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The COVID-19 pandemic provided a unique opportunity to examine how varied environmental and social factors in urban environments affect human wellbeing, which is an important dimension of urban sustainability. Past research has focused on individual dimensions of health and isolated nature interactions, limiting knowledge about how different environmental and social factors affect distinct aspects of residents' wellbeing. Through quantitative analyses of social survey and environmental data in metropolitan Phoenix, Arizona (USA), we explored how distinct nature recreation activities—along with nature satisfaction and social capital—affected residents' subjective, mental, and physical wellbeing across diverse neighborhoods during the COVID-19 pandemic, controlling for socio-demographics. Results reveal how distinct drivers shape different dimensions of wellbeing. Perceived social and environmental attributes of neighborhoods and proximity to nature preserves were associated with subjective wellbeing, while park visitation was linked to physical health. Nature interactions largely were unrelated to mental health. Changes in nature recreation during the COVID-19 pandemic did not significantly impact any dimension of wellbeing. Our research suggests that multiple local environmental and social features should be considered when designing healthy communities for urban sustainability. [ABSTRACT FROM AUTHOR]

Urban parks, integral to city life, have long contributed to the well-being of residents through various ecosystem services. Previous studies consistently highlighted unequal park distribution and access, and the COVID-19 pandemic has exacerbated these disparities. From a spatial justice perspective, this review examines urban parks' roles during the pandemic, the impact on equity and access, and how evolving park usage characteristics inform future management challenges. Analysing 53 peer-reviewed studies and 11 online materials from January 2020 to April 2023, this review reveals a significant increase in park visitors during the pandemic, causing challenges in accommodating the surge due to lockdown measures. The findings underscore physical and social justice dimensions, revealing disparities in park access during COVID-19. These challenges prompt reevaluating urban parks' potential for well-being and ecosystem benefits, advocating for inclusive decision-making to enhance community resilience and socialisation. The COVID-19 crisis highlighted planning and management challenges, emphasising the need for a more sustainable, liveable, and responsive approach to urban park planning. [ABSTRACT FROM AUTHOR]

Urban parks played an important role during the COVID-19 pandemic among urban dwellers. Numerous studies have shown that park visitations increased or decreased during the pandemic depending on the parks' contexts, locations, and populations. However, a lack of research has been conducted regarding the impact of COVID-19 on vulnerable and non-vulnerable communities. Therefore, this study seeks to identify the differences between socioeconomic levels in responses to COVID-19′s impact on urban park visits. To observe park users' movements in real-world scenarios, mobile signaling data were used to capture their movements. Then, using Repeated Measures ANOVA (RM ANOVA), the effectiveness of park visit patterns was statistically verified by considering two variables: "time" and "vulnerability". The results showed that park visits increased during the early stages of the COVID-19 pandemic regardless of the vulnerability. As COVID-19 spread, underserved communities experienced decreased park visits, demonstrating park inequality after the pandemic. The comparisons in this study provide recommendations for park managers and policymakers in terms of reducing park inequality. [ABSTRACT FROM AUTHOR]

Forecasting tourism volume can provide helpful information support for decision-making in managing tourist attractions. However, existing studies have focused on the long-term and large-scale prediction and scarcely considered high-frequency and micro-scale ones. In addition, the current approaches are limited regarding forecasting the visitor volume of a designated sub-area in a tourist attraction. This sub-area forecast can assist local-scaled managing decisions of tourist attractions, particularly for large-scale tourist attractions. Therefore, to achieve high-frequency forecasts of tourist volume for finer scale areas such as parks and their sub-areas and generate more controllable and flexible forecasts, this study developed a novel method that incorporates a forecasting model composed of multiple deep learning components and a designed control mechanism. The control mechanism produces high-temporal-resolution sequences of tourist volume for designated sub-areas, and the forecasting model is built on an attention-based deep-bidirectional neural network to better capture the long-range dependencies of the sequence and enhance the forecasting accuracy and robustness. The experimental research was performed at Taiyangdao Park and its two designated sub-areas to validate the effectiveness and superiority of the proposed method compared to other widely used deep-learning methods; three types of performance evaluations were adopted including fitting methods, error measures, and Diebold–Mariano tests. The results demonstrated that the proposed method provided outstanding performance in high-frequency forecasts and yielded more desired forecasting outcomes than other widely used forecasting methods. Furthermore, the comparison with the performances of various other deep learning models provide insights concerning their forecasting capacity; for instance, bidirectional RNN models tend to achieve better forecasts than general RNN models in the high-frequency forecasts. The proposed method has significant practical applicability in aiding short-term micro-scale management decisions and can also serve as an alternative approach in the field of tourist volume forecasting. [ABSTRACT FROM AUTHOR]

Park visits are beneficial for people's physical and psychological health, as well as for the development of social relationships. This study investigated the degree of recovery of physical, psychological, and social aspects of residents in different types of parks and the influence of socio-demographic factors, personal factors, residential space attributes, and park characteristics on park visitation. The results show that tourists visiting urban parks have higher physiological and social recovery than those visiting suburban parks and that there are significant differences. Physical exercise, rest and relaxation, and spending time with family and children were the three most prevalent factors influencing park visits, while time constraints were the most important reason why residents failed to use parks. Socio demographics, residential spatial attributes, individual variables, and park characteristics explained 13.6%, 16.7%, 4.6%, and 2.9% of the total variance in park visit frequency, respectively. Residential green space, age, children under age seven, time spent in residential green space, willingness to spend time in nature, greenery, maintenance, and amenities were positively associated with park use. Income, education, home price, and distance from home to the park were negatively associated with park use. These findings have implications for park management and for future research. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has limited people's visitation to public places because of social distancing and shelter-in-place orders. According to Google's community mobility reports, some countries showed a decrease in park visitation during the pandemic, while others showed an increase. Although government responses played a significant role in this variation, little is known about park visitation changes and the park attributes that are associated with these changes. Therefore, we aimed to examine the associations between park characteristics and percent changes in park visitation in Harris County, TX, for three time periods: before, during, and after the shelter-in-place order of Harris County. We utilized SafeGraph's point-of-interest data to extract weekly park visitation counts for the Harris County area. This dataset included the size of each park and its weekly number of visits from 2 March to 31 May 2020. In addition, we measured park characteristics, including greenness density, using the normalized difference vegetation index; park type (mini, neighborhood, community, regional/metropolitan); presence of sidewalks and bikeways; sidewalk and bikeway quantity; and bikeway quality. Results showed that park visitation decreased after issuing the shelter-in-place order and increased after this order was lifted. Results from linear regression models indicated that the higher the greenness density of the park, the smaller the decrease in park visitation during the shelter-in-place period compared to before the shelter-in-place order. This relationship also appeared after the shelter-in-place order. The presence of more sidewalks was related to less visitation increase after the shelter-in-place order. These findings can guide planners and designers to implement parks that promote public visitation during pandemics and potentially benefit people's physical and mental health. [ABSTRACT FROM AUTHOR]

Effective quantification of visitation is important for understanding many impacts of the COVID-19 pandemic on national parks and other protected areas. In this study, we mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices and released by SafeGraph as part of their Social Distancing Metric dataset. Based on comparisons with visitation statistics released by the U.S. National Park Service, our results confirmed that mobility records from digital devices can effectively capture park visitation patterns but with much finer spatiotemporal granularity. In general, triggers of visitation changes corresponded well with the parks' management responses to COVID-19, with all six parks showing dramatic decreases in the number of visitors (compared to 2019) beginning in March 2020 and continuing through April and May. As restrictions were eased to promote access to the parks and the benefits associated with outdoor recreation, visitation in 2020 approached or even passed that from 2019 by late summer or early autumn at most of the parks. The results also revealed that parks initially saw the greatest increases in visitation after reopening originating from nearby states, with visitorship coming from a broader range of states as time passed. Our study highlights the capability of mobility data for providing spatiotemporally explicit knowledge of place visitation. [ABSTRACT FROM AUTHOR]