
Understanding the characteristics of street spaces capable of favoring outdoor activities under a major psychological distress can be crucial for future planning and design guidelines, not only for post-pandemic scenarios but for future spaces more considerate of the psychological statuses of their users. The setting of urban streets is very important, and a good walkable urban street is a key factor in improving outdoor activities. In addition, factors such as pedestrian safety, comfort, and convenience should also be considered [61]. Relevant studies have shown that in the high-density urban streets of Hong Kong, some traffic management measures should be considered to develop pedestrian priority areas to provide a safer pedestrian environment [61], as well as enhancing street spaces, widening sidewalks, increasing street greenery to meet the demand for a better sense of spaciousness and proximity to nature of parks and intersections [62], building more outdoor escalators or handrails [63], and using Air Ventilation Assessment (AVA) systems to coordinate the negative effects of street congestion [64]. Lastly, Hong Kong streets would benefit from more exposure to trees, visual volume, and drift magnitude to improve people’s mood [65]. However, there is a lack of studies that investigate the relationship between outdoor activities and their settings under the impact of a prolonged and major psychological distress, caused in this case by COVID-19.

1.3. Outdoor Activities as Volitional Behaviors, a Decision-Making Perspective

Outdoor activities are defined as activities that involve the use of an outdoor space [66], and are not necessarily limited to transit. Previous research in urban environments has focused on outdoor activities [49,54,66,67,68,69,70,71,72,73,74,75,76,77]. Earlier studies normally distinguish activities based on necessity [78] or mobility [54], still commonly preserving the same criteria as embodiments of action performances [70]. In this paper, we consider outdoor activities as those actions performed by pedestrians that involve a prolonged stay on the street rather than mere transit, or in other terms, stationary activities [78]. These uses potentially encourage sociability and interaction, precious elements contributing to psychological well-being.
However, given the complexity of contemporary urban scenarios in high-density contexts and the multitude of factors inferring human behavior, and even more with the pandemic spread in Hong Kong, we adopted an alternative approach from a psychological and behavioral perspective through which we could gain a deeper understanding of outdoor activities, as a declination of human behavior, under distress and in relationship to their environment. Volitional behaviors are the result of complex decision-making processes that occur prior to the actual behavior or action performances; as an example, health psychologists have demonstrated that perceived threat is a determinant of behavior [79,80,81,82]. In other terms, prior beliefs and determinants of behavior shape how intentions become actual actions [83]. To better understand those dynamics, some insights can be gained from behavioral theories that operationalize determinants of behavior in precise variables.
For instance, the theory of reasoned action [84] categorizes beliefs prior to action into individual and normative beliefs. In addition to those, further studies recognized control beliefs as significantly influencing volitional behaviors, leading the theory of reasoned action to evolve into the theory of planned behavior (TPB hereafter) [83,85], one of the most influential models for behavior prediction [86]. Studies using this theoretical framework have found that perceived behavioral control is an essential determinant of behavior [87] under volitional behavior conditions [83,85]. Perceived behavioral control is the extent to which a person feels capable of enacting the behavior [88], and its effects on intentions can be interactive [87]. While the present study does not focus on predicting behavior based on intentions, we suggest some approaches that may enrich current perspectives on our understanding of outdoor activities as systems occurring in high-density urban environments. Although the TPB helps to demonstrate the complex psychological mechanisms that link intentions and actions, it assumes that actors are rational, neglecting the effects of affection and emotions on behavioral intentions [89]. Ajzen [86] argues that the TBP does not sufficiently consider the affective and cognitive processes that bias human behavior. To address this limitation, Conner and Armitage [89] introduce other variables into the model: belief salience, past behavior/habit, perceived behavioral control vs. self-efficacy, moral norms, self-identity, and affective beliefs. This theory is being continually improved upon, and it is used in this study as a theoretical framework to deepen our understanding of the relationship between human behavior (specifically here outdoor activities) and the urban environment. The decision-making perspective through which we address outdoor activities can support a more profound interpretation of the results and possible implications for future planning and design guidelines for high-density cities.

1.4. Research Aim

With this study, we attempt to detect street vibrancy patterns through a longitudinal study during the psychological distress induced by COVID-19 in Hong Kong. Our goal is to understand behavioral transitions in relation to their environment. The understanding of environments capable of potentially preserving outdoor activities even under psychological distress could be extremely beneficial for design and planning policies for post-pandemic scenarios as well for generally tackling psychological well-being in high-density cities such as Hong Kong.

2. Materials and Methods

As a case study, we selected three urban blocks in Mong Kok, an extremely dense neighborhood in Hong Kong. Mong Kok is a bustling district known for its high population density and limited public space, with a per capita open public space of only 0.6 m2 and a population density of 130,000/km2 (making it one of the most densely populated areas in the world). Despite this, Mong Kok boasts a unique mixture of old and new buildings across a range of heights and plot ratios. Although being distributed morphologically around a quite regular urban grid, this diversity creates a visually and sensorially intense environment [90], making Mong Kok an ideal location for our investigation. The streets of Mong Kok provide viable outdoor spaces for urban dwellers due to its unique morphology and diversity of building types, which are represented in this study using three urban blocks with different building typologies and street–building interfaces. The first block is distinguished by its locality-specific characteristics. The street frontage is quite fragmented and dominated by restaurants, pet shops, and street-food shops. With fragmented, we intend a non-continuous street frontage, with frequent changes in materials, different degrees of façade transparency and internal visibility, multi-presence of openings (doors, shop windows, and windows), and walls setbacks. The built environment comprises mainly old medium- and low-rise buildings, predominantly used for residential purposes and as hostels/hotels, and small businesses. The second block morphologically resembles the first with a fragmented façade and old medium- and low-rise buildings. The ground level interface is commercially focused, with a section of Fa Yuen Street nicknamed “Sneakers Street” due to the abundance of sneaker/sport shoe shops in the area. The third one comprehends Langham Place, one of the highest landmarks in Mong Kok, and it represents a typical superblock featuring a prominent shopping mall that impacts both the ground-level interface and the typology of the built environment on the west side, and a main road with tall buildings dominating the east side. The façade here is non-fragmented, with few accesses and a different character (Figure 1).

Data Collection and Processing

To gather primary data on outdoor activities in urban areas, a behavioral mapping approach was utilized to visualize spatial distribution patterns and understand their resistance in terms of street vibrancy. Video recordings were used in conjunction with visual snapshots to supplement the data and ensure the retrievability of dependable information. To enhance the quality of results obtained, video recordings were collected over a period of ten days between the end of March 2020 and the end of July 2020 with two rounds of video shooting conducted each day, resulting in a total of twenty videos (first round starting at 11.00 AM and second round at 4.30 PM). The data collection process was not significantly impacted by weather conditions, which ranged from sunny to cloudy with light showers. Overall, the goal was to identify outdoor activities on the streets and discern their spatial and temporal variation to better visualize street vibrancy under distress. The three routes (Figure 2) each covered vehicular streets with sidewalks. As a remark, the data were collected in a time frame when COVID-19 vaccination was not yet available in Hong Kong.
Following the collection of video recordings, data transfer and processing were carried out to enable further analysis and the creation of visual representations of behavioral patterns in relation to spatial patterns and settings. To accomplish this, the stationary use of the street for outdoor activities was counted and geolocated using a Geographic Information System, reaching a total of 4944 observation samples. Images of outdoor activities in each scene were captured, and individuals were highlighted and geolocated, with location accuracy increased through manual annotation and referencing of street shops.
The distribution of pedestrian volumes during the period after the COVID-19 outbreak from March 2020 to July 2020 was generalized in a way similar to vehicular flow distribution so that the daily average pedestrian volume and standard deviation (SD hereafter) can be deduced from short but multiple counts. Nineteen separate 15 min counts in 92 parcels within the three selected urban locations were used to determine the typical daily distributions of standing pedestrians in residential and business district streets. For each half-day, the standard deviation, mean value, and coefficient of variation (CV, defined as the SD divided by the mean) were calculated as measures of variation among locations to determine the best time of day for performing short counts. CV is a key indicator as it reveals the level of dispersion around the mean; in the context of a study on pedestrian behavior in different parcels within urban areas, a lower CV would generally be desirable. This is because a lower CV would indicate that the pedestrian behavior is more consistent or similar across the different parcels within each urban area. Additionally, we analyzed proximity to bus stops and MTR to visualize accessibility degree and radius, potential links with activity patterns (SD), and therefore the potential impact of COVID-19 as although it is well-established that transportation facilities have a significant impact on pedestrian behavior and outdoor activities [36], the perceived threat coming from the pandemic might have impacted on consequent decisions related to the use of public transport facilities.

3. Results

3.1. Activity Density Dispersion

The average pedestrian volume and distributions for 92 parcels were calculated and visualized, along with the SD and CV. To illustrate the distribution of activities, the results were grouped into seven categories based on the natural breaks in the mean values (Figure 3). The busiest parcels were in Area 01 and Area 03, followed by Area 02, which displayed the lowest spatial variation in activity density. The result showed that in Area 01, the No. 2, No. 3, and No. 19 parcels attracted the most people during the observation period, followed by No. 4. The southwest part seemed to have fewer people. In Area 02, the No. 30 and No. 58 parcels attracted the most people, followed by No. 48, as well as No. 59, No. 29, and No. 31–32, which were located adjacent to the most attractive areas. The west side of Area 02 had fewer people than the east side. In Area 03, parcels No. 74 and No. 79 had the most people there, followed by No. 63, No. 69, and No. 70.
The SD and CV serve as measures of the time fluctuation characteristics and focus on reflecting the dynamic changes of crowd activity in time. They specifically refer to the spatial unit in the specified time in the crowd activity density dispersion as a measurement index for the stability of the “changes” of the activities’ vibrancy, which are based on unstructured observations conducted on multiple days during five months. Periods with relatively small coefficients produce better estimates of the daily expansion factor, yielding better estimates of the daily volumes. Figure 4 shows the distribution of the SD in all parcels within the three areas. The parcels on the north side of Mong Kok Road have the highest SD values in Area 01, while most parcels in Area 02 have SD values below 2.5, except for a few parcels in the northwest corner, the middle of the south side, and the east side near road intersections.
CV is often used to compare the variability of different datasets. A lower CV indicates that the data points are closer to the mean, while a higher CV indicates that the data points are more spread out (Figure 5). In addition, we plotted the mean and SD values for all parcels with a double bar chart, employing the median line of the mean as well as the upper and lower quartiles of the SD as a standard of reference (Figure 6). All parcels satisfying the criteria of a mean greater than the median and a SD between the upper and lower quartiles were categorized as active and stable. By filtering, the parcels No. 1, 7, 11, 12, 14, 24, 28, 31, 32, 60, 61, 65, 67, 69, 70, 72, 73, 76, 78, 80, 81, 84, and 86 were highlighted as being more consistent with the distribution of parcels with lower CV values.

3.2. Activities Related to Mini-Bus and MTR

Transport facilities have a significant impact on the parcel values in Area 03. As can be seen in Figure 7a,b, we visualized the radius of accessibility for each transportation facility by applying the precise spot of each bus stop and metro station as the center of the circle along with the European distance as the basis, and taking the colors ranging from warm to cold to represent the degree of accessibility from excellent to poor. Then, the average parcel-to-spot distance was separated and used for correlation analysis.
Meanwhile, a correlation analysis was conducted between the total number of pedestrians per parcel and the site’s proximity to the closest bus stop and MTR station entrance (Table 1). The results depict an overwhelming majority of red areas, which correspond to regions in Figure 4 with high SD values. This is also consistent with the fact that, during an epidemic, travelers reduce their exposure to high-density population contact and their use of the metro. However, higher SD values frequently indicate a region that is susceptible to particular factors and has less resilience. Therefore, “active” and “low-change” areas play a crucial role in cities, particularly in the face of complex epidemic situations.

3.3. Spatial Correlation of Anchor Parcels Related to Outdoor Activities

This study employed an exploratory spatial data analysis method of running spatial autocorrelation of the mean value from each parcel to investigate the spatial distribution of street activities, including their aggregation and abnormalities, and to explore the relationship between average pedestrian counts and adjacent subdistricts in three sites. On the mean value, a global Moran’s I analysis was performed, which revealed that the average pedestrian quantity in different parcels remained stable in magnitude and spatial distribution pattern, with no significant changes observed in the expected values (E(I)) and a significant p-value (p < 0.05), indicating a clustered tendency (Figure 8). In addition, the LISA (local indicators of spatial association) cluster map was evaluated using a statistical comparison of the contribution of “anchor” stores or locations to the user activities of these streets (Figure 9). This analysis allows us to comprehend the potential influence of these “anchor” stores or nodes. The regions highlighted in red have high values for the variable and neighbors with similarly high values (high-high). According to the legend, in the same scheme, blue areas are low-low, faint blue regions are low-high, and yellow areas are high-low. Strongly colored regions are, therefore, those that significantly contribute to a positive global spatial autocorrelation result, whereas pale colors significantly contribute to a negative global spatial autocorrelation result. In our case, as the traditional living street block, several pet shops are located in parcels No. 1, 2, 3, and 6 in Area 01, which will have a significant impact on their neighbors, meaning that if more people visit these parcels, they will also visit the surrounding area more often. Additionally, parcels 73 and 75, which are the two main entrances and exits of Langham Place, have a significant impact on the surrounding parcel. Noteworthy is the large continuous dark blue cluster that appears in Area 02. Most of the area’s buildings are low-rise residential structures with podiums, and the building typology has a significant impact on the entire neighborhood, which suggests an opposite trend: the fewer the visitors to this area, the fewer people will visit the surrounding areas. Thereby, the street structure, including the proportion of buildings, the type of business, and the distribution of stores, will become an essential aspect of urban revitalization in the future. We also tested Areas 01–03 individually for reference (Figure 10).
The average number of individuals in each parcel showed a general downward trend from March to July, indicating a general increase in public apprehension of the epidemic based on the analysis of this case (Figure 11). On 8 April, 18 April, and 24 June, there was an upward trend in the mean values, which was associated with probable news reports and the government’s control of the epidemic, instilling public confidence and reducing fears of the outbreak. To identify the significant spatial influence factors on pedestrian quantity, a one-way analysis of variance (ANOVA) was conducted on the data (Table 2). The numerator degree of freedom (v1) is 18, and the denominator degree of freedom (v2) is 1729, with an F-value of 6.965 and a significance value, which is less than 0.05, indicating that there are indeed significant differences between the contract periods of the different sub-groups. Then, we utilized the post hoc test to determine, when a significant difference exists between the means of multiple samples, the details of the groups between which that significant difference occurs. Comparing the categories led us to the conclusion that the aggregate numbers are declining.
To cross-sectionally compare the impact of clustered parcels in each area, we calculated the daily mean of clustered parcels in the three areas (Area 01 with six parcels containing high-high and low-low, Area 02 with thirteen adjacent parcels of low-low, and Area 03 with two parcels of high-high), and regressed them on the overall daily mean of the area in which they were located. In contrast to the R2 values in the regression results of the three groups, the R2 values are all significantly greater than 0.6 in Area 01 and Area 02, indicating that the cluster in these two areas has a substantial impact on the neighborhood where it is located. However, the R2 value for Area 03 is extremely low, indicating that the parcel’s influence on the overall area is minimal in this area. This may be due to the fact that only two parcels exhibit high-high clustering, which has a negligible effect on the whole. The R2 value of Area 01 is 0.867, the highest among the three groups, followed by Area 02 (R2 = 0.618) and Area 03 (R2 = 0.46) (Table 3). With regard to the regression coefficients of the three groups, the p-values for Area 01 and Area 02 were statistically significant, whereas the p-value for Area 03 was 0.38, therefore greater than 0.05, indicating that the regression coefficient was not statistically significant and there was no linear correlation between the dependent and independent variables (Table 4). Area 01 had a regression coefficient of 0.629, while Area 02 had a regression coefficient of 0.946, indicating that the continuous clustering of parcels in Area 02 has a significant impact on the entire neighborhood and should, therefore, be fully considered in future urban regeneration and planning strategies and guidelines.

4. Discussion

Outdoor activities have been extensively studied in urban research as an expression of behavior benefitting the physical, social, and psychological well-being of urban populations. With the advent of COVID-19, however, streets were forced to empty, challenging the psychological well-being of the urban population. A deeper understanding of their resistance under distress in terms of street vibrancy can provide helpful insight for future planning and design guidelines.
The findings of this study shed light on the importance of specific parcels in an extremely high-density city’s morphological context as activators of spaces that can enhance social interactions and outdoor activities. These parcels could embody a key comprehensive intermediate scale element currently lacking in the contemporary body of knowledge. We adopt the concept of ‘parcels’ in the city as settings for outdoor activities and, therefore, crucial for fulfilling a role as environmental settings for social interactions, which are strong contributors to the well-being of the urban population. This unique methodological approach considers the parcels of the city as intermediate scale elements or clusters, rather than smaller-scale urban elements or larger-scale urban blocks, to act as a setting through which to measure street vibrancy patterns related to outdoor activities. From the results both deriving from SD and CV, the parcels with less dispersion around the mean (CV) act as initiators of vibrancy as data visualization clearly indicates their redundancy, even if decreasing, in the adjacent parcels.
From another morphological perspective, the results from color-coded intervals and SD highlighted the predominance of Area 01 street parcels acting as activators in terms of spatial settings compared to Area 03, showing the more accessible and fragmented interfaces as favoring outdoor activities rather than the linear and compact ones (Langham Place). This factor strongly relates to land use planning regulations in Hong Kong, which currently do not allow ventilation corridors at ground levels in the typical podium towers development. Combined with an increasing awareness about the relevance of natural ventilated corridors and wind performances especially for pandemic scenarios, this evidence is challenging the typical podium and tower urban block typology as a development model, which usually presents a lower degree of fragmentation and accessibility through the façade at the street level of the city.
Aligned with previous reflections, to better understand the environment and possible design implications related to the activator parcels, we further followed up with unstructured on-site observations, which led to more detailed reflections on the land use and design settings of the selected areas. This study reveals that the extreme fragmentation of the boundary between the street and the shop (in parcel No. 2), with live-fish bags hanging outside the street walls and open access to the interior space of the shop, allows for visual and physical accessibility to both space and life-form interaction; at some level, the shop extends to the street and allows closer human–environment interactions through its design. This parcel contains the only shop that through its spatial design features allows people standing on the street to have a direct interaction with a non-human life form, which distinguishes it from the other pet shops that require stepping inside and changing environment to interact with the animals. Additionally, the parcels with higher vibrancy highlighted in the LISA analysis are evidently related to psychological factors, respectively, affective and emotional (cultural/identity/memory). The more vibrant parcels, in fact, commonly embody very typical Hong Kong cultural traditions belonging to food culture (mostly street-food stalls and local restaurants). Shopping for fish and eating local street food are two important socio-cultural activities in Hong Kong’s local tradition and memory. The dynamic environments of these two street corners are strongly related to affective/cognitive/socio-cultural dimensions. Although similar spatial configurations and attributes can be found throughout Hong Kong’s urban fabric, even within the data collection areas, these results indicate that the activities in these two spaces have a more complex origin. Moreover, those parcels present streets façades that, from a design perspective, allow users to get closer and interact with the environment, as well as with other life-forms and people, without changing settings and allowing margins of action control. Translated into design variables, this means more openings in thefaçades, a higher visibility degree, and more flexibility in movement and trespassing of the border between the outdoors and indoors.
A summary of the data analysis, results, and design directions explored above is synthesized below (Table 5).
From another design standpoint and from a decision-making perspective (essential in this study to further interpret results), prior approaches to urban research exploring outdoor activities may benefit from an enhanced understanding of psychological and behavioral frameworks that suggest that outdoor activities are intricate systems shaped by psychological mechanisms, leading to volitional behaviors or action performances. Identifying and categorizing outdoor activities on streets based on action performances presented considerable challenges, as many individuals were found to be occupying spaces without engaging in particular or defined activities (Figure 12).
Notably, exceptions emerged in certain areas that exhibited higher levels of vibrancy, where outdoor activities involved more purposeful action. However, design solutions usually respond to actions rather than interactions, for instance, sitting, playing, eating, and reading, to mention but a few (a typical example from a design perspective relies on street furniture, mostly and commonly designed for action performances such as sitting or resting). Nevertheless, standing in place is perceived positively, given the potential for the facilitation of social encounters and possibly nurturing social interactions. Learning from the theory of planned behavior (TPB) conceptual model, in light of the importance of perceived behavioral control as a determinant influencing intentions and consequent actions, urban design strategies and approaches could be reoriented from ‘designing for actions’ to ‘designing for interactions.’ More specifically, design responses either belonging to urban design or architectural design should be oriented to respond to interaction modes with people, other life-forms, and the environment itself. Similar insights on the relevance of perceived control are addressed in some studies on urban spaces in relation to the sense of agency [91]. From a psychological perspective, being able to control the level of interaction plays a relevant role in determining the happening of a determined action; this could influence behavior at a community scale as well, which can also be quite reflected in the relevance of what usually is defined as participatory planning [92]. This perspective underscores the significance of perceived behavioral control as a level of control that impacts not only engagement but also interactions with the surrounding environment as well as other individuals.
Further research in this direction, with the enlargement of a wider-scale multifactor perspective, would help urban designers and planners to design more vibrant streets and cities that cater to the diverse needs of their inhabitants, especially in the presence of psychological distress in the population.

5. Conclusions

During the COVID-19 pandemic, high-density cities, and Hong Kong in particular, still traumatized by the SARS epidemic in 2003, were challenged in terms of psychological well-being. Mental health has been highly impacted by the pandemic, and therefore more research is needed to understand street vibrancy criteria for post-pandemic and future urban complex scenarios. From a spatial and morphological as well as a socially engaging perspective, natural ventilation, ventilation corridors at ground level, as well as interfaces that allow closer human–environment interactions, are considered favorable. In terms of design variables, this could be reflected in a more fragmented street façade, materials that allow visibility, as well as openings allowing passage and closer interactions among users and the environment. Those variables should, however, be considered as parts of the more complex element explored here and defined as a city parcel.
Methodologically, to understand vibrancy criteria, the unique approach we adopted suggests the ‘cluster’ approach as a unit to understand vibrancy on the streets and, consequently, their resilience. The relevance of the cluster lies in triggering vibrancy for adjacent parcels as well. The fragmentation of the typical urban block typology could potentially provide a concrete solution for the improvement of social life in high-density and compact cities such as Hong Kong. Lastly, the pandemic highlighted the crucial role that psychological factors play as determinants of behaviors. Future research should stress more interdisciplinary perspectives and address in a deeper mode those psychological mechanisms, attempting to bridge the gaps among the fields. Potential future research should also incorporate environmental perspectives as well as socio-economical factors for a better comprehensiveness of the understanding of urban systems towards a better future and psychological well-being of urban populations.

Author Contributions

Conceptualization, M.Z. and J.Y.T.; methodology, M.Z. and J.Y.T.; software, M.Z. and H.H.; validation, M.Z., J.Y.T. and H.H.; formal analysis, H.H.; investigation, M.Z., J.Y.T. and H.H.; resources, M.Z. and J.Y.T.; data curation, H.H.; writing—original draft preparation, M.Z.; writing—review and editing, M.Z., J.Y.T. and H.H.; visualization, M.Z. and H.H.; supervision, J.Y.T.; project administration, M.Z. and J.Y.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are available on request due to privacy restrictions.

Acknowledgments

We would like to thank Louie Sieh for her support and feedback. We would also like to thank Junpai Chen, Jingfei Huang and Yixuan Li for their technical support. We would also like to thank the editors and the reviewers for their time and effort in revising this paper.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Campion, J.; Javed, A.; Sartorius, N.; Marmot, M. Addressing the public mental health challenge of COVID-19. Lancet Psychiatry 2020, 7, 657–659. [Google Scholar]
  2. Cullen, W.; Gulati, G.; Kelly, B.D. Mental health in the COVID-19 pandemic. QJM Int. J. Med. 2020, 113, 311–312. [Google Scholar] [CrossRef]
  3. Usher, K.; Durkin, J.; Bhullar, N. The COVID-19 pandemic and mental health impacts. Int. J. Ment. Health Nurs. 2020, 29, 315. [Google Scholar]
  4. Xiang, Y.-T.; Yang, Y.; Li, W.; Zhang, L.; Zhang, Q.; Cheung, T.; Ng, C.H. Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. Lancet Psychiatry 2020, 7, 228–229. [Google Scholar] [CrossRef]
  5. Xiong, J.; Lipsitz, O.; Nasri, F.; Lui, L.M.W.; Gill, H.; Phan, L.; Chen-Li, D.; Iacobucci, M.; Ho, R.; Majeed, A.; et al. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. J. Affect. Disord. 2020, 277, 55–64. [Google Scholar] [CrossRef]
  6. Zhang, J.; Lu, H.; Zeng, H.; Zhang, S.; Du, Q.; Jiang, T.; Du, B. The differential psychological distress of populations affected by the COVID-19 pandemic. Brain Behav. Immun. 2020, 87, 49. [Google Scholar]
  7. Drapeau, A.; Marchand, A.; Beaulieu-Prevost, D. Epidemiology of psychological distress. Ment. Illnesses-Underst. Predict. Control. 2012, 69, 105–106. [Google Scholar]
  8. Horwitz, A.V. Distinguishing distress from disorder as psychological outcomes of stressful social arrangements. Health 2007, 11, 273–289. [Google Scholar]
  9. Ridner, S.H. Psychological distress: Concept analysis. J. Adv. Nurs. 2004, 45, 536–545. [Google Scholar]
  10. Chaix, B.; Delamon, G.; Guillemassé, A.; Brouard, B.; Bibault, J.-E. Psychological distress during the COVID-19 pandemic in France: A national assessment of at-risk populations. Gen. Psychiatry 2020, 33, 6. [Google Scholar]
  11. Qiu, J.; Shen, B.; Zhao, M.; Wang, Z.; Xie, B.; Xu, Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy recommendations. Gen. Psychiatry 2020, 33, 2. [Google Scholar]
  12. Wise, T.; Zbozinek, T.D.; Michelini, G.; Hagan, C.C.; Mobbs, D. Changes in risk perception and self-reported protective behaviour during the first week of the COVID-19 pandemic in the United States. R. Soc. Open Sci. 2020, 7, 200742. [Google Scholar]
  13. Pfefferbaum, B.; North, C.S. Mental Health and the COVID-19 Pandemic. N. Engl. J. Med. 2020, 383, 508–510. [Google Scholar] [CrossRef]
  14. World Health Organization. Severe Acute Respiratory Syndrome (SARS)-Multi-Country Outbreak—Update 49. 2003. Available online: https://www.who.int/emergencies/disease-outbreak-news/item/2003_05_07a-en (accessed on 1 September 2020).
  15. United Nations, Department of Economic and Social Affairs Population Division. World Population Prospects 2022, Online Edition. 2022. Available online: https://population.un.org/wpp/Download/Standard/MostUsed/ (accessed on 1 January 2023).
  16. McBride-Chang, C.; Cho, J.-R.; Liu, H.; Wagner, R.K.; Shu, H.; Zhou, A.; Cheuk, C.S.; Muse, A. Changing models across cultures: Associations of phonological awareness and morphological structure awareness with vocabulary and word recognition in second graders from Beijing, Hong Kong, Korea, and the United States. J. Exp. Child Psychol. 2005, 92, 140–160. [Google Scholar]
  17. Zacharias, J. Pedestrian dynamics on narrow pavements in high-density Hong Kong. J. Urban Manag. 2021, 10, 409–418. [Google Scholar] [CrossRef]
  18. Chow, J. Public Open Space Accessibility in Hong Kong: A Geospatial Analysis. 2018. Available online: https://www.researchgate.net/publication/328482653_Public_Open_Space_Accessibility_in_Hong_Kong_A_Geospatial_Analysis (accessed on 1 September 2020). [CrossRef]
  19. Lai, C. Unopened Space: Mapping Equitable Availability of Open Space in Hong Kong; Civic Exchange: Hong Kong, China, 2017. [Google Scholar]
  20. Wicks, C.; Barton, J.; Orbell, S.; Andrews, L. Psychological benefits of outdoor physical activity in natural versus urban environments: A systematic review and meta-analysis of experimental studies. Appl. Psychol. Health Well-Being 2022, 14, 1037–1061. [Google Scholar] [CrossRef]
  21. Buchecker, M.; Degenhardt, B. The effects of urban inhabitants’ nearby outdoor recreation on their well-being and their psychological resilience. J. Outdoor Recreat. Tour. 2015, 10, 55–62. [Google Scholar] [CrossRef]
  22. Wolsko, C.; Lindberg, K. Experiencing Connection With Nature: The Matrix of Psychological Well-Being, Mindfulness, and Outdoor Recreation. Ecopsychology 2013, 5, 80–91. [Google Scholar] [CrossRef]
  23. Johansson, M.; Hartig, T.; Staats, H. Psychological Benefits of Walking: Moderation by Company and Outdoor Environment: Environmental Moderation of Walking Benefits. Appl. Psychol. Health Well-Being 2011, 3, 261–280. [Google Scholar] [CrossRef]
  24. Weng, P.-Y.; Chiang, Y.-C. Psychological Restoration through Indoor and Outdoor Leisure Activities. J. Leis. Res. 2014, 46, 203–217. [Google Scholar] [CrossRef]
  25. Bustamante, G.; Guzman, V.; Kobayashi, L.C.; Finlay, J. Mental health and well-being in times of COVID-19: A mixed-methods study of the role of neighborhood parks, outdoor spaces, and nature among US older adults. Health Place 2022, 76, 102813. [Google Scholar] [PubMed]
  26. Beall, J.M.; Jackson, S.B.; Casola, W.R.; Peterson, M.N.; Larson, L.R.; Stevenson, K.T.; Seekamp, E. Self-reported participation in outdoor and nature-based recreation before and during the COVID-19 pandemic supports psychological health and well-being. Wellbeing Space Soc. 2022, 3, 100094. [Google Scholar] [PubMed]
  27. Hillier, B.; Penn, A.; Hanson, J.; Grajewski, T.; Xu, J. Natural movement: Or, configuration and attraction in urban pedestrian movement. Environ. Plann. B 1993, 20, 29–66. [Google Scholar] [CrossRef]
  28. Kuo, F.E.; Sullivan, W.C.; Coley, R.L.; Brunson, L. Fertile Ground for Community: Inner-City Neighborhood Common Spaces. Am. J. Community Psychol. 1998, 26, 823–851. [Google Scholar] [CrossRef]
  29. Peng, Y.; Feng, T.; Timmermans, H. A path analysis of outdoor comfort in urban public spaces. Build. Environ. 2019, 148, 459–467. [Google Scholar] [CrossRef]
  30. Addy, C.L.; Wilson, D.K.; Kirtland, K.A.; Ainsworth, B.E.; Sharpe, P.; Kimsey, D. Associations of Perceived Social and Physical Environmental Supports with Physical Activity and Walking Behavior. Am. J. Public Health 2004, 94, 440–443. [Google Scholar] [CrossRef]
  31. Sugiyama, T.; Thompson, C.W. Outdoor Environments, Activity and the Well-Being of Older People: Conceptualising Environmental Support. Environ. Plan A 2007, 39, 1943–1960. [Google Scholar] [CrossRef]
  32. Roe, J.; Mondschein, A.; Neale, C.; Barnes, L.; Boukhechba, M.; Lopez, S. The Urban Built Environment, Walking and Mental Health Outcomes among Older Adults: A Pilot Study. Front. Public Health 2020, 8, 575946. [Google Scholar] [CrossRef]
  33. Titze, S.; Stronegger, W.; Owen, N. Prospective study of individual, social, and environmental predictors of physical activity: Women’s leisure running. Psychol. Sport Exerc. 2005, 6, 363–376. [Google Scholar] [CrossRef]
  34. Owen, N.; Humpel, N.; Leslie, E.; Bauman, A.; Sallis, J.F. Understanding environmental influences on walking: Review and research agenda. Am. J. Prev. Med. 2004, 27, 67–76. [Google Scholar]
  35. Saelens, B.E.; Sallis, J.F.; Frank, L.D. Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures. Ann. Behav. Med. 2003, 25, 80–91. [Google Scholar] [CrossRef] [PubMed]
  36. Pikora, T.J.; Bull, F.C.L.; Jamrozik, K.; Knuiman, M.; Giles-Corti, B.; Donovan, R.J. Developing a reliable audit instrument to measure the physical environment for physical activity. Am. J. Prev. Med. 2002, 23, 187–194. [Google Scholar] [CrossRef]
  37. Popkin, B.; Duffey, K.; Gordonlarsen, P. Environmental influences on food choice, physical activity and energy balance. Physiol. Behav. 2005, 86, 603–613. [Google Scholar] [CrossRef] [PubMed]
  38. Frank, L.D.; Engelke, P.O. The Built Environment and Human Activity Patterns: Exploring the Impacts of Urban form on Public Health. J. Plan. Lit. 2001, 16, 202–218. [Google Scholar] [CrossRef]
  39. Gómez-Varo, I.; Delclòs-Alió, X.; Miralles-Guasch, C. Jane Jacobs reloaded: A contemporary operationalization of urban vitality in a district in Barcelona. Cities 2022, 123, 103565. [Google Scholar]
  40. Guo, X.; Yang, Y.; Cheng, Z.; Wu, Q.; Li, C.; Lo, T.; Chen, F. Spatial social interaction: An explanatory framework of urban space vitality and its preliminary verification. Cities 2022, 121, 103487. [Google Scholar]
  41. Málovics, G.; Creţan, R.; Méreine-Berki, B.; Tóth, J. Socio-environmental justice, participatory development, and empowerment of segregated urban Roma: Lessons from Szeged, Hungary. Cities 2019, 91, 137–145. [Google Scholar]
  42. Park, K.; Ewing, R.; Sabouri, S.; Larsen, J. Street life and the built environment in an auto-oriented US region. Cities 2019, 88, 243–251. [Google Scholar]
  43. Perrone, C. ‘Downtown Is for People’: The street-level approach in Jane Jacobs’ legacy and its resonance in the planning debate within the complexity theory of cities. Cities 2019, 91, 10–16. [Google Scholar]
  44. Askarizad, R.; He, J. Post-pandemic urban design: The equilibrium between social distancing and social interactions within the built environment. Cities 2022, 124, 103618. [Google Scholar] [CrossRef]
  45. Ran, M.-S.; Gao, R.; Lin, J.-X.; Zhang, T.-M.; Chan, S.K.W.; Deng, X.-P.; Zhang, B.-Z.; Zhang, X.-F.; Huang, G.-P.; Pu, D.-S.; et al. The impacts of COVID-19 outbreak on mental health in general population in different areas in China. Psychol. Med. 2022, 52, 2651–2660. [Google Scholar] [PubMed]
  46. Honey-Rosés, J.; Anguelovski, I.; Chireh, V.K.; Daher, C.; Konijnendijk van den Bosch, C.; Litt, J.S.; Mawani, V.; McCall, M.K.; Orellana, A.; Oscilowicz, E.; et al. The impact of COVID-19 on public space: An early review of the emerging questions–design, perceptions and inequities. Cities Health 2021, 5, S263–S279. [Google Scholar]
  47. James, A.C. Don’t stand so close to me: Public spaces, behavioral geography, and COVID-19. Dialogues Hum. Geogr. 2020, 10, 187–190. [Google Scholar]
  48. Low, S.; Smart, A. Thoughts about public space during COVID-19 pandemic. City Soc. (Wash. DC) 2020, 32, 1. [Google Scholar] [CrossRef]
  49. Zordan, M.; Tsou, J.Y. Urban Transitions in Pandemic Scenarios: Insights from Hong Kong. J. Asian Behav. Stud. 2020, 5, 55–70. [Google Scholar]
  50. Bereitschaft, B.; Scheller, D. How might the COVID-19 pandemic affect 21st century urban design, planning, and development? Urban Sci. 2020, 4, 56. [Google Scholar]
  51. Tang, B. Explaining the inequitable spatial distribution of public open space in Hong Kong. Landsc. Urban Plan. 2017, 161, 80–89. [Google Scholar]
  52. Tieben, H. Public space trends in Hong Kong. A view from the new territories. J. Public Space 2016, 1, 25–34. [Google Scholar] [CrossRef]
  53. Rossini, F. Temporary urban intervention in the vertical city: A place-making project to re-activate the public spaces in Hong Kong. J. Urban Des. 2019, 24, 305–323. [Google Scholar]
  54. Von Schönfeld, K.C.; Bertolini, L. Urban streets: Epitomes of planning challenges and opportunities at the interface of public space and mobility. Cities 2017, 68, 48–55. [Google Scholar] [CrossRef]
  55. Gehl, J. Life between Buildings, 6th ed.; The Danish Architectural Press: Copenhagen, Denmark, 2011. [Google Scholar]
  56. Gehl, J. A changing street life in a changing society. Places 1989, 6, 1. [Google Scholar]
  57. Malone, K. Street life: Youth, culture and competing uses of public space. Environ. Urban. 2002, 14, 157–168. [Google Scholar]
  58. Burton, E.; Mitchell, L. Inclusive Urban Design: Streets for Life; Elsevier: Amsterdam, The Netherlands, 2006. [Google Scholar]
  59. Carmona, M.; Heath, T.; Tiesdell, S.; Oc, T. Public places urban spaces. Urban Des. Q. 2004, 90, 40. [Google Scholar]
  60. Mehta, V.; Bosson, J.K. Third places and the social life of streets. Environ. Behav. 2010, 42, 779–805. [Google Scholar]
  61. Loo, B.P.; Lam, W.W.Y. Geographic accessibility around health care facilities for elderly residents in Hong Kong: A microscale walkability assessment. Environ. Plan. B Plan. Des. 2012, 39, 629–646. [Google Scholar]
  62. Chen, S.; Bao, Z.; Chen, J.; Yang, L.; Lou, V. Sustainable built environment for facilitating public health of older adults: Evidence from Hong Kong. Sustain. Dev. 2022, 30, 1086–1098. [Google Scholar] [CrossRef]
  63. Yu, R.; Cheung, O.; Lau, K.; Woo, J. Associations between Perceived Neighborhood Walkability and Walking Time, Wellbeing, and Loneliness in Community-Dwelling Older Chinese People in Hong Kong. Int. J. Environ. Res. Public Health 2017, 14, 1199. [Google Scholar] [CrossRef]
  64. Ng, E. Policies and technical guidelines for urban planning of high-density cities–air ventilation assessment (AVA) of Hong Kong. Build. Environ. 2009, 44, 1478–1488. [Google Scholar]
  65. Xiang, L.; Cai, M.; Ren, C.; Ng, E. Modeling pedestrian emotion in high-density cities using visual exposure and machine learning: Tracking real-time physiology and psychology in Hong Kong. Build. Environ. 2021, 205, 108273. [Google Scholar] [CrossRef]
  66. Elsheshtawy, Y. Where the sidewalk ends: Informal street corner encounters in Dubai. Cities 2013, 31, 382–393. [Google Scholar]
  67. Whyte, W.H. The Social Life of Small Urban Spaces; Project for Public Spaces Inc.: New York, NY, USA, 1980. [Google Scholar]
  68. Gospodini, A. Urban design, urban space morphology, urban tourism: An emerging new paradigm concerning their relationship. Eur. Plan. Stud. 2001, 9, 925–934. [Google Scholar]
  69. Gehl, J.; Kaefer, L.J.; Reigstad, S. Close encounters with buildings. Urban Des. Int. 2006, 11, 29–47. [Google Scholar]
  70. Mehta, V. Lively Streets: Exploring the Relationship between Built Environment and Social Behavior; University of Maryland: College Park, MD, USA, 2006. [Google Scholar]
  71. Mehta, V. Lively streets: Determining environmental characteristics to support social behavior. J. Plan. Educ. Res. 2007, 27, 165–187. [Google Scholar]
  72. Mehta, V. Walkable streets: Pedestrian behavior, perceptions and attitudes. J. Urban. 2008, 1, 217–245. [Google Scholar]
  73. Mehta, V. Look closely and you will see, listen carefully and you will hear: Urban design and social interaction on streets. J. Urban Des. 2009, 14, 29–64. [Google Scholar]
  74. Pafka, E. Places as Intersecting Flows: Mapping Urban Morphologies, Functional Constellations and Pe-destrian Rhythms. In Proceedings of the 4th Global Conference, Space and Place, Oxford, UK, 9–12 September 2013. [Google Scholar]
  75. Ruiz-Apilánez, B.; Karimi, K.; García-Camacha, I.; Martín, R. Shared space streets: Design, user perception and performance. Urban Des. Int. 2017, 22, 267–284. [Google Scholar]
  76. Bertolini, L. From “streets for traffic” to “streets for people”: Can street experiments transform urban mobility? Transp. Rev. 2020, 40, 734–753. [Google Scholar]
  77. Mehta, V.; Bosson, J.K. Revisiting lively streets: Social interactions in public space. J. Plan. Educ. Res. 2021, 41, 160–172. [Google Scholar]
  78. Gehl, J.; Svarre, B. How to Study Public Life; Springer: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
  79. Janz, N.K.; Becker, M.H. The health belief model: A decade later. Health Educ. Q. 1984, 11, 1–47. [Google Scholar]
  80. Maiman, L.A.; Becker, M.H. The health belief model: Origins and correlates in psychological theory. Health Educ. Monogr. 1974, 2, 336–353. [Google Scholar]
  81. Rosenstock, I.M. Historical origins of the health belief model. Health Educ. Monogr. 1974, 2, 328–335. [Google Scholar]
  82. Rosenstock, I.M.; Strecher, V.J.; Becker, M.H. Social learning theory and the health belief model. Health Educ. Q. 1988, 15, 175–183. [Google Scholar] [CrossRef] [PubMed]
  83. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar]
  84. Fishbein, M. A theory of reasoned action: Some applications and implications. Neb. Symp. Motiv. 1979, 27, 65–116. [Google Scholar]
  85. Ajzen, I. The social psychology of decision making. In Social Psychology: Handbook of Basic Principles; American Psychological Association: Washington, DC, USA, 1996; pp. 297–325. [Google Scholar]
  86. Ajzen, I. The theory of planned behaviour: Reactions and reflections. Psychol. Health 2011, 26, 1113–1127. [Google Scholar]
  87. Terry, D.J.; O’Leary, J.E. The theory of planned behaviour: The effects of perceived behavioural control and self-efficacy. Br. J. Soc. Psychol. 1995, 34, 199–220. [Google Scholar]
  88. Francis, J.; Eccles, M.P.; Johnston, M.; Walker, A.; Grimshaw, J.M.; Foy, R.; Kaner, E.F.; Smith, L.; Bonetti, D. Constructing Questionnaires Based on the Theory of Planned Behaviour: A Manual for Health Services Researchers; Centre for Health Services Research, University of Newcastle upon Tyne: Newcastle upon Tyne, UK, 2004. [Google Scholar]
  89. Conner, M.; Armitage, C.J. Extending the Theory of Planned Behavior: A Review and Avenues for Further Research. J. Appl. Social Pyschol. 1998, 28, 1429–1464. [Google Scholar] [CrossRef]
  90. Dahl, P.-J. Exloring design potentials in porous urban space: Split vision urbanism HK through Montage. In Proceedings Beyound ISM: The Landscape of Landscape Urbanism; Lund University: Lund, Sweden, 2016. [Google Scholar]
  91. Hunter, M.G.; Soro, A.; Brown, R.A.; Harman, J.; Yigitcanlar, T. Augmenting Community Engagement in City 4.0: Considerations for Digital Agency in Urban Public Space. Sustainability 2022, 14, 9803. [Google Scholar]
  92. Smith, R.W. A theoretical basis for participatory planning. Policy Sci. 1973, 4, 275–295. [Google Scholar]
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Figure 1. Snapshots from the selected areas. (a) Area 02; (b) Area 03.
Figure 1. Snapshots from the selected areas. (a) Area 02; (b) Area 03.
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Figure 2. The three areas selected for data collection in Mong Kok and the filming routes (R01, R02, R03). Top right: keymap highlighting the location of Mong Kok (Yau Tsim Mong District).
Figure 2. The three areas selected for data collection in Mong Kok and the filming routes (R01, R02, R03). Top right: keymap highlighting the location of Mong Kok (Yau Tsim Mong District).
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Figure 3. Visualization of color-coded intervals on site (activity pattern/parcel).
Figure 3. Visualization of color-coded intervals on site (activity pattern/parcel).
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Figure 4. Distribution of SD value by color-coded intervals on site.
Figure 4. Distribution of SD value by color-coded intervals on site.
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Figure 5. Distribution of CV value by color-coded intervals on site.
Figure 5. Distribution of CV value by color-coded intervals on site.
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Figure 6. Median of mean and lower quartile/upper quartile of SD in each parcel.
Figure 6. Median of mean and lower quartile/upper quartile of SD in each parcel.
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Figure 7. Accessibilities from each parcel. (a) Bus stop; (b) MTR.
Figure 7. Accessibilities from each parcel. (a) Bus stop; (b) MTR.
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Figure 8. The result of Moran’s I.
Figure 8. The result of Moran’s I.
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Figure 9. LISA clustering of pedestrian quantity in the 92 parcels.
Figure 9. LISA clustering of pedestrian quantity in the 92 parcels.
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Figure 10. LISA clustering of pedestrian quantity in the 92 parcels (individual test).
Figure 10. LISA clustering of pedestrian quantity in the 92 parcels (individual test).
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Figure 11. Mean value of pedestrian quantity from a daily perspective.
Figure 11. Mean value of pedestrian quantity from a daily perspective.
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Figure 12. Snapshots from the video recordings showing outdoor activities which do not involve precise actions but utilizing the space or just standing there.
Figure 12. Snapshots from the video recordings showing outdoor activities which do not involve precise actions but utilizing the space or just standing there.
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Table 1. Correlation analysis between accessibilities of bus stops/MTR stations from each parcel.
Table 1. Correlation analysis between accessibilities of bus stops/MTR stations from each parcel.
MEANDist_MTRDist_Bus
MEANPearson Correlation1−0.016−0.236 *
Sig. (2-tailed) 0.8830.023
N929292
Dist_MTRPearson Correlation−0.01610.235 *
Sig. (2-tailed)0.883 0.024
N929292
Dist_BusPearson Correlation−0.236 *0.235 *1
Sig. (2-tailed)0.0230.024
N929292
* Correlation is significant at the 0.05 level (2-tailed).
Table 2. Result of one-way analysis of variance (ANOVA).
Table 2. Result of one-way analysis of variance (ANOVA).
Sum of SquaresdfMean SquareFSig.Partial Eta SquaredNoncent. ParameterObserved Power a
Contrast1758.9891897.7226.9650.0000.068125.3781.000
Error24,256.957172914.029
The F tests the effect of TIME. This test is based on the linearly independent pairwise comparisons among the estimated marginal means. a Computed using alpha = 0.05. Dependent Variable: NUM.
Table 3. Contrast of three models.
Table 3. Contrast of three models.
AreaRR2Adjusted R2Std. Error of the EstimateChange StatisticsDurbin–Watson
R2 ChangeF Changedf1df2Sig. F Change
010.931 a0.8670.8590.7022605348234480.867110.5201170.0001.857
020.786 a0.6180.5960.513554640.61827.5541170.0001.866
030.214 a0.046−0.0110.664026590.0460.8131170.0001.918
a Predictors: (Constant), daily mean of clustered parcels. Dependent Variable: overall daily mean of the area.
Table 4. Contrast of coefficients from three areas.
Table 4. Contrast of coefficients from three areas.
ModelUnstandardized CoefficientsStandardized CoefficientstSig.95.0% Confidence Interval for B
BStd. ErrorBetaLower BoundUpper Bound
1(Constant)0.3660.321 1.1380.271−0.3121.043
Area 010.6290.0600.93110.5130.0000.5030.756
2(Constant)0.5790.1850.7863.1320.0060.1890.968
Area 020.9460.1805.2490.0000.5661.327
3(Constant)2.6730.2860.2149.3290.0002.0683.277
Area 030.0790.0880.9020.380−0.1060.264
Table 5. Summary of data analysis methods, results, and design directions.
Table 5. Summary of data analysis methods, results, and design directions.
TargetData AnalysisResultsDesign Direction
Activity density dispersionSD and CVHighest SD values in A01. A02 with SD below 2.5.Parcel as a cluster (intermediate scale) planning approach for design and analysis.
Accessibility MTR and BusPearson CorrelationMajority of red areas corresponding to regions with high SD values.Parcel as a cluster (intermediate scale) planning approach for design and analysis.
Spatial AssociationMoranAverage pedestrian quantity in different parcels remained stable in magnitude and spatial distribution pattern, with no significant changes observed in the expected values (E(I)) and a significant p-value (p < 0.05), indicating a clustered tendency.Parcel as a cluster (intermediate scale) planning approach for design and analysis.
LISANo. 1, 2, 3, and 6 in A01, and No. 73 and 75 in A03 have a significant impact on surroundings; majority of low-low in A02.Street interface spatial design for controllable and multilevel human environment interactions (materials and design guidelines). Addition of cognitive and affective elements within the design strategy.
ANOVA and post hoc testThe numerator degree of freedom (v1) is 18, and the denominator degree of freedom (v2) is 1729, with an F-value of 6.965 and a significance value, which is less than 0.05.
The p-values for Area 01 and Area 02 were statistically significant, whereas the p-value for Area 03 was 0.38, which was greater than 0.05, indicating that the regression coefficient was not statistically significant and there was no linear correlation between the dependent and independent variables.		Podium-tower urban block morphologically questioned.
Design for interaction approach (integration of decision-making and behavioral perspectives within design strategy).
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Zordan, M.; Tsou, J.Y.; Huang, H. Street Vibrancy and Outdoor Activities under COVID-19 Psychological Distress: Lessons from Hong Kong. Land 2023, 12, 1896. https://doi.org/10.3390/land12101896

AMA Style

Zordan M, Tsou JY, Huang H. Street Vibrancy and Outdoor Activities under COVID-19 Psychological Distress: Lessons from Hong Kong. Land. 2023; 12(10):1896. https://doi.org/10.3390/land12101896

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Zordan, Mirna, Jin Yeu Tsou, and Hao Huang. 2023. "Street Vibrancy and Outdoor Activities under COVID-19 Psychological Distress: Lessons from Hong Kong" Land 12, no. 10: 1896. https://doi.org/10.3390/land12101896

APA Style

Zordan, M., Tsou, J. Y., & Huang, H. (2023). Street Vibrancy and Outdoor Activities under COVID-19 Psychological Distress: Lessons from Hong Kong. Land, 12(10), 1896. https://doi.org/10.3390/land12101896

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