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Humanities and Social Sciences Communications volume 11, Article number: 828 (2024)
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In today’s world, creating safe, comfortable, and beautiful slow communities has become an urgent research topic for addressing energy crises, environmental pollution, and traffic congestion. This study explored the relationship between the built environment and residents’ leisure-time physical activities in slow communities in Shanghai. This study uses the analytic hierarchy process and entropy weight method to construct a new evaluation model to explore the sustainability mechanism. The results of the study are as follows. First, women participated in walking, jogging, and bicycling activities at a higher rate than men. Second, various leisure-time physical activities have different requirements for the built environment. Third, the built environment of slow communities in Shanghai shows a “pyramid” type of spatial stratification phenomenon. This study contributes to a new evaluation system and optimization model for promoting leisure-time physical activities, providing theoretical and methodological guidance for constructing livable slow communities in developing countries and promoting slow living.
Traffic congestion is a serious problem in many cities during urban development and is a global challenge. Traffic congestion not only compromises the essence of sustainable urban development but also highlights the critical necessity for adopting pedestrian-first principles in developing safe, comfortable, and visually appealing slow communities. Nevertheless, the transition towards these communities presents complex challenges, particularly concerning energy crises, environmental pollution, and traffic congestion. Slow communities strive to enhance pedestrian mobility and decrease dependence on motor vehicles, facing specific hurdles. These challenges include the need for considerable infrastructural modifications amid energy crises, hindering the sustainable support of such transformations without reliance on fossil fuels (Markovska et al. 2016; Zhong et al. 2024). Furthermore, environmental pollution exacerbates the difficulty of this transition, as reducing vehicle usage in congested areas requires extensive planning and community engagement to lower emissions (Rajé et al. 2018; Zhong and Li, 2024). Additionally, the ongoing issue of traffic congestion demands direct action. Slow communities aim to mitigate this problem by implementing pedestrian-centric urban layouts, which necessitate re-evaluating the current infrastructure, which depends heavily on vehicular transport. This re-evaluation includes redesigning urban spaces to integrate more greenery, pedestrian paths, and bike lanes, thus promoting a shift in public transportation methods and diminishing the urban carbon footprint (Prasara-A and Bridhikitti, 2022). It is urgent to study and address the issue of how to return to the “pedestrian-first” concept and construct safe, comfortable, and beautiful Slow Communities. As of November 2022, 287 international slow cities were distributed in 33 countries and regions worldwide. Moreover, the Chinese government proposed the “14th Five-Year Plan for National Economic and Social Development of the People’s Republic of China” to promote sustainable urban transportation development, strengthen the construction of green, low-carbon transportation such as walking, cycling, and public transportation, and construct a “pedestrian-vehicle separation, pedestrian-first” urban road system. Overall, slow city planning is a global concept that many countries are actively exploring and implementing to improve urban sustainability and quality of life for residents.
Leisure-time physical activities are becoming increasingly important, and disciplines such as human geography, urban planning, and urban sociology are paying more attention to the quality of humans and improving the environment in cities (Chen et al. 2023; Zhong et al. 2023). Researchers are paying more attention to residents’ outdoor leisure-time physical activities and spatial patterns (Masoumi, 2017; Patria, 2022; Wali et al. 2022). Therefore, examining the changes in macro urban spatial structure from the perspective of residents’ leisure-time physical activities has become a research hotspot both domestically and internationally. As early as 2002, Handy et al. proposed that the built environment is an important factor affecting residents’ health and physical activity (Handy et al. 2002; Bauman et al. 2012; SallisFloyd et al. 2012). Various studies have confirmed that a good spatial environment can encourage residents to engage in more outdoor leisure-time physical activities, reducing the risk of cerebrovascular disease and obesity (Pinto et al. 2021; Koreny et al. 2022). Other studies have found that optimising the community spatial environment can create a sense of environmental maintenance among residents, thereby promoting further adoption of green and slow modes of transportation, such as walking and cycling (Pucher et al. 2010; de Nazelle et al. 2011; Wang et al. 2016; Boakye et al. 2023). Regarding green space in communities, the elderly population has a greater demand, frequency of use and duration than other groups. They also participate in physical activities more frequently in larger green spaces (Gong et al. 2014; Wu et al. 2021). Many literature sources emphasise the positive role of the spatial environment (Masoumi, 2017; Patria, 2022; Wali et al. 2022; Zhong et al. 2022), including the built environment, social environment, and natural environment, in residents’ activities. In addition, the spatial environment contributes to human well-being by providing residents with motivation for activities and promoting physical and mental health (Cooper et al. 2011; Van Dyck et al. 2013; McCormack, 2017; Pinto et al. 2021; Koreny et al. 2022). Therefore, sufficient comfort in the spatial environment is increasingly regarded as a basic standard for liveable slow urban environments.
However, previous research has focused mainly on constructing slow transportation systems at the macro level (Gilat et al. 2003; Sinha, 2003; Aygun et al. 2021; Reul et al. 2021; Szell et al. 2022). Sinha (2003) analysed urban data from around the world over the past few decades and found that profound changes in urban structure and activities can slow or reverse the growth of private car use and make transportation and other methods attractive and feasible, thereby greatly improving the sustainability of urban transportation systems. Aygun et al. (2021), using Sigacik as an example, pointed out that cities face threats such as a lack of transportation and infrastructure and increased vehicle traffic, deviating from the slow city standards. Szell et al. (2022) suggested that cycling is a promising solution to unsustainable urban transportation systems. However, the current development of cycling networks follows a slow and segmented process without considering the structural complexity of transportation networks. Therefore, they analysed and discussed the topological limitations of bicycle network development in 62 cities. They all start at the municipal, provincial, and regional levels, with little focus on the community scale. This study fills this gap from the community perspective. As developing countries climb the ladder of urbanisation, slow communities are increasingly emphasised as a goal for high-quality living. In China, the construction of slow communities has been a planning goal since the reform and opening to optimise or transform the built environment to actively promote leisure-time physical activities such as walking, jogging, or cycling among residents. Microlevel construction of slow communities can not only fill the gaps in spatial planning during urban construction but also more directly influence residents’ leisure-time physical activities (Cho, 2011; Botta, 2016; McCormack, 2017; Criss et al. 2023). The main elements of slow community construction are the material components of the community (i.e., the built environment), which serve as important spatial carriers for residents’ leisure-time physical activities (Handy et al. 2002; Bauman et al. 2012; Sallis et al. 2012). Therefore, it is crucial to construct an evaluation system for slow community construction based on the factors that influence the built environment for leisure-time physical activities to promote the construction of slow communities and even the entire city. Unfortunately, the current identification of the built environment elements of slow communities is based on general surveys and qualitative judgements, spatial experience index measurements, and subjective evaluation scores (Botta, 2016; Raco et al. 2018; Goldhagen, 2019). For example, Botta (2016) attempted to classify the most common sustainable communities by providing a more comprehensive theoretical framework to analyse the development of the concept of slow living. Through case studies analysing the results of psychological and neurocognitive research, Goldhagen (2019) suggested that architects, landscape designers, and urban planners should adopt a scientific phenomenological approach to designing thriving, slow-walking, healthy urban environments. Although these methods quantify some slow environment indicators, the scoring methods are somewhat subjective. This cannot reflect objective facts and leads to certain errors in constructing a slow community-building evaluation system.
To fill this research gap, slow communities emphasise slowing the pace of life to improve quality of life, especially health and environmental sustainability. Therefore, this study explores the construction elements of slow communities, investigates the relationship between the built environment of slow communities in Shanghai and residents’ slow activities, and constructs a slow environment assessment system. Evaluating the quality of the built environment of slow communities from multiple dimensions helps to promote the development of slow communities in China. Our research questions are: (1) What are the characteristics of residents’ leisure-time physical activities? (2) Which built environment elements of slow communities are related to residents’ leisure-time physical activities? (3) What are the characteristics of the development of slow communities calculated by the evaluation system of the built environment of slow communities? These research questions are directly related to the slow lives of residents, especially in terms of exploring the impact of the built environment on residents’ slow activities. Through these questions, the aim is not only to identify the built environment elements that affect residents’ leisure-time physical activities but also to understand how these elements specifically impact residents’ quality of life and health. To answer the above questions, this paper makes the following innovations. First, it focuses on understanding the motivations, frequency, and impact on the quality of life of residents’ participation in slow activities from a community perspective, reflecting the emphasis of slow-living theory on details and individual experiences. Existing research may overlook the characteristics of a particular community from a municipal, provincial, or even national perspective. Second, this paper uses a multiple regression model to reveal the main built environment factors affecting residents’ leisure-time physical activities under activity differentiation. Exploring how to promote healthy and sustainable lifestyles by optimising the community’s built environment reflects the focus on environmental improvement and life pace within the theory of slow living. Third, this paper uses analytic hierarchy process analysis and the entropy value method to construct an evaluation system for leisure-time physical activity systems from subjective and objective perspectives. Compared to a single evaluation method, this approach provides more comprehensive and precise results. By constructing an evaluation system, this study aims to provide a quantitative and scientific method for assessing the development of slow communities, further promoting the application of slow living in urban planning and community development.
Shanghai, located in the eastern region of China’s Yangtze River Delta, covers 6,340.5 square kilometres. As of the end of 2022, the city has 16 districts. Shanghai’s geographical location is unique. It is bordered by the East China Sea to the east, Hangzhou Bay to the south, and the provinces of Jiangsu and Zhejiang to the west. To the north is the mouth of the Yangtze River. These geographical and hydrological conditions have a profound impact on urban spatial planning, transportation networks, and residential environments. At the same time, Shanghai has a long history of urban development, a blend of modernity and tradition. The city’s diverse urban space and architectural styles provide rich materials for the construction environment of this study on slow communities.
Since the release of the 2013 Shanghai Transportation Development White Paper (draft for public comment), Shanghai has focused on improving the quality of walking and cycling environments and ensuring the basic right of a “slow life”. In 2022, Shanghai released a new round of the Shanghai Transportation Development White Paper, which focused more on enhancing citizens’ slow travel experience, emphasising the development of slow travel as a priority and improving residents’ travel experience. Based on the sustainable transportation policy of the Shanghai municipal government, this study provides strong support for an in-depth understanding of how to effectively promote the development of residents’ slow lives in large modern cities.
Based on the above factors, this study selected Shanghai as the research object. Considering the significant differences in population density within the central urban areas, the complex and diverse urban spatial environment, and the availability and coverage of data, seven districts in the central area of Shanghai were selected. They are the Hongkou, Huangpu, Jing’an, Putuo, Xuhui, Yangpu, and Changning districts. To evaluate the composite score of the built environment of slow communities, ArcGIS 10.2 software was used, and 1000 × 1000 m grids were used as the basic evaluation unit to analyse the spatial distribution characteristics of slow communities in Shanghai (see Fig. 1).
Study area map (Source: author’s drawing).
The data related to the built environment used in this study were obtained from the Resource and Environment Science Data Center (http://www.resdc.cn/). The urban land use classification data were obtained from the current land use map of the Shanghai Master Plan, which was rasterised and adjusted according to the actual construction situation. The road data were processed and standardised based on data obtained from the Open Street Map (OSM) and adjusted according to the current road conditions in Shanghai.
This study used Python programming to obtain the original slow travel activity trajectory data from a sports app company in Shanghai from 2016 to 2018. This period was selected for two reasons: (1) 2019 was the beginning of the outbreak of the COVID-19 pandemic, which severely restricted the public’s ability to engage in slow physical activity. Therefore, very few post-2019 exercise data are available, and these data lack scientific validity for natural experimental research. (2) A three-year study period helps to observe the differences in public slow travel physical activities more scientifically. The trajectory data are presented in the form of .txt files, each containing geographic latitude and longitude coordinates ranging from tens to thousands, representing the trajectory information of different users in Shanghai. These trajectory point coordinates will be further processed and analysed as the raw data for the study. Figure 2a shows an example of the processing of trajectory point coordinate information. The specific processing steps (see Fig. 2b) are as follows: The first step is to convert these coordinates into trajectory points in ArcGIS 10.2 and then connect them to form a movement trajectory. The second step is to link the user ID of each trajectory with the attribute table in ArcGIS software after batch conversion of trajectories to match relevant movement information (Zhong et al. 2022). The third step was to use the “identification” tool in ArcGIS software to filter out valid trajectory data within the research scope and exclude invalid trajectories with abnormal movement factors, such as location deviation, short exercise time (<1 minute), short activity distance (<0.1 km), and slow speed (<5 km/h), which were removed (the speed anomaly was defined based on the minimum jogging speed standard (Greiwe and Kohrt, 2000; Gazendam and Hof, 2007; Xu et al. 2018)). Among the remaining datasets, users who engaged in walking, jogging, and cycling activities were selected as research samples based on activity type identification (all personal data were deleted to protect privacy). The processed dataset contained 19,535 valid trajectory data points and included activity trajectories of different slow travel activities (walking, running, and cycling) (see Fig. 3).
Raw trajectory data processing (Source: author’s drawing and Zhong et al. 2022).
Different types of leisure-time physical activity trajectories (Source: author’s drawing).
This study aims to comprehensively evaluate the built environment of slow communities. Therefore, this study builds on the 5D factor framework proposed in previous studies (Zhang et al. 2020; Chen et al. 2022; Wan et al. 2022; Wu et al. 2022) as the research foundation. Second, when selecting relevant indicators, reference was made to previous studies in the built environment and physical activity (Fuzhong et al. 2005; Sun et al. 2017; Sun and Yin, 2018; Zhang et al. 2020; Chen et al. 2022; Koreny et al. 2022; Wu et al. 2022), combined with the specific characteristics of slow communities, to choose indicators covering a wide range of factors. The classification and logic of these indicators are as follows: density factors depict the actual conditions of residents’ daily activities and are crucial for comprehending the spatial layout of communities and resident interactions. Diversity factors aid in analysing the variety of urban land use and are closely linked to residents’ lifestyles and the comprehensive functions of communities. The design factors reflect transportation development and the natural environment of the area, impacting residents’ comfort and travel convenience. Distance to transit factors concentrates on residents’ transportation transfer convenience, directly affecting travel efficiency and convenience. Destination accessibility factors gauge the diversification and attractiveness of public service facilities, significantly improving residents’ quality of life. Previous studies have demonstrated the effectiveness of these indicators in understanding and evaluating urban environments, ensuring a comprehensive assessment of multiple dimensions influencing residents’ daily quality of life and community vitality.
In addition, to quantify the built environment indicators more effectively, this study constructed a 1000 × 1000 m grid as the basic evaluation unit and calculated the values of various environmental indicators within each unit. In addition, this study added the normalised slow distance and frequency without weighting to obtain the intensity of leisure-time physical activity as the dependent variable of this study. Based on this, a summary table of built environment indicators for slow communities (Table 1) was initially constructed.
In addition, this study is divided into the following sections (see Fig. 4):
Descriptive statistics of residents’ leisure-time physical activity characteristics were analysed using SPSS 21.0, and Origin 18 was used for visualisation. The figure mainly shows the differences in leisure-time physical activities among different gender groups, as well as the distribution of different types of leisure-time physical activities among residents at different times.
The obtained data on the built environment of slow communities were subjected to reliability analysis, factor analysis, correlation analysis, and collinearity analysis to obtain reliable indicators of the built environment through data verification.
A multiple linear regression equation was used to explore the relationships between variables. Model 1 was constructed to investigate the impact of the built environment of slow communities on the overall intensity of residents’ leisure-time physical activities. Model 2 was established to examine the influence of the built environment of slow communities on different types of residents’ leisure-time physical activities, revealing the main built environment factors that affect residents’ leisure-time physical activities under activity differentiation.
The obtained indicators of the built environment were assigned weights using the analytic hierarchy process (AHP) and the entropy weight method to construct an evaluation system for the built environment of slow communities. The basic principle of the AHP is to treat the complex problem to be studied as a large system, analyse multiple factors in the system, delineate the ordered hierarchy of interrelationships between factors, and then ask experts to make relatively objective judgements on each factor of each layer, giving quantified representations of relative importance. Mathematical models are then established to calculate all factor’s weight in each layer and rank them (see Fig. 5). The rationale for employing AHP lies in its ability to break down complex, multidimensional issues into simpler, hierarchically structured components. This breakdown is vital for tackling the intricate relationship between the built environment and leisure-time physical activities, allowing for a systematic analysis of various elements and their interconnections. AHP facilitates the incorporation of expert judgements, transforming subjective assessments into quantified weights, thereby enabling a structured decision-making process (Sipahi and Timor, 2010). However, AHP is not without its limitations. One of the primary concerns is its potential for subjectivity in expert judgements, which could introduce bias. Additionally, the method’s reliance on pairwise comparisons might lead to inconsistencies, particularly with large sets of factors (Ishizaka and Labib, 2011). To mitigate these limitations, we integrated the entropy weight method, which determines weights based on the variability of indicator values, thus providing an objective counterbalance to the AHP’s subjective elements (Zhu et al. 2020). The entropy weight method complements the AHP by reducing the subjectivity in weight assignment. It leverages the inherent data characteristics of the indicators, assigning weights based on their distribution and information content. This objectivity is crucial for validating the evaluation model, especially in diverse and dynamic urban environments (Nyimbili and Erden, 2020). Nonetheless, the entropy method also has limitations. It may not fully capture the nuances of expert knowledge or the contextual importance of certain indicators, potentially overlooking subjective yet critical aspects of the built environment. By combining AHP and the entropy weight method, our study aims to create a balanced and robust evaluation system for the built environment of slow communities, addressing the multifaceted nature of leisure-time physical activities. This dual-method approach allows for a comprehensive analysis, integrating expert insights and objective data analysis, thereby enhancing the reliability and validity of our results.
In addition, the experts selected for this study were individuals with rich experience and a deep understanding of the field, and their selection criteria included educational background, industry experience, and professional knowledge (Table 2). These experts were contacted by phone or consulted offline, and the purpose and tasks of the research were explained to them in detail. The main responsibility of the experts is to participate in the pairwise comparison process of the AHP, and their answers directly influence the calculation of indicator weights. Their professional knowledge and experience provided this study with greater credibility and depth. This study distributed 20 questionnaires from November 1, 2022, to December 1, 2022, resulting in 16 responses with a response rate of 80%. This indicates wide study support and provides greater trustworthiness and accuracy.
Process framework for the research methodology (Source: author’s drawing).
AHP application steps diagram (Source: author’s drawing).
Finally, using this evaluation system, the quality of the slow communities under the grid system was calculated, and Geoda and ArcGIS 10.2 were used for spatial autocorrelation analysis and spatial heterogeneity analysis of the slow communities. This study investigated the current development situation of slow communities in Shanghai and proposed relevant optimisation suggestions.
The daily activities of individuals of different genders are subject to different spatial and temporal constraints, and there are also differences in the demand for urban facilities and services. Therefore, this paper reveals the differences in the distribution of different leisure-time physical activities among different gender groups in leisure-time physical communities from a statistical perspective. The number of walking, jogging, and cycling trajectories for different gender groups is shown in Table 3. Among 19,535 trajectories, the number and population of activity trajectories for females were greater than for males. The differences between the two sexes were mainly in walking activities, with a difference of 4443 trajectories and 930 people, followed by jogging activities, with a difference of 1141 trajectories and 309 people; finally, cycling activities, with a difference of 573 trajectories and 74 people. Looking at different activity types, nearly 60% of females and males choose to walk, and the frequency of selecting walking activities is relatively high; the selection rate for jogging activities is relatively low, accounting for approximately 30%, while the selection rate for cycling activities is only approximately 10%.
As shown in Fig. 6, this study counted and analysed the number of walking, jogging, and cycling trajectories for 24 h a day, and the curves of the three activities showed obvious differences in fluctuations. The peak of walking activity is at approximately 9:00, and there is a small peak at approximately 13:00. The peak of jogging activity is at approximately 7:00, and the peak of cycling activity occurs at approximately 8:00. Furthermore, the number of walking activities increases sharply after 4 pm and peaks at approximately 8 pm. The number of jogging activities also increases sharply after 4 pm, peaking at approximately 9 pm. The number of cycles increases sharply after 5 pm, peaking at approximately 6 pm. There are three peak times for walking activities, which are 9 am, 1 pm, and 8 pm. There are two peak times for jogging activities, which are 7 am and 9 pm. There are two peak times for cycling activities, which are 8 am and 4 pm. Overall, walking activities fluctuate while jogging and cycling show a bimodal fluctuation trend.
Statistics of different types of activities by time (Source: author’s drawing).
The reliability and validity of the sample data on the built environment of the slow community were analysed using the Reliability Analysis module of SPSS 21.0 software, as shown in Table 4. The results showed that the reliability coefficient (Cronbach’s alpha) of 21 variables related to the built environment in the slow community was 0.894. In addition, the validity of the data was tested using the KMO and Bartlett’s sphericity tests in the factor analysis module of SPSS 21.0, and the KMO coefficient was found to be 0.893. The results of the reliability and validity tests showed that the sample data had good internal consistency and passed the data verification.
First, Pearson correlation analysis was used to identify the main environmental factors significantly correlated with residents’ leisure-time physical activities and to differentiate the core variables among the related factors, with the correlation coefficient (P) as the measuring standard. When P < 0.05, the two variables are considered to have a certain degree of correlation; when P < 0.01, the correlation between the two variables is considered to be extremely significant. Through correlation analysis, a total of 21 potential environmental elements that affect the intensity of residents’ leisure-time physical activities in slow communities were identified (see Table 5 and Fig. 7).
Heatmap of correlation of built environment factors in slow communities (Source: author’s drawing).
Next, this study conducted a multicollinearity analysis on the 21 potential correlated environmental factors identified through the correlation test (Table 6). The research results showed that the Model R2 was 0.508, indicating a good simulation effect and the overall p-value of the equation was <0.001, which passed the significance test. However, the model also had environmental indicators with VIF values greater than 10, indicating the presence of multicollinearity. Therefore, this study needs to adopt a method of gradually eliminating the independent variables with larger eigenvalues to address the issue of model multicollinearity.
Through tests and analyses of correlation and multicollinearity on the environmental indicators, the final model, after passing the multicollinearity test, no longer exhibited collinearity issues. Multiple linear regression analysis revealed that independent variables impacted the intensity of leisure-time physical activities (Table 7). To verify the accuracy and reliability of the model assumptions, the following analyses were conducted. According to the residual independence test, the Durbin–Watson value was 1.299, indicating data independence. According to the variance results (Table 8), F = 17.558, P < 0.001, suggesting that one or more independent variables explain a portion of the variance in the dependent variable, thereby increasing the regression variability and reducing the residual variability. This indicates successful model establishment with a higher confidence level in the regression conclusions. The residual scatter plot (see Fig. 8) shows that the standardised residual values are distributed near the 0 value, exhibiting a symmetrical distribution. The distribution does not change with increasing predicted values, implying the homogeneity of the data variance. The residual histogram and P-P plot (see Fig. 9) indicate that the residuals follow a normal distribution with a mean close to 0 and a standard deviation close to 1 (standard normal distribution), satisfying the assumption of normality for linear regression. The P-P plot also confirmed the fulfilment of the normality assumption. The model’s R-squared is 0.506, and the adjusted R-squared is 0.477, illustrating a good fit and explanatory power.
Plot of residuals (Source: author’s drawing).
The residual histogram and P-P plot (Source: author’s drawing).
Based on the above test results, it can be concluded that the model assumptions are valid and that the model results are reliable and accurate. In this empirical study, among the built environment factors of slow communities, population density (β = 0.108, p < 0.05), residential land density (β = 0.285, p < 0.001), bus line density (β = 0.164, p < 0.01), bus stop density (β = 0.159, p < 0.01), accessibility to educational facilities (β = 0.209, p < 0.01), and accessibility to scenic spots (β = 0.173, p < 0.01) had significant positive impacts on the intensity of residents’ leisure-time physical activities. In contrast, building density (β = −0.161, p < 0.001), the NDVI (β = −0.17, p < 0.01), and accessibility to commercial facilities (β = −0.182, p < 0.05) had significant negative impacts on the intensity of residents’ leisure-time physical activities.
This study examined the differences in leisure-time physical activities, including walking, jogging, and cycling, using multiple linear regression analysis to investigate the impact of various built environment factors on residents’ participation in these activities (Table 9).
The results show that in the walking activity model, built environment factors, such as population density (β = 0.118, p < 0.05), residential land density (β = 0.301, p < 0.001), bus line density (β = 0.167, p < 0.05), bus stop density (β = 0.186, p < 0.01), accessibility to educational facilities (β = 0.192, p < 0.05), and accessibility to scenic spots (β = 0.119, p < 0.05), have a significant positive impact on residents’ walking activity intensity. The normalised difference vegetation index (NDVI) (β = −0.158, p < 0.01) has a significant negative impact.
In the jogging activity model, accessibility to educational facilities (β = 0.268, p < 0.01) and accessibility to scenic spots (β = 0.178, p < 0.01) have a significant positive impact on residents’ jogging activity intensity, while building density (β = −0.257, p < 0.01) and degree of relief (β = −0.199, p < 0.1) have a significant negative impact.
In the cycling activity model, residential land use density (β = 0.163, p < 0.05), bus line density (β = 0.204, p < 0.01), accessibility to catering facilities (β = 0.278, p < 0.01), accessibility to workplaces (β = 0.174, p < 0.05), and accessibility to scenic spots (β = 0.132, p < 0.05) positively impacted residents’ cycling activity intensity, while building density (β = −0.228, p < 0.01) and urban function mix (β = −0.272, p < 0.001) had a significant negative impact.
In summary, the built environment of slow communities has distinct differential impacts on various leisure-time physical activities. Population density positively impacted walking activity within the built environment of slow communities. Building density negatively impacted jogging and cycling activities. Residential land density positively impacted walking and cycling activities. The mix of urban functions negatively impacted cycling activities. The degree of relief negatively impacted jogging activities. The NDVI negatively impacted walking activities. Bus line density positively impacted walking and cycling activities. Bus stop density positively impacted only walking activity. Access to catering facilities and workplace accessibility have a significant positive impact only on cycling activity. Access to educational facilities positively impacted walking and jogging activities.
Based on previous research and the above tests, this study selects 12 indicators to construct a slow community-built environment evaluation system. Specifically, population density, building density, and residential land density reflect the number of residents and location of their daily activities. The mix of urban functions reflects the diversity of public service facilities and regional attractiveness. The degree of relief and NDVI reflect the comfort of the natural environment, and the bus line density and bus stop density reflect the convenience of residents’ transportation. Access to catering facilities, accessibility to workplaces, accessibility to educational facilities, and accessibility to scenic spots reflect the accessibility and attractiveness of destinations for residents.
Based on the environmental indicators that affect leisure-time physical activities obtained from the regression model, this study used a combination of subjective and objective weighting to determine the indicator’s weights. The study first uses the analytic hierarchy process to determine the subjective weights of the elements. The AHP is a systematic and hierarchical approach to analysis (Mastrocinque et al. 2020; Hu et al. 2021; Sang et al. 2022; Kilic et al. 2023; Zhong et al. 2023). Specifically, 20 experts familiar with the research field were selected to form an expert group to score the evaluation object. Then, we constructed a judgement matrix to calculate the expert’s average score. According to the judgement matrix, we can further derive the weight score of each element.
Where ({a}_{{jn}}) denotes Criterion layer indicator data; (B) denotes the judgement matrix. ({M}_{j}) is the geometric mean of the row vector elements of the judgment matrix; (n) denotes the number of indicators; ({Q}_{j}) is the weight of the jth evaluation indicator; ({{rm{lambda }}}_{max }) is the maximum characteristic root; ({CI}) is the consistency indicator; ({RI}) is the random consistency indicator.
To reflect the importance of the evaluation indicators more comprehensively and accurately, this study continues to use the entropy value method to determine the objective weights of the indicators. This study needs to confirm the information entropy value, and information entropy redundancy and finalize the weights respectively. The specific formula is as follows.
Standardisation of indicator data.
where ({x}_{{ij}}) is the value of the jth indicator standardized for the ith evaluation sample.
The entropy weighting method calculates the indicator weights with the following formula:
where ({{rm{Y}}}_{{rm{ij}}}) is the weight value of the jth indicator of the ith evaluation sample; ({{rm{e}}}_{{rm{j}}}) is the information entropy value of the jth indicator; ({{rm{g}}}_{{rm{j}}}) is the information entropy redundancy of the jth indicator; ({{rm{W}}}_{{rm{j}}}) is the value of the weight coefficient of the jth indicator.
In conclusion, this study determined the weight of each indicator and element, as shown in Tables 10 and 11. The results show that among the various indicator weightings, only three indicators, namely, population density, residential land density, and bus line density, account for 57.226% of all indicators, and the weighting of bus stop density is also as high as 14.382%. The weight distributions of building density, degree of relief, and NDVI are in the third tier of all indicators. The environmental indicators, such as the mix of urban functions, accessibility to catering facilities, accessibility to the workplace, accessibility to educational facilities, and accessibility to scenic spots, are in the last gradient. Among the various element weightings, the density, design, and distance to transit are more prominent, with weights of 26.285, 30.193, and 21.1%, respectively, and the total weighting is as high as 77.578%. The weights of diversity and destination accessibility are similar, at 12.119 and 10.304%, respectively.
Based on the indicators selection and weighting calculations, a comprehensive and effective evaluation system for the built environment of slow communities was constructed (Table 12). Furthermore, the composite scores for the built environment of slow communities can be obtained based on the formula provided below:
Where U represents the evaluation index for the built environment of slow communities. The index is established based on the “5D” model. A represents the density index, ({{rm{W}}}_{{rm{Aj}}}) represents the weight of the jth indicator in the A layer, ({{rm{Z}}}_{{rm{Aj}}}) represents the standardized value of the jth indicator in the A layer, and ({{rm{W}}}_{{rm{A}}}) represents the weight of the A layer. The index setting and calculation for B, C, D, and E are the same as those for A.
Figure 10 shows that Moran’s I value was 0.537, indicating a significant positive spatial autocorrelation for the composite scores of the built environment in slow communities and demonstrating spatial agglomeration.
Slow community built environment composite scores Moran’s I (Source: author’s drawing).
To analyse the similarity and spatial characteristics of the composite scores of individual community units and their neighbouring community units, ArcGIS 10.2 was used to construct a cluster map of the composite scores of each slow community-building environment, as shown in Fig. 11. The distribution of each slow community in the HH, LL, HL, and LH quadrants was significant. The communities in the HH quadrant are mainly concentrated in the central area, while the communities in the LL quadrant are mainly concentrated in the four corners of the region. The communities in the HL and LH quadrants are scattered and few and are mainly distributed around the communities in the HH and LL quadrants. In summary, there is a clear spatial dependency of the composite scores of each slow community-built environment.
a Results of LISA clustering test. b Results of LISA significance test.
To better visualise the spatial differentiation of the composite scores of the slow community construction environment in the central urban area of Shanghai during the analysis process, a natural break point method was applied to divide the types of composite scores of the slow community construction environment based on the normalized scores. ArcGIS software was used for this purpose (Table 13).
As shown in Fig. 12, the composite scores of the built environment in slow communities were ranked in descending order in this study. The results indicate that the trend of the composite scores of the built environment in slow communities has an almost uniform 45-degree downward slope, and the curve is relatively smooth. This suggests that the development differences among various slow-stage communities are relatively balanced.
Trend of the composite scores for the built environment of slow communities (Source: author’s drawing).
Figure 13 shows that there are obvious spatial differences among the slow-regenerating communities. High-value slow communities are distributed in a “polar core” spatial structure, with the “polar core” high-value areas mainly located in the central area of the study area and exhibiting a dispersed concentration state. Slow communities with relatively high values show a typical irregular and open “ring-shaped cluster radiation” spatial structure, and most of them have a certain quality. The slow communities with relatively high values are mainly distributed in the central and northeastern contiguous areas of the urban area. The spatial distribution characteristics of the built environment in edge communities in the southeast, northwest, and southwest tended to be at a medium or relatively low level, indicating weaker sustainable capacity and exhibiting a “patchy cluster radiation” spatial structure. Overall, slow communities exhibit a typical “inner high, outer low” pyramid spatial structure.
Spatial differentiation of composite scores of the built environment in the slow community (Source: author’s drawing).
As shown in the descriptive analysis results, the number of women participating in the three types of leisure-time physical activities, namely, walking, jogging, and cycling, is greater than men, especially in walking activities, which is three times greater. Additionally, the number of trajectories of women engaging in these three types of leisure-time physical activities is also approximately twice as high as men. This may be attributed to the well-developed “feminine civilization” in the modern world and the improvement of the traditional “men work outside, women stay at home” family model. This has enabled housewives, who are often tied up with household chores, to have more autonomous time to engage in leisurely leisure-time physical activities (Blanco and Feldman, 2000; Daminger, 2019; Picchioni et al. 2020; Doan et al. 2022). Second, women may be more concerned about their health and physique and thus more willing to participate in activities that help maintain their shape and health (Gore et al. 2016). Third, for women, these moderate activities may also be a social outlet. By joining walking groups or running clubs, women can make new friends and enjoy the camaraderie of group activities (Morris et al. 2019). Finally, women may tend to choose lower-risk forms of exercise, and walking, running, and cycling are often seen as relatively safe choices, especially in crowded, well-equipped urban environments (Risová and Madajová, 2020). These findings are consistent with a research report on the physical activity levels of different populations in different countries published in the well-known scientific journal The Lance. To encourage more women to participate in leisure-time physical activities, specific measures are taken in community planning. For example, measures such as increasing street lighting, enhancing surveillance, and improving sidewalk conditions are taken to enhance the safety of slow activities, making women more willing to participate. Additionally, establishing community centres or outdoor activity groups encourages women to participate in collective slow activities to meet their social needs. Through community health education programs, women’s awareness of and interest in healthy lifestyles are enhanced, thereby promoting their participation in slow activities. This gender-based community planning approach ensures that urban design is more closely aligned with the actual needs of residents, thus creating a more inclusive, healthy, and vibrant community environment.
Moreover, the results also revealed significant differences in the amount of time residents spent engaging in different leisure-time physical activities. The peak times for walking were 9 am, 1 pm, and 8 pm; for jogging, the peak times were 7 am and 9 pm; and for cycling, the peak times were 8 am and 4 pm. These differences may be due to variations in sunlight and temperature at different times. Different types of leisure-time physical activities have different sunlight and temperature requirements (Duncan et al. 2008; Sumukadas et al. 2009; Klenk et al. 2012; Min et al. 2021; Klimek et al. 2022), but the specific reasons for these differences require further research.
As shown in Fig. 14, the built environment factors of slow communities significantly impact different types of leisure-time physical activities. The following differences are mainly observed: 1. Among the built environment factors of slow communities, population density positively impacts walking activities while building density can negatively impact jogging and cycling activities. These results support each other and further confirm the scientific validity of this study. It is well known that population density and building density have a mutually dependent relationship. With increased population, the number of buildings also increases (Su et al. 2017; Schug et al. 2021). High population density can increase the scale of slow while not impacting jogging and cycling activities can be attributed to the high density of the compact urban environment, which results in the complexity of the road network and reduces the willingness of residents to participate in jogging and cycling activities (Cervero et al. 2009; Cruise et al. 2017; Kotharkar and Bahadure, 2020; Zang et al. 2021; Yin et al. 2022). The environmental requirements for jogging or cycling activities are generally greater than walking environments (Kavanagh et al. 2005; Jansen et al. 2017). These findings suggest that urban planners should increase the width of sidewalks, increase pedestrian crossings and rest areas in densely populated areas, and plan dedicated jogging or cycling paths near areas with high building density; 2. Residential land density positively impacts walking and cycling activities. We believe that increased residential land areas will result in wider road space or activity areas, which facilitate residents’ leisure-time physical activities (Zenk et al. 2011; Carlson et al. 2015; McGreevy et al. 2021), enhance their enthusiasm for leisure-time physical activities, and increase the likelihood of walking and cycling activities. The lack of impact on jogging activities may be because jogging activities are more focused on personal subjective ideas. However, the specific reasons for this difference require further investigation. Urban planning should focus on creating “15-minute communities” where residents can easily walk or cycle to workplaces, shops, schools, and other essential infrastructure; 3. The mix of urban functions negatively impacts cycling activities. This is because when the urban functional mix is high, the community is located in an economic development centre with high-density buildings, crowded road space, and complex traffic systems, the main factors that inhibit residents’ cycling activities (Liu, Yang et al. 2020). This implies that in areas with a high degree of urban functional mix, there is a need to improve cycling infrastructure, such as setting up more bike lanes and safety measures; 4. The degree of relief negatively impacts jogging activities, which is in line with common sense in daily life and further confirms the scientific validity of this study. 5. The NDVI negatively impacts walking activities. A high NDVI indicates that the community’s natural environment is excellent, often located in remote areas with low urban land use, poor living conditions, and low population size, leading to decreased walking activities (Shuvo et al. 2021; Wang et al. 2021). The impact of relief and NDVI on leisure-time physical activities suggests that urban planners should consider how to promote slow activities through planning in communities with specific natural and topographical features; 6. Bus line density positively impacts walking and cycling, and bus stop density positively impacts walking. This indicates that the community’s transportation system is relatively complete, providing residents with good travel tools. Therefore, residents will choose to combine walking and public transportation instead of using motor vehicles, which increases the frequency of walking activities, which is also in line with previous studies (Pucher and Buehler, 2008; Tight et al. 2011; Zhou et al. 2020); 7. Access to catering facilities and workplace accessibility have a significant positive impact only on cycling activities, which indicates that the greater the distance between residents’ homes and catering facilities and the workplace is, the greater the likelihood that residents will choose cycling as a means of transportation to reach their destination. This may be because catering and work are essential daily affairs for residents, and when the distance is relatively far, cycling is more convenient for residents to reach their destination and complete their necessary tasks within a shorter time. This is consistent with the current fast-paced urban lifestyle and with the important goal of slow living in this study (Forman et al. 2008; Pucher et al. 2010; Cho, 2011; Lawton et al. 2013; Botta, 2016; Boakye et al. 2023; Criss et al. 2023); 8. Access to educational facilities positively impacts walking and jogging, which may be attributed to the fact that within a certain range, the relatively long distance from schools and other educational institutions often prompts parents and students to complete their journeys to school through walking or jogging activities, as moderate exercise is believed to promote the physical and mental health of children in parents’ subconscious minds (Napier et al. 2011; Stevens and Brown, 2011; Janssen and King, 2015; Hunter et al. 2023). The reason it does not impact cycling activities is that schools and other educational institutions are not suitable for motorized or nonmotorized transportation, which is consistent with existing research (Lavoie et al. 2014; Rothman et al. 2016; Rodrigues et al. 2018). The positive impact of accessibility to these public service facilities on leisure-time physical activities highlights the importance of considering daily life needs in urban planning to promote slow activities.
Heatmap of the differential influence of the built environment of the slow community on different types of leisure-time physical activities (Source: author’s drawing).
This study proposes a new evaluation system to address the limitations of the existing evaluation framework in assessing the built environment of slow communities. The uniqueness of this new evaluation system lies in its comprehensiveness, objectivity, and innovative application of methodology. This study’s new evaluation system is based on an in-depth study of 5D environmental elements, analysing the relationship between residents’ leisure-time physical activities (such as walking, running, and cycling) and environmental factors, thus preliminarily constructing a comprehensive and objective set of evaluation indicators. These indicators cover not only the diversity of environmental elements but also their impact on residents’ health activities. Second, this study’s new evaluation system employs a combination of the analytic hierarchy process (AHP) and entropy weight method. This dual weighting mechanism, combining subjective and objective perspectives, not only fully considers expert opinions but also significantly enhances the objectivity and scientific nature of the evaluation system. Finally, this study’s new evaluation system accurately analyses the relationships between different types of leisure-time physical activities and various environmental factors, enabling a more accurate assessment of the key factors affecting the built environment of slow communities. This method not only enhances the stability and accuracy of the assessment results but also improves its applicability, making it more suitable for different types of community environments. In summary, the new evaluation system proposed in this study surpasses the existing evaluation framework in terms of methodology, objectivity, and comprehensiveness, offering a more scientific and effective path for assessing the built environment of slow communities. Furthermore, this system primarily demonstrates how to promote a lifestyle under the slow living concept through the assessment and optimization of the built environment. For instance, the system can identify and improve those environmental characteristics that encourage residents to walk, cycle, and engage in other slow activities, directly addressing the emphasis on healthy lifestyles in the theory of slow living. This includes providing safe and convenient walking and cycling paths, as well as ample public spaces for residents to engage in physical activities and relaxation. Second, the community is a core component of the slow-living concept. The assessment system encourages the inclusion of more public gathering places (such as parks, community gardens, and leisure squares) in community design to foster social interaction among residents, strengthening a sense of community belonging and social support networks. Finally, the assessment system encourages the adoption of a slower, more mindful attitude in daily life through the design and planning of the built environment. This can be achieved by offering serene resting spaces, promoting closeness to nature, and encouraging residents to slow their pace of life through community design.
Based on the new evaluation system, this study calculated the comprehensive score of the built environment of slow communities in Shanghai and conducted spatial visualization, and a clear “pyramid” spatial stratification phenomenon emerged. The relatively high-value communities are mainly distributed in the core areas of Shanghai’s central urban area’s comprehensive functions, mainly consisting of affluent communities with a good liveable environment and a complete system of public service facilities. Therefore, residents have a stronger sense of happiness (Brereton et al. 2011; Liu et al. 2022; Mouratidis, 2022; Dai et al. 2023), their enthusiasm for leisure-time physical activities is relatively high, and they participate more frequently (Nguyen et al. 2016; Pfeiffer and Cloutier, 2016; Felez-Nobrega et al. 2021; Vaquero-Solis et al. 2021). The slow communities in the northern, eastern, western, and southern parts of the old city have relatively weak quality built environments, and the comprehensive functions and location levels of the activity environment in these areas are not well developed, which leads to insufficient energy for residents’ leisure-time physical activities. Therefore, the study revealed that differences in the level of economic development largely shape residents’ health behaviours and lifestyles, as well as the activity environment and dynamics within communities. Especially in communities with higher levels of economic development, due to superior living conditions and comprehensive public service facilities, residents are more inclined to participate in leisure-time physical activities such as walking, jogging, and cycling. Conversely, in economically weaker communities, due to a lack of necessary infrastructure and services, residents have limited opportunities to engage in these activities. This spatially uneven development not only highlights the challenges in urban planning and community construction but also leads to health inequalities between communities, thereby affecting the quality of life and cohesion of residents. Therefore, to promote healthy lifestyles among all community residents, it is necessary to reduce differences between communities, improve public facilities in low-income communities, increase green spaces and recreational areas, and provide more resources and opportunities for healthy living, thus providing a basis for more equitable and effective urban planning policies.
To break the one-way overdraft inertia of the “centre-periphery” spatial layout of Shanghai’s slow communities, it is necessary to promote the orderly distribution of urban residents’ leisure-time physical activities and environmental system elements and to ensure their autonomy, balance, and inclusiveness. This should be done based on mutual complementarity and common improvement rather than simply relying on static spatial level differences in scale and functional positioning.
At the same time, based on the spatial distribution results of each level system, it is necessary to first accelerate the orderly evacuation and replacement of public service resources and population that are overly concentrated in the central area of the old city and to promote the comprehensive development, supporting facilities, and population agglomeration of the new central area. This will enhance the concentration of modern and traditional lifestyles and promote the convenience, low-carbon health, humanistic ecology, and all-age friendliness of leisure-time physical activities. Second, it is necessary to vigorously promote the construction of slow transportation networks, drive the development and utilization of diverse mixed-use land, establish multichannel connections between key communities for leisure-time physical activities and environmental elements, and form a well-balanced grouping of daily leisure-time physical activity spaces within communities. Additionally, it is important to accelerate the organic renewal of old city communities and the orderly development of new cities, enhance the coordinated complementarity between communities, reduce the gradient level between communities, and optimize the two-way interactive relationships between communities, thereby enhancing the efficiency, effectiveness, and quality of daily leisure-time physical activities.
This study has some limitations that need further improvement. First, the source of the exercise data is single, and the distribution of the groups using this app may be inconsistent with the actual distribution. Second, the selection of indicators is based on previous studies, which may overlook some impactful indicators. Third, this study revealed significant differences in the amount of time residents spent performing different types of leisure-time physical activities. However, the underlying mechanism is not clear and requires further exploration. Fourth, environmental economic indicators were not used in this study to reflect the socioeconomic status of the slow communities, and relevant factors were not considered. These factors can be included to reflect the variability in socioeconomic status in future work. Fifth, future research could use the parsimonious spherical AHP method, which offers potential advantages and enhances the robustness of the research framework over traditional AHP methods. Additionally, given that leisure-time physical activities may be influenced by specific demographic factors, it is necessary to define specific factors (such as gender and age) as control variables. Furthermore, this study mainly used natural experimental data and lacked population census data, economic census data, and questionnaire survey data.
This study used a multivariate linear regression model, the analytic hierarchy process (AHP), and the entropy method to explore the sustainable development mechanisms of slow communities and construct an evaluation system. The research results indicate the following:
The importance of gender differences and temporal variation: Women’s participation in leisure-time physical activities such as walking, jogging, and cycling is greater than men’s, and there are significant differences in the amount of activity at different times. This highlights the need to consider gender and time in community planning and design, providing a new perspective for related fields.
The diversity of environmental needs: This study reveals the different needs of various leisure-time physical activities for community construction environmental elements, which is instructive for the design and optimization of slow communities, emphasising the importance of demand differences in community construction.
Spatial stratification phenomenon: Through a case study of slow communities in Shanghai, a clear “pyramid” spatial stratification phenomenon was found, providing a new perspective for understanding urban community structure and having important reference value for future community planning and design.
This research proposes a new evaluation system design method that combines subjective and objective perspectives, which not only provides theoretical support for the optimisation of slow communities but also enriches the theoretical model of slow communities in China and other developing countries. In addition, a leisure-time physical activity optimisation model is proposed, providing new materials and a basis for the theoretical development of disciplines such as urban planning, sports science, healthy geography, and design. Based on the research results, the following policy suggestions are proposed: 1. Urban planners should pay special attention to the needs of female users, such as adding lighting to enhance the safety of walking and cycling at night, setting up rest and social areas, and encouraging women to participate actively. 2. Urban planning should include activity diversity, setting up walking and cycling paths of different difficulties, providing diversified green spaces and rest areas, and meeting the needs of different users. 3. The government and planning agencies should take measures to reduce inequality between communities, such as increasing public spaces and leisure facilities in low-income areas, optimising traffic connections, and improving the quality of community life. 4. Government and community organisations should carry out publicity and education activities to promote slow activities, such as regular community walking and cycling activities, and advocate a slow lifestyle in schools and workplaces.
Finally, future research should explore the impact of different community environmental factors on slow activities, as well as the roles of gender, age, socioeconomic status, and other factors in slow activities, and discuss how policy interventions can effectively promote the development of slow communities and the active participation of residents in healthy activities. Additionally, considering the focus on sustainability, future studies could explore the application of the parsimonious spherical AHP. This method, known for its ability to handle complex decision-making scenarios with fewer pairwise comparisons, could offer a more streamlined approach to evaluating sustainable development mechanisms in slow communities.
This trajectory data does not contain personal information and has obtained personal informed consent. The data that support the findings of this study are available from the Dorray Sports app. Restrictions apply to the availability of these data, which were used under license for this study. The availability of the data is subject to additional restrictions, which may require specific permissions. Please note that the data usage rights are provided by Dorray Sports. Therefore, this study is unable to share this data.
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This article would like to acknowledge funding support from the Fundamental Research Funds for the Central Universities of Central South University (No. 2024ZZTS340).
This research was funded by the Fundamental Research Funds for the Central Universities of Central South University (No. 2024ZZTS340).
School of Architecture and Art, Central South University, Changsha, China
Qikang Zhong, Bo Li & Tian Dong
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Conceptualisation: BL and QZ; methodology: QZ and BL; formal analysis: QZ and BL; resources: QZ, and BL; writing—original draft preparation: QZ, BL, and TD; writing—review and editing: QZ, BL, and TD; visualisation: QZ, BL, and TD; supervision: BL; funding acquisition: BL All authors have read and approved the final manuscript.
Correspondence to Bo Li.
Ethical approval was not required as the study did not involve human participants.
Ethical approval was not required as the study did not involve human participants.
The authors declare no competing interests.
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Zhong, Q., Li, B. & Dong, T. Building sustainable slow communities: the impact of built environments on leisure-time physical activities in Shanghai. Humanit Soc Sci Commun 11, 828 (2024). https://doi.org/10.1057/s41599-024-03303-y
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DOI: https://doi.org/10.1057/s41599-024-03303-y
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