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Topic Editors

School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China
School of Architecture and Urban Planning, Chongqing University, Chongqing 400045, China
Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Department of Environmental Science and Engineering, Fudan University, Songhu Road 2005, Shanghai 200438, China
School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, China
College of Architecture and Planning, Fujian University of Technology, Fuzhou 350118, China

Biophilic Cities and Communities: Human-Environment Interaction and Sustainable Governance

Abstract submission deadline
31 May 2025
Manuscript submission deadline
31 July 2025
Viewed by
9664

Topic Information

Dear Colleagues,

Biophilic design is a design philosophy that encourages the use of natural and sustainable systems to enhance the built environment on multiple scales (e.g., building, site, city and regional) (Gillis and Gatersleben, 2015). Currently, and with increasing frequency, a host of biophilic programs, policies and initiatives are being developed in many cities, facilitating natural resource conservation and environmental and social sustainability in the built environment. The increasing trend demonstrates that biophilic perspectives on cities and communities have profound connotations worthy of further exploration. Nevertheless, enhancing the built environment to create biophilic cities and communities is still challenging. From April 2023 to April 2024 we conducted work on the topic "Biophilic Cities and Communities: Towards Natural Resources, Environmental and Social Sustainability" (https://www.mdpi.com/topics/KX5WREG227). We are very grateful to have received over 100 submissions on this subject. We consider the biophilic cities and communities to be worthy of further attention and discussion. Furthermore, the intersection of ecology and exposure science with health concerns has led to a gradual infiltration of the topic. Thus, we propose the topic “Biophilic Cities and Communities: Human−Environment Interaction and Sustainable Governance ”. We expect this to bring together researchers who are working on related topics and encourage them to share their latest accomplishments and research findings.

We welcome submissions of original research articles, reports or technical notes, reviews, and mini-reviews covering topics, including, but not limited, to the following:

(i) Biophilic urbanism and processes Smart cities and communities; Land use/cover change; Spatial-temporal trends; Geodesign; Urban landscape pattern; Sustainable urban-rural planning; Built environment assessment.
(ii) Human−Environment Interaction Environmental behavior and local practice; Social and historical sensing; Economic and cultural sustainability; Big data and social computing; Social Equity; Sustainable governance.
(iii) Exposure Ecology Urban/ natural ecosystem; Ecological pattern and process; Ecological exposure; Forest management; Nature-based solutions; Public health and wellbeing.

Gillis, K., Gatersleben, B. A review of psychological literature on the health and wellbeing benefits of biophilic design, Buildings, 2015, 5(3):948–963.

Dr. Xin-Chen Hong
Prof. Dr. BaoJie He
Dr. Guangyu Wang
Dr. Zhaowu Yu
Prof. Dr. Jiang Liu
Dr. Xiong Yao
Topic Editors

Keywords

  • sustainable governance
  • remote sensing
  • public health
  • social sensing
  • urban ecosystem
  • natural ecosystem
  • environment assessment
  • land-use policy
  • big data
  • sustainability

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Buildings
buildings
3.1 3.4 2011 17.2 Days CHF 2600 Submit
Forests
forests
2.4 4.4 2010 16.9 Days CHF 2600 Submit
Land
land
3.2 4.9 2012 17.8 Days CHF 2600 Submit
Remote Sensing
remotesensing
4.2 8.3 2009 24.7 Days CHF 2700 Submit
Behavioral Sciences
behavsci
2.5 2.6 2011 27 Days CHF 2200 Submit
Sustainability
sustainability
3.3 6.8 2009 20 Days CHF 2400 Submit

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Published Papers (10 papers)

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27 pages, 3910 KiB  
Article
Exploring the Application of Neurostructural Principles to the Design of Public Spaces on University Campuses
by Qihang Zhou and Xingxing Fang
Land 2024, 13(12), 1978; https://doi.org/10.3390/land13121978 - 21 Nov 2024
Viewed by 165
Abstract
In this study, we examined the application of neurostructural principles to the design of public spaces on university campuses to optimize students’ learning efficiency, social interactions, and psychological well-being. Using Hainan University in China as a case study, a descriptive analysis was used [...] Read more.
In this study, we examined the application of neurostructural principles to the design of public spaces on university campuses to optimize students’ learning efficiency, social interactions, and psychological well-being. Using Hainan University in China as a case study, a descriptive analysis was used to evaluate the case study design of the data. Data on students’ preferences for and satisfaction with public learning spaces (libraries, student centers, and open learning areas) were also collected through a questionnaire. The questionnaire was based on the four stages of the AIDA (Attention, Interest, Desire, and Action) model and covered basic information about the participants and their first impressions of the learning spaces, design element preferences, emotional and cognitive influences, and willingness to participate in improving the design of campus spaces. Data were analyzed using quantitative methods, including frequency analysis and score aggregation, to assess the students’ satisfaction with the existing design elements of the learning space and their suggestions for potential improvements. A random sample of students enrolled at Hainan University was used to ensure that the data were representative. The results of the study indicate that the rational allocation of natural light, the optimization of the acoustic environment, the adoption of soothing color schemes, and flexible spatial layouts are effective at relieving students’ psychological stress, enhancing their academic performance, and facilitating social interactions. Some of the existing designs are already in line with neurostructural principles, but there is still room for improvement, especially in terms of color schemes and spatial configurations. Students have positive attitudes towards participating in campus space improvement, with especially high interest in light optimization, spatial layout, and the use of natural materials. This study verifies the effectiveness of using neural structure principles in campus public spaces by establishing an empirical model, proves its positive effect on the quality of the campus environment and students’ well-being, and provides empirical evidence and theoretical support for future campus design. Full article
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<p>Neuroarchitecture conceptual relationship diagram (source: the author of this study).</p>
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<p>Research hypothesis model (source: the author of this study).</p>
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<p>Diagram of AIDA model (source: the Internet).</p>
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<p>Library and classrooms at Hainan University’s Haidian Campus (source: Google Maps).</p>
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<p>Emotional impact diagram of users at the Desire stage (Q14–Q16).</p>
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<p>Q17 data chart.</p>
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<p>Q18: The application of natural elements in campus public learning spaces and their impact on personal emotions.</p>
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<p>Q19: Proportions of user choices regarding neuro-architectural elements.</p>
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<p>Q20: Would you like to participate in the design and feedback process regarding common learning spaces on campus to help improve them?</p>
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<p>Q21–Q22 results.</p>
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11 pages, 1862 KiB  
Article
Association Between Neighborhood Built Environment and Mental Health: Differences Between Older Adults With and Without Restricted Mobility
by Xinyu Kong, Haoying Han, Fangting Chi, Mengyao Zhan and Xianfan Shu
Sustainability 2024, 16(21), 9226; https://doi.org/10.3390/su16219226 - 24 Oct 2024
Viewed by 667
Abstract
The mobility restrictions faced by older adults pose significant challenges to understanding the association between the neighborhood built environment and their mental health. Neglecting the role of restricted mobility hinders a comprehensive analysis of how the built environment impacts older adults’ mental health. [...] Read more.
The mobility restrictions faced by older adults pose significant challenges to understanding the association between the neighborhood built environment and their mental health. Neglecting the role of restricted mobility hinders a comprehensive analysis of how the built environment impacts older adults’ mental health. Furthermore, the differences in this association between older adults with and without restricted mobility remain unclear. Based on data from 1405 adults aged 60 and older in Hangzhou, China, this study explored the association between the neighborhood built environment and the mental health of older adults using multivariable linear regression, with multivariable logistic regression being employed for the sensitivity analysis. The results indicated that access to public canteens and outdoor fitness spaces were significantly positively associated with the mental health of older adults. Notably, the protective effect of outdoor fitness spaces was significant for older adults with restricted mobility, while the supportive effect of public canteens was significant for those without restricted mobility. This study demonstrated an association between the neighborhood built environment and mental health among older adults, highlighting differences in this effect between those with and without restricted mobility. These insights underscore the necessity of designing sustainable and inclusive neighborhoods that cater to the varied needs of older adults, ultimately fostering environments that promote healthy and active aging. Full article
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<p>Location map of sampled neighborhoods.</p>
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<p>Multivariable linear regression models of the mental health of older adults.</p>
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<p>Multivariable logistic regression models of the mental health of older adults.</p>
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20 pages, 15601 KiB  
Article
Integrating Streetscape Images, Machine Learning, and Space Syntax to Enhance Walkability: A Case Study of Seongbuk District, Seoul
by Zhongshan Huang, Bin Wang, Shixian Luo, Manqi Wang, Jingjing Miao and Qiyue Jia
Land 2024, 13(10), 1591; https://doi.org/10.3390/land13101591 - 30 Sep 2024
Viewed by 831
Abstract
As urbanization rapidly progresses, streets have transitioned from mere transportation corridors to crucial spaces for daily life and social interaction. While past research has examined the impact of physical street characteristics on walkability, there is still a lack of large-scale quantitative assessments. This [...] Read more.
As urbanization rapidly progresses, streets have transitioned from mere transportation corridors to crucial spaces for daily life and social interaction. While past research has examined the impact of physical street characteristics on walkability, there is still a lack of large-scale quantitative assessments. This study systematically evaluates street walkability in Seongbuk District, Seoul, through the integration of streetscape images, machine learning, and space syntax. The physical characteristics of streets were extracted and analyzed in conjunction with space syntax to assess street accessibility, leading to a combined analysis of walkability and accessibility. The results reveal that the central and western regions of Seongbuk District outperform the eastern regions in overall street performance. Additionally, the study identifies four distinct street types based on their spatial distribution: high accessibility–high overall score, high accessibility–low overall score, low accessibility–high overall score, and low accessibility–low overall score. The findings not only provide a scientific basis for street development in Seongbuk District but also offer valuable insights for assessing and enhancing walkability in cities globally. Full article
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<p>Research framework.</p>
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<p>Study area.</p>
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<p>Google Street View image collection. The dots in the figure indicate the locations of the Street View images.</p>
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<p>Spatial distribution of the eight indicators.</p>
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<p>Comprehensive quality distribution map of the street.</p>
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<p>Comprehensive quality heat map.</p>
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<p>Street accessibility distribution in the study area (R1000).</p>
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<p>Coupling analysis of street accessibility and walkability evaluation. (<b>a</b>) High accessibility–high overall score. (<b>b</b>) High accessibility–low overall score. (<b>c</b>) Low accessibility–high overall score. (<b>d</b>) Low accessibility–low overall score.</p>
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<p>Representative Street View imagery for four coupling types.</p>
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17 pages, 7426 KiB  
Article
Differential Evaluation of Ecological Resilience in 45 Cities along the Yangtze River in China: A New Multidimensional Analysis Framework
by Chong Li, Yibao Wang, Wen Qing, Cuixi Li and Yujiang Yang
Land 2024, 13(10), 1588; https://doi.org/10.3390/land13101588 - 29 Sep 2024
Viewed by 773
Abstract
The rapid pace of urbanization and global climate change necessitates a thorough assessment of urban ecological resilience to cultivate sustainable regional ecosystem development. Cities along the Yangtze River face an intensifying conflict between ecological preservation and socio-economic growth. Analyzing the ecological resilience of [...] Read more.
The rapid pace of urbanization and global climate change necessitates a thorough assessment of urban ecological resilience to cultivate sustainable regional ecosystem development. Cities along the Yangtze River face an intensifying conflict between ecological preservation and socio-economic growth. Analyzing the ecological resilience of these urban centers is essential for achieving equilibrium in regional urban ecosystems. This study proposes a “system process space” attribute analysis framework, taking into account urban development processes, ecosystem structure, and resilience evolution stages. Utilizing data from 45 Yangtze River cities, we establish a “Driver, Pressure, State, Impact, and Response” (DPSIR) evaluation index system to evaluate changes in ecological resilience levels and evolution trends from 2011 to 2022. Our findings indicate that: (1) The ecological resilience index of Yangtze River cities increased from 0.177 to 0.307 between 2011 and 2022, progressing through three phases: ecological resilience construction, rapid development, and stable development. (2) At the city level, ecological resilience along the Yangtze River exhibits uneven development characteristics. Upstream cities display a significant “stepped” pattern, midstream cities exhibit a significant “Matthew effect”, and downstream cities present a pyramid-shaped pattern. While regional differences in ecological resilience persist, overall polarization is gradually decreasing, intercity connections are strengthening, and there is a growing focus on coordinated regional development. (3) The spatial distribution of ecological resilience in Yangtze River cities demonstrates both continuity and evolution, generally forming a “core-edge” clustered pattern. Based on these findings, we recommend enhancing inter-city cooperation and connectivity, addressing imbalances in urban ecological resilience, and promoting high-quality ecological resilience development along the Yangtze River through tailored development strategies for each city. Full article
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<p>Theoretical framework for UER.</p>
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<p>Overview of the study area. (<b>a</b>) Geographic location of the Yangtze River Basin in China. (<b>b</b>) Location of the study area. (<b>c</b>) Upstream cities in the research area. (<b>d</b>) Midstream cities in the research area. (<b>e</b>) Downstream cities in the research area.</p>
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<p>UERI of cities along the Yangtze River (<b>a</b>), Upstream cities (<b>b</b>), Midstream cities (<b>c</b>), Downstream cities (<b>d</b>).</p>
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<p>Spatial distribution of UER along the Yangtze River (<b>a</b>), 2011–2014 average (<b>b</b>), 2015–2018 average (<b>c</b>), 2019–2022 average (<b>d</b>).</p>
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<p>UER core density curve (<b>a</b>), upstream cities (<b>b</b>), midstream cities (<b>c</b>) and downstream cities (<b>d</b>) along the Yangtze River.</p>
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27 pages, 15562 KiB  
Article
The Combined Effects of the Thermal Environment and Air Quality at Recreation Places on the Physiology and Psychology of People in Urban Parks
by Yuxiang Lan, Jingjing Wang, Yaling Huang, Yuanyang Tang, Zhanhua Liu, Jiahui Zheng, Xiong Yao, Zhipeng Zhu, Jianwen Dong and Ye Chen
Forests 2024, 15(9), 1640; https://doi.org/10.3390/f15091640 - 17 Sep 2024
Viewed by 1167
Abstract
Urban forests, crucial to urban ecosystems, are increasingly threatened by the challenges of urbanization, such as deteriorating thermal environments and declining air quality. Despite their recognized benefits to city dwellers’ quality of life, a systematic understanding of the impact of these environmental factors [...] Read more.
Urban forests, crucial to urban ecosystems, are increasingly threatened by the challenges of urbanization, such as deteriorating thermal environments and declining air quality. Despite their recognized benefits to city dwellers’ quality of life, a systematic understanding of the impact of these environmental factors on public psychophysiological well-being in recreational sites is a notable gap in the literature. The objective of this research was to bridge this gap by examining the effects of the thermal environment and air quality in urban forests on the public’s perception, offering scientific evidence to inform environmental optimization and health management strategies for urban parks, essential for sustainable urban development and public health. Three urban parks in Fuzhou, Fujian Province, namely Fuzhou National Forest Park, Xihu Park, and Jinniushan Sports Park, were selected as research sites. Environmental monitoring and questionnaire surveys were conducted at 24 recreation places from October to December 2020, collecting temperature, humidity, and wind speed; the atmospheric composition includes PM2.5, PM10, negative oxygen ion, and psychophysiological data from the public. Multivariate statistical methods were employed to assess the environmental characteristics of different recreation places types and their impact on public health. The findings reveal that environmental factors explained 1.9% to 11.8% of the variation in physiological and psychological responses, mainly influenced by temperature, wind speed, and negative oxygen ions. Forests and waterfront recreation places significantly outperform canopy and open recreation places in promoting mental invigoration, stress relief, emotional tranquility, and attention restoration. Environmental monitoring results indicate that favorable meteorological conditions and good air quality are crucial for enhancing the service functions of recreation places. Notably, the positive correlation between a negative air ion concentration and psychological well-being provides a novel perspective on understanding the health benefits of urban forests. The thermal environment and air quality of urban recreation places exert a significant influence on the psychophysiological status of the public. Increasing green coverage, improving water body environments, and rationally planning recreation places layout are of great theoretical and practical significance for enhancing the environmental quality and service functions of urban forests. Full article
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<p>(<b>a</b>) Location of Fujian Province in China map; (<b>b</b>) Location of Fuzhou City in Fujian map; (<b>c</b>) Study site selection of urban forests in Fuzhou.</p>
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<p>(<b>a</b>) Study site selection of Jinniushan Sports Park. (<b>b</b>) Study site selection of Fuzhou National Forest Park. (<b>c</b>) Study site selection of Xihu Park.</p>
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<p>(<b>a</b>) Plots of Jinniushan Sports Park. (<b>b</b>) Plots of Fuzhou National Forest Park. (<b>c</b>) Plots of Xihu Park.</p>
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<p>Procedure.</p>
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<p>Variance of HR in different types of recreation places. Different lowercase letters (a,b) represent significant differences in HR variation between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Variance of HR in different types of recreation places in parks. Different lowercase letters (a–c) represent significant differences in HR variation between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Variance of LF/HF in different types of recreation places. Different lowercase letters (a,b) represent significant differences in LF/HF variation between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Variance of LF/HF in different types of recreation places in parks. Different lowercase letters (a–c) represent significant differences in LF/HF variation between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Variance of EDA in different types of recreation places. Different lowercase letters (a,b) represent significant differences in EDA variation between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Variance of EDA in different types of recreation places in parks. Different lowercase letters (a–c) represent significant differences in EDA variation between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Spiritual vitality of different recreation places. Different lowercase letters (a,b) represent significant differences in spiritual vitality between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Spiritual vitality of different recreation places in parks. Different lowercase letters (a–c) represent significant differences in spiritual vitality between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Stress relief in different recreation places. Different lowercase letters (a,b) represent significant differences in stress relief between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Stress relief in different recreation places in parks. Different lowercase letters (a–c) represent significant differences in stress relief between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Emotional arousal in different recreation places. Different lowercase letters (a,b) represent significant differences in emotional arousal between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Emotional arousal in different recreation places in parks. Different lowercase letters (a–c) represent significant differences in emotional arousal between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Attention recovery in different recreation places. Different lowercase letters (a,b) represent significant differences in attention recovery between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Attention recovery in different recreation places in parks. Different lowercase letters (a–c) represent significant differences in attention recovery between different recreation places (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>) The current situation survey of Jinniushan Sports Park. (<b>b</b>) The current situation survey of Fuzhou National Forest Park. (<b>c</b>) The current situation survey of Xihu Park.</p>
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<p>(<b>a</b>) The current situation survey of Jinniushan Sports Park. (<b>b</b>) The current situation survey of Fuzhou National Forest Park. (<b>c</b>) The current situation survey of Xihu Park.</p>
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23 pages, 10654 KiB  
Article
A Study on the Relationship between Campus Environment and College Students’ Emotional Perception: A Case Study of Yuelu Mountain National University Science and Technology City
by Zhimou Peng, Ruiying Zhang, Yi Dong and Zhihao Liang
Buildings 2024, 14(9), 2849; https://doi.org/10.3390/buildings14092849 - 10 Sep 2024
Viewed by 1374
Abstract
The campus environment directly impacts college students’ psychological and emotional well-being, influencing their behavioral performance and the development of their personalities. Investigating the complex relationship between the campus spatial environment and students’ emotions is crucial for designing urban environments that support mental health. [...] Read more.
The campus environment directly impacts college students’ psychological and emotional well-being, influencing their behavioral performance and the development of their personalities. Investigating the complex relationship between the campus spatial environment and students’ emotions is crucial for designing urban environments that support mental health. Using Yuelu Mountain National University Science and Technology City as a case study, this research developed a framework to analyze campus environment characteristics and emotional perception. The study quantitatively assessed emotional perceptions, examined the specific contributions of different campus environment elements to individual emotions, and created an emotion prediction map to explore these relationships in depth. The results indicate that “campus greenery” and “diversity” negatively affect “disappointment” and “depression”, while “sky views” positively impact “happiness” and “sense of security”. Additionally, “diversity” positively affects “relaxation”, and “campus greenery” and “diversity” have negative effects on “disappointment” and “depression”, with “diversity” having a particularly strong positive effect on “relaxation”. The pronounced spatial clustering of emotional perceptions on campus further underscores the significant influence of the campus environment on individual emotional experiences. As the first study to explore the mechanisms underlying the emotional perceptions of Chinese college students in relation to the campus environment, this research overcomes the limitations of traditional environmental assessment indicators by identifying campus environmental elements and psychological factors that better align with the psychological needs of college students. This provides a scientific basis for optimizing campus environments based on the emotional perceptions of students, thereby supporting mental health promotion and guiding campus environment construction. Moreover, the research methodology is broadly applicable. The integration of campus environment image data and deep learning offers a significant tool for assessing campus space and environmental perception, thereby enhancing human-centered environmental assessment and prediction while more accurately reflecting architectural space perception. Full article
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<p>Research framework.</p>
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<p>Campus environment image collection area of YLMNUSTTC, Changsha.</p>
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<p>Flowchart for building the emotional platform.</p>
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<p>DeepLab v3+ model.</p>
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<p>Extracted presentation of six types of spatial elements.</p>
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<p>Map of mood prediction on university campuses.</p>
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<p>Local autocorrelation cluster analysis.</p>
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<p>Result of Getis-Ord Gi* statistic that shows the hot and cold spot for each mood indicator.</p>
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<p>Characteristic importance of perceived emotional impact of environmental elements on university campuses.</p>
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<p>Randomly selected images for result validation.</p>
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<p>Campus environment and mood score results.</p>
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30 pages, 9180 KiB  
Article
The Impact of Street Elements on Pedestrian Stopping Behavior in Commercial Pedestrian Streets from the Perspective of Commercial Vitality
by Xuanming Mu, Liqiang Mu and Jun Zhang
Sustainability 2024, 16(17), 7727; https://doi.org/10.3390/su16177727 - 5 Sep 2024
Cited by 1 | Viewed by 977
Abstract
As urban design increasingly emphasizes livable environments, research on pedestrians and walking environments has been revisited at the street level. Although existing studies have shown that street environments impact pedestrians, there remains a significant gap in our knowledge regarding which street elements affect [...] Read more.
As urban design increasingly emphasizes livable environments, research on pedestrians and walking environments has been revisited at the street level. Although existing studies have shown that street environments impact pedestrians, there remains a significant gap in our knowledge regarding which street elements affect pedestrian walking behavior, to what degree, and which walking characteristics are influenced. This study aims to validate the close relationship between street elements and pedestrian stopping behavior by measuring the influence of different street element environments on walking characteristics. Research methods include a literature review and field research, categorizing street elements into 32 types and pedestrian stopping behaviors into 10 characteristics. By collecting effective walking data from 1587 pedestrians and conducting data processing and regression analysis, we found that rational street design can effectively promote commercial activity and enhance street vitality. Based on the experimental conclusions, we propose urban design recommendations to further enhance the vitality of commercial pedestrian streets, including optimizing street landscape design, improving pedestrian facilities, and increasing leisure spaces. This research provides valuable references for further exploring how to enhance the vitality of commercial pedestrian streets, helping urban planners and designers better understand the relationship between street elements and urban vitality, thereby creating more attractive and vibrant urban spaces and promoting sustainable urban development. Full article
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<p>Map of the experimental location. (<b>A</b>) Heilongjiang Province, China; (<b>B</b>) Harbin City, Heilongjiang Province; (<b>C</b>) Central Street with the study sites.</p>
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<p>Classification of Street Elements: (<b>A</b>) Distribution of Street Elements; (<b>B</b>) Public Street Elements; (<b>C</b>) Building Interface Elements; (<b>D</b>) Spatial Perception Elements.</p>
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<p>Classification of Pedestrian Stationary Behavior Participation.</p>
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<p>Selection of Measurement Points. The red shaded areas represent construction zones or entrances/exits of large shopping malls, where pedestrian data collection will not be conducted. Road intersections are delineated during the street segment division process and will not be included in pedestrian data collection. The numerical order in the figure corresponds to the segment numbers in the following text.</p>
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<p>Statistics of Pedestrian Walking and Stationary Characteristics.</p>
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<p>Statistics of Participation Tendency Characteristics.</p>
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<p>Linear Regression Analysis of Main Factors Affecting Pedestrian Walking and Stationary Behavior.</p>
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22 pages, 11240 KiB  
Article
Research on Landscape Perception of Urban Parks Based on User-Generated Data
by Wei Ren, Kaiyuan Zhan, Zhu Chen and Xin-Chen Hong
Buildings 2024, 14(9), 2776; https://doi.org/10.3390/buildings14092776 - 4 Sep 2024
Viewed by 851
Abstract
User-generated data can reflect various viewpoints and experiences derived from people’s perception outcomes. The perceptual results can be obtained, often by combining subjective public perceptions of the landscape with physiological monitoring data. Accessing people’s perceptions of the landscape through text is a common [...] Read more.
User-generated data can reflect various viewpoints and experiences derived from people’s perception outcomes. The perceptual results can be obtained, often by combining subjective public perceptions of the landscape with physiological monitoring data. Accessing people’s perceptions of the landscape through text is a common method. It is hard to fully render nuances, emotions, and complexities depending only on text by superficial emotional tendencies alone. Numerical representations may lead to misleading conclusions and undermine public participation. In addition, the use of physiological test data does not reflect the subjective reasons for the comments made. Therefore, it is essential to deeply parse the text and distinguish between segments with different semantic differences. In this study, we propose a perceptual psychology-based workflow to extract and visualize multifaceted views from user-generated data. The analysis methods of FCN, LDA, and LSTM were incorporated into the workflow. Six areas in Fuzhou City, China, with 12 city parks, were selected as the study object. Firstly, 9987 review data and 1747 pictures with corresponding visitor trajectories were crawled separately on the Dianping and Liangbulu websites. For in-depth analysis of comment texts and making relevant heat maps. Secondly, the process of clauses was added to get a more accurate representation of the sentiment of things based on the LSTM sentiment analysis model. Thirdly, various factors affecting the perception of landscapes were explored. Based on such, the overall people’s perception of urban parks in Fuzhou was finally obtained. The study results show that (1) the texts in terms of ‘wind’, ‘temperature’, ‘structures’, ‘edge space (spatial boundaries)’, and ‘passed space’ are the five most representative factors of the urban parks in Fuzhou; (2) the textual analyses further confirmed the influence of spatial factors on perception in the temporal dimension; and (3) environmental factors influence people’s sense of urban parks concerning specificity, clocking behavior, and comfort feelings. These research results provide indispensable references for optimizing and transforming urban environments using user-generated data. Full article
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<p>Research steps.</p>
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<p>Study area map. (<b>a</b>) Distribution map of the six study areas; (<b>b</b>) Geographic location of the study areas on the map of China. The red square frame indicates the location of the study areas in Fujian Province in the picture.</p>
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<p>Trends of LDA theme over the years.</p>
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<p>Tree map of subject term categories (top 200 frequency).</p>
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<p>Percentage of image semantic segmentation elements.</p>
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<p>LSTM emotional tendency scatter plot.</p>
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<p>(<b>a</b>) Density map of photo location points in the Forest Park area; (<b>b</b>) density map of visitor track in the Forest Park area; (<b>c</b>) emotional distribution map in the Forest Park area; (<b>d</b>) density map of photo location points in the Fuway area; (<b>e</b>) density map of visitor track in the Fuway area; (<b>f</b>) emotional distribution map in the Fuway area. The picture is an excerpt, and other pictures are detailed in <a href="#buildings-14-02776-f0A1" class="html-fig">Figure A1</a> of the <a href="#app1-buildings-14-02776" class="html-app">Appendix A</a>.</p>
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<p>Words cloud maps. (<b>a</b>) Theme of wind; (<b>b</b>) theme of road; (<b>c</b>) theme of wall; (<b>d</b>) theme of gate; (<b>e</b>) theme of ground; (<b>f</b>) theme of sky; (<b>g</b>) theme of tree; (<b>h</b>) theme of child.</p>
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<p>Scatterplot of emotional tendencies of comments on the theme of ‘child’.</p>
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<p>(<b>a</b>) Density map of photo location points in the West Lake Park area; (<b>b</b>) density map of visitor track in the West Lake Park area; (<b>c</b>) dmotional distribution map in the West Lake Park area; (<b>d</b>) density map of photo location points in the Yantai Hill Park area; (<b>e</b>) density map of visitor track in the Yantai Hill Park area; (<b>f</b>) emotional distribution map in the Yantai Hill Park; (<b>g</b>) density map of photo location points in the Huahai Park area; (<b>h</b>) density map of visitor track in the Huahai Park area; (<b>i</b>) emotional distribution map in the Huahai Park area; (<b>j</b>) density map of photo location points in the Jinji Mountain Park area; (<b>k</b>) density map of visitor track in the Jinji Mountain Park area; (<b>l</b>) emotional distribution map in the Jinji Mountain Park.</p>
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<p>(<b>a</b>) Density map of photo location points in the West Lake Park area; (<b>b</b>) density map of visitor track in the West Lake Park area; (<b>c</b>) dmotional distribution map in the West Lake Park area; (<b>d</b>) density map of photo location points in the Yantai Hill Park area; (<b>e</b>) density map of visitor track in the Yantai Hill Park area; (<b>f</b>) emotional distribution map in the Yantai Hill Park; (<b>g</b>) density map of photo location points in the Huahai Park area; (<b>h</b>) density map of visitor track in the Huahai Park area; (<b>i</b>) emotional distribution map in the Huahai Park area; (<b>j</b>) density map of photo location points in the Jinji Mountain Park area; (<b>k</b>) density map of visitor track in the Jinji Mountain Park area; (<b>l</b>) emotional distribution map in the Jinji Mountain Park.</p>
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23 pages, 10102 KiB  
Article
Heat Mitigation Benefits of Street Tree Species during Transition Seasons in Hot and Humid Areas: A Case Study in Guangzhou
by Senlin Zheng, Caiwei He, Haodong Xu, Jean-Michel Guldmann and Xiao Liu
Forests 2024, 15(8), 1459; https://doi.org/10.3390/f15081459 - 19 Aug 2024
Viewed by 753
Abstract
The potential microclimatic effects of street trees are influenced by their ecological characteristics, planting patterns, and street orientations, especially in subtropical hot and humid areas. To investigate these effects, four typical street tree species in Guangzhou were selected for study during the transition [...] Read more.
The potential microclimatic effects of street trees are influenced by their ecological characteristics, planting patterns, and street orientations, especially in subtropical hot and humid areas. To investigate these effects, four typical street tree species in Guangzhou were selected for study during the transition seasons: Khaya senegalensis, Terminalia neotaliala, Ficus microcarpa, and Mangifera indica. Air temperature (AT), relative humidity (RH), solar radiation (SR), surface temperature (ST), wind speed (WS), and the leaf area index (LAI) were monitored. The cooling effects of these four species and the resulting improvements in human thermal comfort (HTC) were assessed. The influences of tree planting patterns and street orientations on cooling benefits were systematically analyzed. The results indicate that, during transition seasons, the four street trees, on average, can block 96.68% of SR, reduce AT by 1.45 °C and ST by 10.25 °C, increase RH by 5.26%, and lower the physiologically equivalent temperature (PET) by 8.34 °C. Terminalia neotaliala, reducing AT and PET by 1.76 °C and 12.4 °C, respectively, offers the greatest potential for microclimate improvement. Among the four tree species, the variations in ST (ΔST) and PET (ΔPET) were minimal, at only 0.76 °C and 0.25 °C, respectively. The average differences in AT and PET between inter-tree and under-tree environments were 0.06 °C and 0.98 °C, respectively. The AT reduction rate was 1.7 times higher in the double-row planting pattern compared to the single-row planting pattern. Street trees planted in the northwest–southeast (NW-SE) orientation exhibited a 16.96% lower WS reduction than those in other orientations. The northeast–southwest (NE-SW) orientation showed the least potential to enhance human thermal comfort. Compared to NE-SW, the northwest–southeast (NW-SE) orientation achieved twice the rate of AT reduction, while the north–south (N-S) orientation improved it by 1.3 times. This data analysis aids in assessing the impact of green infrastructure on urban climates and demonstrates the year-round microclimatic benefits of street trees. Full article
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Figure 1
<p>Methodological flowchart.</p>
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<p>(<b>a</b>) Meteorological data for a typical year; (<b>b</b>) wind rose diagram in Guangzhou (China Meteorological Data Service Center, 2020).</p>
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<p>Tested street tree species.</p>
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<p>Location and orientation of the experimental test sites.</p>
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<p>Schematic diagram of measuring points.</p>
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<p>Instrument arrangement for experimental testing: (<b>a</b>) Louvered box radiation shield; (<b>b</b>) HOBO thermocouple.</p>
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<p>Daily variations in AT between trees, under trees, and in the open area at a reference height (1.5 m) for four tree species. (<b>a</b>) is <span class="html-italic">Khaya senegalensis</span>, (<b>b</b>) is <span class="html-italic">Terminalia neotaliala</span>, (<b>c</b>) is <span class="html-italic">Mangifera indica</span>, and (<b>d</b>) is <span class="html-italic">Ficus microcarpa</span>.</p>
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<p>Comparison of the air-cooling effect in the shaded area at the reference height (1.5 m) among the four tree species: (<b>a</b>) ΔAT = AT in the open area − AT in the shaded area; (<b>b</b>) reduction rate of AT = ΔAT/AT in the open area × 100%.</p>
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<p>Daily variations in RH between trees, under trees, and in the open area at a reference height (1.5 m) among the four tree species. (<b>a</b>) is <span class="html-italic">Khaya senegalensis</span>, (<b>b</b>) is <span class="html-italic">Terminalia neotaliala</span>, (<b>c</b>) is <span class="html-italic">Mangifera indica</span>, and (<b>d</b>) is <span class="html-italic">Ficus microcarpa</span>.</p>
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<p>Comparison of the rate of increase in RH in the shaded area at reference height (1.5 m) among four tree species: (<b>A</b>) ΔRH = RH in the shaded area − RH in the open area; (<b>B</b>) increase rate of RH = ΔRH/RH in the open area × 100%.</p>
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<p>Daily variations in SR between trees, under trees, and in the open area for the four tree species. (<b>a</b>) is <span class="html-italic">Khaya senegalensis</span>, (<b>b</b>) is <span class="html-italic">Terminalia neotaliala</span>, (<b>c</b>) is <span class="html-italic">Mangifera indica</span>, and (<b>d</b>) is <span class="html-italic">Ficus microcarpa</span>.</p>
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<p>Comparison of the degree of SR modification in the shaded area among the four tree species. (<b>A</b>) ΔSR = SR in the open area − SR in the shaded area; (<b>B</b>) reduction rate of SR = ΔSR/SR in the open area × 100%.</p>
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<p>Differences between under-tree and inter-tree SR for the four tree species. ΔSR = inter-tree SR − under-tree SR.</p>
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<p>Daily variations in ST between trees, under trees, and in the open area for the four tree species. (<b>a</b>) is <span class="html-italic">Khaya senegalensis</span>, (<b>b</b>) is <span class="html-italic">Terminalia neotaliala</span>, (<b>c</b>) is <span class="html-italic">Mangifera indica</span>, and (<b>d</b>) is <span class="html-italic">Ficus microcarpa</span>.</p>
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<p>Comparison of the degree of reduction in ST in the shaded area among the four tree species. (<b>A</b>) ΔST = ST in the open area − ST in the shaded area; (<b>B</b>) Reduction rate of ST = ΔST/ST in the open area × 100%.</p>
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<p>Differences between under-tree and inter-tree ST among the four tree species: (<b>A</b>) ΔST = inter-tree ST − under-tree ST; (<b>B</b>) differential rate of ST = ΔST/under-tree ST × 100%.</p>
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<p>Daily variation in WS in open and shaded areas at a reference height (1.5 m) for the four tree species. (<b>a</b>) is <span class="html-italic">Khaya senegalensis</span>, (<b>b</b>) is <span class="html-italic">Terminalia neotaliala</span>, (<b>c</b>) is <span class="html-italic">Mangifera indica</span>, and (<b>d</b>) is <span class="html-italic">Ficus microcarpa</span>.</p>
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<p>Comparison of the degree of reduction in WS in the shaded area among the four tree species: (<b>A</b>) ΔWS = WS in the open area − WS in the shaded area; (<b>B</b>) reduction rate of WS = ΔWS/WS in the shaded area × 100%.</p>
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<p>Daily variation in PET between trees, under trees, and in the open area at a reference height (1.5 m) for the four tree species. The 80% acceptable rate of PET in Guangzhou is 27.25 °C in summer. The neutral PET in Guangzhou is 24.41 °C in summer. (<b>a</b>) is <span class="html-italic">Khaya senegalensis</span>, (<b>b</b>) is <span class="html-italic">Terminalia neotaliala</span>, (<b>c</b>) is <span class="html-italic">Mangifera indica</span>, and (<b>d</b>) is <span class="html-italic">Ficus microcarpa</span>.</p>
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<p>Comparison of the PET modification between open and shaded areas among four tree species: (<b>A</b>) ΔPET = PET in the open area − PET in the shaded area; (<b>B</b>) reduction rate of PET = ΔPET/PET in the open area × 100%.</p>
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<p>Differences between under-tree and inter-tree PET among the four tree species: (<b>A</b>) ΔPET = inter-tree PET − under-tree PET; (<b>B</b>) differential rate of PET = ΔPET/under-tree PET × 100%.</p>
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<p>Street orientation and the average reduction rates of four parameters: ΔWS, ΔAT, ΔPET, ΔST, and ΔSR.</p>
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18 pages, 6196 KiB  
Article
A Study on the Current Situation of Public Service Facilities’ Layout from the Perspective of 15-Minute Communities—Taking Chengdu of Sichuan Province as an Example
by Yihua Sun and Shixian Luo
Land 2024, 13(7), 1110; https://doi.org/10.3390/land13071110 - 22 Jul 2024
Cited by 1 | Viewed by 924
Abstract
With the rapid expansion of cities, the construction of 15-minute communities has become an important way to improve the urban living environment and enhance the quality of life of residents. In this study, based on the perspective of a 15-minute community in Chengdu, [...] Read more.
With the rapid expansion of cities, the construction of 15-minute communities has become an important way to improve the urban living environment and enhance the quality of life of residents. In this study, based on the perspective of a 15-minute community in Chengdu, the current situation of the spatial layout in the 12 main urban districts of 15,941 public service facility points is studied. Additionally, the matching relationship between the supply and demand of five major categories (19 subcategories) of public service facilities and the population is assessed by using the kernel density analysis method, the Gaussian two-step floating catchment area method, the hierarchical analysis method and the bivariate spatial autocorrelation. Finally, suggestions for the optimization of basic service facilities are made in the light of the current development situation in Chengdu. The results show that (1) there is a large spatial heterogeneity in the distribution and accessibility of public service facilities in the study area; (2) there is a mismatch between the supply and demand of public service facilities and the population in Chengdu; and (3) in order to further optimize the allocation of public service facilities, it is necessary to focus first on areas where demand exceeds supply. This study built a framework for assessing the current status of spatial distribution of public service facilities, which measures the 15-minute accessibility of basic public service facilities in a more comprehensive way and bridges the gap of previous single-type studies, which make it difficult to make comprehensive optimization recommendations directly. Meanwhile, the bivariate spatial autocorrelation reveals the areas of mismatch between supply and demand more accurately, and more clearly shows the areas that need to be focused on for optimization by policy makers. Full article
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<p>Analytical framework of the study.</p>
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<p>Location of the study area.</p>
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<p>Spatial distribution of population density in Chengdu in 2020.</p>
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<p>The spatial distribution of various PSF.</p>
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<p>Kernel density analysis for 5 types of public service facilities.</p>
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<p>Distribution of 15-minute accessibility related to (<b>a</b>) living; (<b>b</b>) healthcare; (<b>c</b>) education; (<b>d</b>) public transit; and (<b>e</b>) entertainment.</p>
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<p>Comprehensive 15-minute accessibility to public services facilities.</p>
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<p>Results for bivariate spatial autocorrelation. (<b>a</b>) Moran index; (<b>b</b>) results of spatial matching of population density and integrated accessibility.</p>
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