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Article

What Affects the Use Flexibility of Pocket Parks? Evidence from Nanjing, China

by
Conghui Zhou
*,
Meng Xie
,
Jin Zhao
and
Yihuan An
School of Architecture, Southeast University, 2 Sipailou Road, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(9), 1419; https://doi.org/10.3390/land11091419
Submission received: 11 August 2022 / Revised: 25 August 2022 / Accepted: 25 August 2022 / Published: 29 August 2022
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Abstract

:
The use flexibility of pocket parks is one of the essential factors that determine their service compatibility, efficiency, and attraction for park users in densely populated cities. To examine the factors that affect the use flexibility of pocket parks, we collected data on the periodic use of 74 pocket parks through a field survey in Nanjing and adopted the Shannon Wiener diversity index to quantitatively measure this variable. After using a multiple regression model series, we examined the internal and surrounding factors that affect the use flexibility of pocket parks on weekdays and weekends, respectively, and compared them with those that affect the use intensity of pocket parks. The results showed that paved ground and the mixed use of surrounding land promoted both use flexibility and intensity of pocket parks. Boundary buildings and surrounding residents only promoted their use flexibility, while tree canopies and exercising facilities only promoted the use intensity of pocket parks. A significant correlation (p < 0.05) was also found between the use flexibility and intensity on weekdays. These findings can contribute to future decisions regarding pocket park planning and design in Nanjing and similar cities.

1. Introduction

1.1. Pocket Parks and Their Use Flexibilities

Pocket parks, also known as vest-pocket parks, green pocket and mini-parks, are types of green spaces defined by their spatial scale [1,2]. Pocket parks are generally recognised as public park spaces whose area is usually less than the threshold area of community parks (1 hm2) [3,4,5]. Compared to traditionally larger parks (like urban parks and community parks), pocket parks are characterised by lower construction and maintenance costs, higher adaptability to their surroundings, and greater proximity to residents [6,7]. As such, they are usually adopted as an effective tool to mitigate the severe shortages of outdoor recreational resources in densely populated areas [3,8]. Moreover, with the intensification of urbanisation and the tensions of modern life, pocket parks are also treated as precious places that can help with restoration and promote mental health [9,10].
Owing to the high saturation of land development in densely populated urban areas, newly installed pocket parks are often developed from ambivalent, leftover, or dormant spaces, most of which are considered ‘liminal spaces’ [11,12,13]. The concept of liminality refers to a state of ‘betweenness’, intermediacy, or ambiguity of being—the ‘indeterminacy of loose space’ [13,14]. Liminal spaces exist ‘at the margins’ and are characterised by emergence and flux, fluidity, and malleability, and are neither segregated nor uncontained [15,16]. This state of liminality also grants pocket parks a unique ability to be compatible with diverse recreational activities, simultaneously or in different periods [17,18]. This use flexibility is not frequently found in traditional parks, where the function and use of each internal site are clearly defined and restricted [8,19].
In a densely populated neighbourhood, the flexible use of pocket parks can effectively meet the local populace’s diverse recreational needs for those from different resident groups, meaning that the park’s services can be equally shared [20,21]. Therefore, the use flexibility is essential to the service effectiveness of pocket parks in densely populated cities (such as New York, in which the average density is more than 10,000 people/km2), wherein land resources are seriously limited. In addition, existing studies [16,21] have also shown that flexible use with fewer restrictions is one of the core attractions of pocket parks.

1.2. Factors Affecting Park Use

Current studies have mainly focused on the internal and external factors that influence park use [22,23,24]. The internal factors primarily refer to their site dimensions (e.g., park area, pavement, water features, and vegetation) [25], and facility conditions (e.g., facility types, their number, and locations) [7,26], while the external factors include the physical characteristics of their surroundings (e.g., surrounding facilities and transportation) [27] and the socioeconomic characteristics of their area (e.g., surrounding land development, population, and resident preferences) [28]. The features of park usage, such as their use mode, intensity, and frequency, result from the joint influence of both these internal and external factors [29,30].
However, most studies have only focused on the factors that affect the use of traditional large parks [25,28,31]. As the element composition and service mechanism of pocket parks differ from those of larger parks, the results drawn from the research on the use of the latter may not have the same applicability to pocket parks [3,32]. For example, due to their size limitations and the concentrated recreational demand around, most pocket parks must leave the majority of their space for recreational activities and cannot install comprehensive landscape elements or service facilities in the same way as large parks [33]. However, the liminality of the pocket park space can promote much tighter interconnections between pocket parks and their surroundings than those between large parks and their surroundings [26,34,35]. These tight interconnections bring not only numerous potential visitors to pocket parks, but also compensate for pocket park size deficiencies through the use sharing of the service facilities, such as public toilets, retail shops, and restaurants around pocket parks [8,36].
Since the internal constituents of pocket parks are simpler and their use status is more sensitive to surroundings than those of large parks, it is found that the factors that affect pocket park use are different from those affecting large parks [6,33]. Owing to these significant differences, traditional park planning and design guidelines, which are mainly derived from studies and practices on large parks, may not be fully applicable in pocket park planning and design. Therefore, it is necessary to conduct specific studies that explore and examine the factors affecting the usage of pocket parks to support the relevant planning and design practices [37,38].
As the flexibility of use of pocket parks greatly determines their service effectiveness and equitable access, studies in this area are rare compared to those on park use intensity (i.e., number of visits during a specific period). Therefore, questions such as ‘what factors affect pocket park use flexibility and how?’ and ‘is there any correlation between the factors affecting pocket park use flexibility and those affecting their use intensity?’ remain unanswered. In response to these questions, our study established a quantitative framework to measure the use flexibility of pocket parks. We built on the existing methods, using multiple regression models to analyse both the internal and external effects. Meanwhile, a comparison model series was also constructed to analyse the factors that affect pocket park use intensity and explore the possible correlations and differences between the factors affecting use flexibility and those affecting use intensity. By utilising the city of Nanjing as the study area, this study has two main objectives. The first is to develop a quantitative tool to measure the factors that affect pocket park use flexibility based on multi-source data. The second is to uncover the main factors and their action mechanisms that affect the use flexibility of pocket parks. Only through these objectives can we present accurate suggestions to inform future decision-making involved in pocket park planning and design in Nanjing and other similar cities worldwide.

2. Materials and Methods

2.1. Study Area and Sample Selection

Nanjing is the capital of the Jiangsu Province and a central city in eastern China. In 2020, the urban population of Nanjing was 8.09 million and its urbanisation rate was 86.8%, with a built-up area of 868.28 km2. The main part of Nanjing covers an area of 278 km2, which accommodates more than half of its urban population. According to 2019 statistical data, the average population density in the main city exceeded 15,000 people/km2 (about 6000 dwelling/hm2), with that in the central district exceeding 40,000 people/km2. We chose Nanjing for the following reasons: (1) it is a typical densely populated area, meaning that our research results are generalisable to similar areas either in China or internationally, and (2) it has hundreds of pocket parks (including side green pockets, mini-parks, and street corner parks), which offer abundant samples for this study. For example, the development of pocket parks between 2013 and 2017 increased the per capita park area of Nanjing by approximately 10% [39]. According to the newly formulated Nanjing Parkland Layout Plan (2017–2035), the main city will continue to promote the development of pocket parks, ensuring that every resident will eventually be able to access at least one park within a five-minute walking distance [40].
The selection of pocket park samples was based on the following two criteria: (1) through considering the threshold area of the average community park in China (10,000 m2) [41] and the areas that have enough space for the flexible use of a pocket park [42], the size of the pocket park sample was set as 800–10,000 m2, and (2) to effectively collect the residents’ daily use information of the pocket parks, the pocket park samples must be located in the areas with residents in their surrounding 300 m (a five-minute walking distance) [43]. Therefore, we obtained 74 pocket park samples, with an average area of 4102 m2 and an average density of 31,000 residents/km2 in the surrounding 300 m area (Figure 1).

2.2. Data Collection

We collected the usage data of pocket parks through fixed-point shooting via a field survey. The investigations were carried out on weekdays and weekends (excluding national holidays) in both autumn (between late September and late November 2020) and spring (between late March and late May 2021). The climate of these periods in Nanjing is considered the most suitable for outdoor recreation [44]. To collect the most representative usage data of pocket parks, we conducted the field surveys only on sunny or cloudy days, with the average temperature being between 15 °C and 25 °C. To trace the different usage rates of pocket parks over a given day, four periods, including early morning (6:30–8:30), late morning (9:30–11:30), afternoon (14:30–16:30), and night (18:30–20:30), were used as the target investigation periods because the use of pocket parks in these periods was found to be relatively more active. In contrast, the utilisation mode of pocket parks was relatively stable within each period, according to our preliminary site survey that involved 15 samples.
To improve the efficiency of our data collection, we divided the investigation area into five sub-areas according to the spatial distribution of the pocket park samples. Around 15 samples were located within each sub-area, which are relatively closed to each other and are convenient to be investigated as a group. One to two team members were responsible for data collection in each area, ensuring that the use conditions of each sample were recorded through photos in each target period during each investigation day. As existing studies have demonstrated that the factors affecting park use on weekdays and weekends differ [33,45], we collected the usage data of all the samples over 10 days, including five weekdays and five weekends.
The type of use and the number of users in each target period of every sample pocket park were recorded via photos taken during each field survey (Figure 2). We classified pocket park use based on the six-category framework for outdoor activities in green spaces, as developed by Lachowycz and Jones [46]. Three adjustments to the original framework were made to fit our sampled pocket parks and study objectives (Table 1). Because the use category ‘interaction with wildlife and nature’ in the original framework was rarely found in our pocket park samples, we removed it. Second, we did not utilise the ‘travel through parks’ category as one of our flexible use categories for assessing pocket parks because it is not recreation-related. Instead, we counted people travelling through pocket parks as a variable termed ‘passer-by density’, which reflects the characteristics of the site conditions that may affect the flexible use of pocket parks. For example, a continuous linear space traversed within a pocket park can easily be used as a shortcut by pedestrians that could then influence the normal use of the given park. Third, according to the results of our site surveys, we expanded the category ‘other activities’ to include all activities that were not covered in the original categories, including standing and waiting, people being on their phone, and so on. We also found that different types of recreational activities were dominated by different age-groups. For example, most people participating in group activities are senior people (older than 60 years old), while most people involved in family and social interactions are children (younger than 10 years old) and middle-aged (30–60 years old). Therefore, the diversity of the recreation activities within a pocket park can also partially reflect the compatibility of a park to different age-groups. As a result, five categories of park-related uses, including physical activities, relaxation and entertainment activities, family and social interactions, participation in group activities, and other activities, were used to measure the use flexibility of pocket parks. In contrast, the original category ‘travel through parks’ was dubbed ‘passer-by density’ for measuring the site condition.

2.3. Model

We established two model series to analyse the influential factors and their effects on pocket park use. Each model series comprised two linear regression models, including a one-weekday model (Model 1), which was used to analyse the factors affecting pocket park use on weekdays, and a one-weekend model (Model 2), which was used to analyse the factors affecting pocket park use on the weekend. Model series 1 takes the indicator that reflects use flexibility as the dependent variable, which is used to analyse the factor types and their action effects on pocket park use flexibility. Model series 2 takes the indicator that reflects use intensity as the dependent variable, which is employed to analyse the factor types and their action effects on pocket park use intensity. Model series 2 also functions as a comparison to further outline the correlations and differences between the factors affecting pocket park use flexibility and those affecting use intensity (Figure 3).
The use intensity of a pocket park in a specific period is measured by the visit frequency of the users counted from the photos taken in this period. To quantitatively measure the use flexibility of pocket parks, we introduced the Shannon Wiener index (SWI)—an index that was originally designed to evaluate the level of biodiversity in the field of ecology—to reflect the extent of the flexible use of parks by quantifying the diversity of their recreational activities in a specific period. The diversity measurement of the SWI uses the uncertainty measurement principle from information theory to predict the variations in the number of species that an individual may encounter within a specific community. The higher the diversity of the community, the greater is the variation in their natural species. The SWI of pocket park j (SWIj) is derived from Equation (1).
SWI j = − ∑ 1 n G i ln ( G i )
where n is the total number of pocket park use categories and Gi is the proportion of the use intensity gained from each category I in terms of their total use intensity. The application of the SWI in the measurement of pocket parks’ use flexibility reflects the number of use categories and the distribution evenness of the users in each category. Accordingly, a higher SWI indicates higher use flexibility of each pocket park.
To be consistent with the attributes of pocket parks, we used the categories of park internal conditions and park surrounding conditions to aggregate the independent variables. The independent variables in each category were selected based on existing research [27,47] and then narrowed down using exploratory and correlation analyses. During this process, variables were either aggregated into a group or eliminated. For example, two preliminary variables in the surrounding category (the capacity of outdoor fitness equipment for adults and the capacity of outdoor fitness equipment for children) were aggregated into one variable titled ‘exercising facility capacity’, owing to their high collinearity. Meanwhile, for variables with high collinearity that could not be aggregated, the more influenced variable with a higher coefficient was retained. For example, the park area variable was found to be highly collinear with that of the paved ground area. After comparison, the latter was retained because it was found to be more influenced, while still reflecting more delicate features of the site condition. After screening, we extracted seven variables for modelling, including four internal condition variables and three surrounding condition variables (Table 2 and Table 3).
In the internal conditions category, paved ground usually accommodates most pocket park visitors, as reported in existing research [34,35]. To differentiate between pathways or passage spaces within parks, we included only paved ground that is larger than 12 × 12 m2 in our statistics because these parameters are considered the minimum threshold size capable of supporting most group activities [34,42,48]. The tree canopy coverage area was used because the space under a large tree has proven to be more attractive to park users because it improves the microclimate, as well as biodiversity [49,50]. To unify the measurements of various exercising facilities of differing sizes, we adopted a capacity unit to measure the exercising facilities (including fitness equipment for adults and children). As mentioned, passer-by density was also included as a dynamic attribute of the site conditions because it indirectly reflects the spatial structure within pocket parks. In addition to passer-by density, which was counted through a field survey, we measured the site condition data through the Maxar Worldview-03 images of Nanjing (April 2020), with a ground sampling distance of 0.51 m. Moreover, a site investigation was performed in October 2020 to measure the capacity of exercising facilities and to verify the borders of the paved ground, which cannot be observed directly from the Maxar images.
The surrounding conditions category included the boundary building ratio, resident population, and the land mixed-use level within the surrounding 300 m area of the pocket park samples. Approximately 300 m around each pocket park was defined as the surrounding area because this is considered the best service scope of pocket parks in most planning guidelines [43,51]. The boundary building ratio was included because it is an important indicator that reflects the spatial features of a pocket park (such as spatial closeness and openness to the city public space), as well as the potential complementary support that a pocket park can obtain from the boundary facilities, as per the existing guidelines [52]/studies [53]. The boundary building ratio was calculated through the land use maps and Maxar images. To measure the surrounding resident population, we captured the number of households in the surrounding area of each sample from the open-access website Lianjia, the largest letting and estate agency in China. Then, the resident population was calculated using the average household population of Nanjing, as derived from the statistical data of 2020 from the Nanjing Statistical Bureau. Land mixed-use level indicates the degree of land used in varying ways which is measured by land use entropy (LUE). LUE considers the relative percentage of two or more land-use types within a given area. When calculating the LUE, seven types of point of interest (POI) data, including shops, restaurants, houses, recreation and entertainment facilities, administrations and offices, medical agencies, and educational agencies, were also collected from the Baidu Map through the JavaScript Application Programming Interface. The POI data from the Baidu Map include the name, location coordinates, and subcategory of each facility type, thereby supporting the targeted geographic information system (GIS) mapping. Furthermore, these data are dynamically updated by app users, meaning that they are sufficiently accurate and timely to support our measurements. The LUE of pocket park j is derived from Equation (2).
LUE j = − [ ∑ 1 k P i ln P i ] / ln k  
where Pi is the percentage of the POI type i in the surrounding POI of pocket park j, with k being the total types of POI around pocket park j (by definition, k ≥ 2). A higher LUE indicates a greater extent of land mixed-use.
Moreover, to further investigate the usage modes of pocket parks in the samples, k-means clustering was used to segregate pocket park samples into two three-group series, according to the use flexibility (UF) and use intensity (UI). We used the relative mean value (clustering centre) of each group within the use intensity group and use flexibility group series to label each group as either ‘high value (H)’, ‘medium value (M)’, or ‘low value (L)’. Then, we segregated all the pocket park samples into nine usage modes, according to the combined results of the three labels, including the modes of H-UF and H-UI, H-UF and M-UI, H-UF and L-UI, M-UF and H-UI, M-UF and M-UI, M-UF and L-UI, L-UF and H-UI modes, L-UF and M-UI modes, and L-UF and L-UI. By analysing the distribution of samples in these nine usage modes and the user structure of each mode, we can reveal the service features of the pocket parks in the study area.

3. Results

3.1. Characteristics of Pocket Park Use in Nanjing

A total of 55,609 recreational park uses were observed in all samples over 10 days, including 26,332 on weekdays and 29,277 on weekends. The park use intensity for each category was higher on weekends than on weekdays, while the composition structures of the five park use categories on weekdays and weekends were similar (Figure 4). Meanwhile, the use intensity of the pocket parks during each investigation period on weekends was also higher than that on weekdays (Figure 5).
According to our investigation data, pocket park use was the lowest in the early morning (6:30–8:30) and the highest at night (18:30–20:30) on both weekdays and weekends. In addition, the use intensity continued to rise from the early morning into the evenings on both weekdays and weekends. The difference was that pocket park use increased rapidly in the following mornings on the weekends. The pocket park use at night was 24.5% and 10.7% higher than in the late morning (9:30–11:30) and afternoon (14:30–16:30), respectively. However, usage after the late morning on weekdays was relatively stable, with the use at night only being 4.5% and 1.6% higher than that in the late morning and afternoon, respectively.
Moreover, the composition structures of the five park use categories across the four periods on weekdays and weekends were also similar (Figure 6). In the early morning, pocket parks were mainly occupied by group activities, physical activities, and relaxation and entertainment in a balance mode. Then, relaxation and entertainment dominated the use of pocket parks in the late morning and afternoon. At night, group activities returned to pocket parks and became the main activity category in this period.

3.2. Results of the Models

The results of each model within Model series 1 and 2 are shown in Table 4. The F-value showcases that the results of all the regression models are significant, reflecting a good fitting degree of the data to the models. The average variance inflation factor (VIF) value of all the models was lower than 1.5, with the highest VIF of a single variable being less than 2, indicating no collinearity between the independent variables.
Based on the results of Model series 1, the weekday model (Model 1) and weekend Model (Model 2) explain 34% and 21% of the variance in pocket park use flexibility, respectively. Meanwhile, as shown in Model series 2, the weekday model (Model 2) and weekend model (Model 3) explain 59% and 51% of the variance in pocket park use intensity, respectively. The R-square values of the contrast models in Model series 2 are significantly higher than those in Model series 1, indicating that the factors affecting pocket park use flexibility are much more complex than those affecting pocket park use intensity. Moreover, the R-square values of two weekday models are also higher than those of the corresponding weekend models within the model series, indicating that the factors affecting pocket park use on weekends are more complex than those affecting pocket park use on weekdays.

3.3. Factors Affecting Pocket Park Use Flexibility and Intensity

The test results for the variables are presented as the standardised beta coefficient t-value, with a p-value of 0.05 indicating statistical significance (Table 4). All statistically significant variables had consistent signs across all models within each series.
The results of Model series 1 reveal that the boundary building ratio was significant and represents positive predictors of use flexibility for both the weekday model and weekend model, whereas passer-by density was significant and a negative predictor of use flexibility for both the weekday model and weekend model. The paved ground area and resident population only had a significant and positive effect in the weekday model, whereas LUE only had a significant and negative effect in the weekend model. According to the significance level, paved ground area, passer-by density, and resident population were more influential on weekdays than on weekends, whereas the boundary building ratio and LUE were more influential on weekends than weekdays.
The results of Model series 1 show that the paved ground area, tree canopy coverage area, exercising facility capacity, and LUE were statistically significant and positive predictors of use intensity in both the weekday model and weekend model. The passer-by density had significant and positive effects on use intensity only in the weekend model. According to the significance level, the influence of the surrounding LUE index was stronger on weekdays than on weekends, whereas the influence of passer-by density was stronger on weekends than on weekdays.

3.4. Relationship between Use Flexibility and Use Intensity of Pocket Parks

A correlation analysis was carried out between the pocket park use intensity and flexibility on weekdays and weekends (Table 5). The results reveal that the pocket park use intensity on weekdays correlates positively and significantly with pocket park use flexibility on weekdays, indicating that an increase in pocket park use promotes its use flexibility on weekdays. However, this correlation effect was not significant on weekends.
The results of k-means clustering (Table 6) showed that, in our study area, 41.89%, 40.54%, and 17.57% of the samples ranked as high use flexibility (H-UF), medium use flexibility (M-UF), and low use flexibility (L-UF), respectively, while 9.46%, 13.51%, and 77.03% of the samples ranked as high use intensity (H-UI), medium use intensity (M-UI), and low use intensity (L-UI), respectively. Table 7 shows that only 5.41% of pocket parks served in the H-UF and H-UI modes. The usage modes with the most samples were the M-UF and L-UI modes (33.78%). However, the number of the pocket park samples in the H-UF and H-UI modes, M-UF and H-UI modes, and H-UF and M-UI modes was only about 18% of the total samples, while still serving more than half of the users (Figure 7). These three usage modes were used the most, either for relaxation and entertainment or for group activities. In contrast, pocket parks in the L-UF and L-UI mode were used the most for travelling through.

4. Discussion

This study developed a model series to analyse the factors influencing the use flexibility of pocket parks. We adopted the SWI to quantify pocket park use flexibility based on a field survey of 74 samples from Nanjing city and analysed 7 factors, including the park’s internal and surrounding conditions. For our case study, the main results of the modelling series are as follows.
For the park internal condition category, paved ground promoted pocket parks’ use flexibility and intensity because it allows for a greater diversity of activities. Correspondingly, for densely-populated areas with scarce land resources, any site with sufficient area and no barriers should be considered as a priority option in the design of urban spaces to promote service efficiency and compatibility of pocket parks. The passer-by density had a negative impact on pocket park use flexibility, while positively promoting pocket park use intensity. As passers-by tend to promote certain park uses, including sitting at one’s leisure, eating, or drinking, while disturbing other activities, such as group activities, children’s playing, and most physical activities; balanced strategies in spatial design and management should mitigate this scenario. For example, encouraging passers-by to walk around the main activity site of a pocket park rather than having them pass through it would be an effective approach in park design and management. Moreover, although ‘tree canopy coverage area’ and ‘exercising facilities’ did not show any significant effects on pocket park use flexibility, they were found to significantly promote pocket park use intensity. Owing to the significant and positive correlations between pocket park’s use flexibility and intensity on weekdays, these factors might also indirectly promote pocket park use flexibility.
Regarding the surrounding condition, the boundary building ratio significantly promotes pocket park use flexibility, partially verifying the support effect on park use from the facilities (such as public toilets, restaurants, and retail shops) within the buildings around. In addition, according to our investigation, certain activities, such as chess or card playing, group dancing, relaxation, and physical activities, are more likely to occur in quiet or undisturbed places whose boundaries are well defined and partly enclosed, such as those located within the interstices between buildings [34,54]. Therefore, more focused strategies are needed in pocket park design to achieve a proper balance between the openness versus the enclosure of park spaces. Our study also found that pocket park use flexibility is significantly promoted by the surrounding residents and LUE. This is most likely because these two factors contribute to the diversity of the park’s use demands from its surroundings [27,55]. However, the action mechanisms of these two factors differ. The surrounding resident population significantly promoted pocket park use flexibility on weekdays, while LUE only possessed a significant and positive effect on weekends. Moreover, LUE also significantly promoted pocket park use intensity on both weekdays and weekends, whose influence is much stronger than the surrounding population. This indicates a high diversity of pocket park users in a densely populated district. Therefore, to ensure the future use efficiency of pocket parks in planning, higher priority for pocket park installation should be given to the areas with mixed land use than those with single land use. Moreover, for the existing pocket parks, improving the connections between pocket parks and surroundings through greenways, footpaths, or bike lanes is also helpful to increase the number and diversity of visitors [26,56].
According to the clustering results of pocket park usage modes, most pocket parks in Nanjing can be used flexibly, even including those with low use intensity. In contrast, different pocket park clusters exhibited highly disparate use intensity. The mean visit intensity of the H-UI cluster was about eight times higher than that of the L-UI cluster, reflecting extremely unbalanced service efficiency between the pocket park samples. However, only less than 10% of the pocket park samples in Nanjing had relatively high use intensity, which indicated great potential for most pocket parks to improve their service efficiency. The results of user distribution in each usage mode show that the pocket parks with high UF and high UI are dominated by long-time activities (such as leisure and entertainment, collective dance, and gymnastics), and the pocket parks with low UF and low UI are dominated by traversing activity. Correspondingly, to effectively improve the service effectiveness of the pocket parks in Nanjing, more space for people to stay for a longer time, as well as a proper arrangement of the passers-by, are recommended in future park design. This is also consistent with the results of the model series.

5. Conclusions

This study offers the following three key contributions to pocket park research, discourse, theory, and practice. First, our study provides a framework that quantitatively measures pocket park use flexibility; our model series apply to various studies on public spaces. Second, this study revealed both the positive and negative factors affecting pocket park use flexibility and intensity on weekdays and weekends. These findings will contribute to formulating guidelines for future park planning and the wider urban design in Nanjing and similar cities. Third, our results quantitatively verified the correlation between the various factors and their action mechanisms on pocket park use flexibility and intensity. This correlation will inform strategies in park planning, design, and management. Although this study took the pocket parks within Nanjing as the study samples, our methodology and findings apply to both Chinese and international planning contexts that include areas with a similar population density and lifestyle.
This study also presents several areas for future research. First, the flexible use of pocket parks is a complex process determined by numerous quantifiable and unquantifiable factors. Limited by data precision, data integration, and analysis technology, we could not identify all the relevant factors in the models. Second, due to limited data accessibility, we could not effectively analyse the impact of certain socioeconomic characteristics (e.g., age, sex, profession, and income level) of user groups on pocket park use. Third, because of the limited sample number and the high collinearity between variables, we had to combine certain types of boundary facilities into categories and could not identify the detailed effect of each one individually. Fourth, because the pocket park samples were widely distributed across the city and there are no accessible data that precisely reflect the use details of each one, we could only record the use conditions of the samples in a limited period of two hours via our field survey. Given that the sample data are not precisely space–time synchronous, the accuracy of our results may have been influenced. These limitations could be addressed in future studies and provide scope for extending the current research.

Author Contributions

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

Funding

This research was funded by the National Natural Science Foundation of China, Grant No. 51978146 and the Science and Technology Projects of Ministry of Housing and Urban-Rural Development of China, Grant No. 2018-K2-005.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 11 01419 g001 550
Figure 1. Study area and distribution of pocket park samples.
Figure 1. Study area and distribution of pocket park samples.
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Figure 2. The use of pocket park sample No. 28 on 13 November 2020 (weekday, sunny, 16 °C on average throughout the day): (a) 6:30–8:30, (b) 9:30–11:30, (c) 14:30–16:30, (d) 19:30–21:30.
Figure 2. The use of pocket park sample No. 28 on 13 November 2020 (weekday, sunny, 16 °C on average throughout the day): (a) 6:30–8:30, (b) 9:30–11:30, (c) 14:30–16:30, (d) 19:30–21:30.
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Figure 3. Modeling roadmap.
Figure 3. Modeling roadmap.
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Figure 4. Distribution of the five pocket park use categories on weekdays and weekends.
Figure 4. Distribution of the five pocket park use categories on weekdays and weekends.
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Figure 5. Pocket park uses observed across the four investigation periods on weekdays and weekends.
Figure 5. Pocket park uses observed across the four investigation periods on weekdays and weekends.
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Figure 6. The composition structures of the five park use categories across the four periods on weekdays and weekends.
Figure 6. The composition structures of the five park use categories across the four periods on weekdays and weekends.
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Figure 7. Distribution of park users in usage modes and activities.
Figure 7. Distribution of park users in usage modes and activities.
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Table
Table 1. Classification framework of recreational activities within pocket parks.
Table 1. Classification framework of recreational activities within pocket parks.
CategoriesContentsRepresentative Activities
Recreational activitiesPhysical activitiesEquipment-based fitness, tai chi, rope skipping, shuttlecock kicking, skating, roller skating, ball playing, badminton playing, top whipping, and diabolo playingLand 11 01419 i001Land 11 01419 i002Land 11 01419 i003
(a) Equipment-based fitness(b) Tai chi(c) Badminton playing
Relaxation and entertainmentEating, drinking, reading, sitting leisurely, squatting to rest, dog walking, walking with caged birds, photography, card playing, chess-playing, mahjong playing, singing, and playing a musical instrumentLand 11 01419 i004Land 11 01419 i005Land 11 01419 i006
(d) Eating and drinking(e) Sitting leisurely(f) Mahjong and card playing
Family and social interactionsLooking after or playing with children, chatting, and communicating while playing with pets Land 11 01419 i007Land 11 01419 i008Land 11 01419 i009
(g) Looking after children(h) Chatting(i) Communicating while playing with pets
Participation in group activitiesSquare dancing, sword dancing, and aerobicsLand 11 01419 i010Land 11 01419 i011Land 11 01419 i012
(j) Square dancing(k) Sword dancing(l) Aerobics
Other activitiesStanding and waiting, pacing within the park(s) (differentiated from ‘travel through parks’ through our field survey), being on a phone, and so on Land 11 01419 i013Land 11 01419 i014
(m) Standing and waiting(n) Being on a phone
Non-recreational activitiesTravelling throughTravelling through parks either by foot or by bikeLand 11 01419 i015Land 11 01419 i016
(o) Travelling through parks by foot(p) Travelling through parks by bike
Table
Table 2. Independent variables’ descriptions and data sources.
Table 2. Independent variables’ descriptions and data sources.
CategoryVariablesDescription (Unit)Data Source
Internal conditions Paved ground areaArea of paved ground (hm2) larger than 12 m × 12 mMaxar Worldview-03 images of Nanjing (April 2020)
Tree canopy coverage areaArea of the site covered by the tree canopy (hm2)Maxar Worldview-03 images of Nanjing (April 2020)
Exercising facility capacityThe capacity of facilities that provide opportunities for physical activities, including outdoor fitness equipment for adults and children (people)Field survey (October 2020)
Passer-by densityThe density of people travelling through pocket parks (people/hm2)Field survey (autumn, 2020 and spring, 2021)
Surrounding conditionsBoundary building ratioThe ratio of boundary building length according to park perimeterBaidu Map (2020)
Resident populationNumber of residents in the surrounding 300 m area of each pocket park (people)Lianjia.com (2020)
Land mixed-use levelThe entropy of land use types in the surrounding 300 m area of each pocket parkBaidu Map (2020)
Table
Table 3. Descriptive statistics of the independent variables.
Table 3. Descriptive statistics of the independent variables.
CategoryVariables (Unit)MeanStd. DevMin.–Max.
Internal conditionsPaved ground area (hm2)0.0680.0540.000–0.240
Tree canopy coverage area (hm2)0.1980.1680.004–0.849
Exercising facility capacity (people)3.5819.8330–60
Passer-by density (total, people/hm2)3696700–3466
(weekdays, people/hm2)1863150–1536
(weekends, people/hm2)1843660–1930
Surrounding conditionsBoundary building ratio0.3540.2100–0.96
Resident population (people)112378284125–34245
Land mixed-use level (land use entropy)0.7340.1000.349–0.912
Note. Std. Dev. = standard deviation, Min. = minimum, Max. = maximum.
Table
Table 4. Results from regression model series (n = 74).
Table 4. Results from regression model series (n = 74).
CategoryVariablesUse Flexibility (Model Series 1)Use Intensity (Model Series 2)
Weekday ModelWeekend ModelWeekday ModelWeekend Model
NCCtNCCtNCCtNCCt
Internal conditionsPaved ground area0.2802.718 **0.1050.9300.3504.298 **0.3273.695 **
Tree canopy coverage area0.1991.6220.0310.2380.3773.877 **0.3463.328 **
Exercising facility capacity−0.043−0.385−0.041−0.3320.2833.193 **0.2792.888 **
Passer-by density−0.420−3.546 **−0.304−2.477 *−0.099−1.0610.2352.434 *
Surrounding conditionsBoundary building ratio0.2532.356 *0.3522.988 **0.0760.8980.0991.071
Resident population0.2872.511 *0.1781.4510.0921.013−0.007−0.070
Land mixed-use level−0.081−0.7990.2942.631 **0.2162.685 **0.1872.130 *
Observed users26,33229,27726,33229,277
R20.4030.2810.6270.555
Adj R20.3390.2050.5870.508
F-value6.357 **3.685 **15.823 **11.769 **
Mean VIF1.3771.3341.3771.334
Note. * p < 0.05, ** p < 0.01, VIF = variance inflation factor, NCC = normalised correlation coefficient.
Table
Table 5. Bivariate correlation between the use flexibility and use intensity of the sampled pocket parks (n = 74).
Table 5. Bivariate correlation between the use flexibility and use intensity of the sampled pocket parks (n = 74).
Weekday Use IntensityWeekend Use Intensity
Spearman CorrelationpSpearman Correlationp
Weekday use flexibility0.4580.000 **
Weekend use flexibility 0.1420.227
Note. * p < 0.05, ** p < 0.01.
Table
Table 6. Results of k-means clustering according to total use flexibility and total use intensity.
Table 6. Results of k-means clustering according to total use flexibility and total use intensity.
Total Use FlexibilityTotal Use Intensity (People/ha)
HMLHML
Mean value1.4201.2000.89328731595343
n31301371057
%41.8940.5417.579.4613.5177.03
Table
Table 7. Use mode distribution of pocket parks.
Table 7. Use mode distribution of pocket parks.
Total Use Flexibility
HML
nps (%)pu (%)nps (%)pu (%)nps (%)pu (%)
Total use intensityH45.4118.5334.0518.47000
M68.1115.4122.705.8822.705.01
L2128.3814.712533.7812.691114.869.31
Note. ps = proportion of samples, pu = proportion of users.
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Zhou, C.; Xie, M.; Zhao, J.; An, Y. What Affects the Use Flexibility of Pocket Parks? Evidence from Nanjing, China. Land 2022, 11, 1419. https://doi.org/10.3390/land11091419

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Zhou C, Xie M, Zhao J, An Y. What Affects the Use Flexibility of Pocket Parks? Evidence from Nanjing, China. Land. 2022; 11(9):1419. https://doi.org/10.3390/land11091419

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Zhou, Conghui, Meng Xie, Jin Zhao, and Yihuan An. 2022. "What Affects the Use Flexibility of Pocket Parks? Evidence from Nanjing, China" Land 11, no. 9: 1419. https://doi.org/10.3390/land11091419

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