
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39223190
69881
10.1038/s41598-024-69881-8
Article
The relationships between the campus built environment and walking activity
Zhang Zhehao 1
Sun Tianyi tianyis6@illinois.edu

2
Fisher Thomas 3
Wang Haiming wanghaiming@yitsd.edu.cn

4
1 https://ror.org/01rp41m56 grid.440761.0 0000 0000 9030 0162 School of Architecture, Yantai University, Yantai, 264005 China
2 https://ror.org/047426m28 grid.35403.31 0000 0004 1936 9991 Department of Landscape Architecture, University of Illinois at Urbana-Champaign, Champaign, IL61820 USA
3 https://ror.org/017zqws13 grid.17635.36 0000 0004 1936 8657 School of Architecture, University of Minnesota, Minneapolis, MN55455 USA
4 School of Architecture and Engineering, Yantai Institute of Technology, Yantai, 264003 China
2 9 2024
2 9 2024
2024
14 2033021 2 2024
9 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Despite the gradual development of students’ sedentary habits and associated health problems, only a few studies have extensively and systematically measured campus built environments (CBE) and their impact on street walking activity. This study explores the association between CBEs and pedestrian volume (PV). Comprehensive questionnaires, field audits, and GIS were used to measure the CBE variables and PV of 892 street segments on eight Chinese campuses in Tianjin. We used negative binomial regression models without spatial autocorrelations to investigate the relationship between the CBEs and PV. The findings indicated that campus Walk Score, facility and residential land ratio, campus design qualities, sidewalk conditions, street amenities, and other streetscape features were positively associated with PV. This study presents implications for campus research and planning practices in designing a pedestrian-friendly, sustainable, and healthy campus.

Keywords

Built environment
Walking activity
Pedestrian volume
Campus planning
Subject terms

Environmental impact
Sustainability
Shandong Provincial Natural Science Foundation, ChinaZR2023QE328 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Walking is a green travel mode and an active lifestyle. It significantly affects the economy, environment, transportation, society, and health1–3. As an indispensable part of the urban environment, university campuses are imperative places to spread culture, inherit education, and impart knowledge. Walking is the primary method of traveling on campus and has remarkably influenced students’ daily lives. It can strengthen students’ physical activities and body health and promote communication and a sense of campus belonging4,5. Improved campus built environment (CBE) walkability highly promotes students’ walking comfort, reduces the pressure of study and life, progresses academic performance and college life satisfaction, and helps build a sustainable, green, and healthy campus6–8. However, the rapidly developing economy and Internet facilitate students to meet their daily learning and living requirements without leaving dormitories. Students’ sedentary habits have become increasingly common, gradually declining their physical activity, affecting their health, and causing obesity, diabetes, and other non-communicable diseases9,10. College life habits and physical and mental health conditions significantly affect future development10.

Researchers have verified the intervention design of the built environment from different interdisciplinary disciplines, such as public health, urban and transportation planning, and urban design, to promote walking activities11–14. In the health domain, academia has correlated the built environment and the multi-level mixed features of socioeconomic status (age, sex, income, etc.), psychological factors (self-selection), social indicators, physical activity, obesity, and body mass index (BMI) to improve residents’ health by intervening in the built environment of the residential area and providing policy guidance and active living guidelines12,14. Most studies have examined the built environment’s impact on health promotion following the ecological model proposed by Sallis' team14. Lee and Moudon suggested a behavior model of environment (BME) combining origin or destination, route, and area and established the physical environmental elements that affect walking activities13. Scholars have also primarily studied the influence of the built environment on travel mode choice, travel time and purpose (utilitarian and recreational or hedonic walking), vehicle travel miles, and other travel variables to provide theoretical and data support for advocating active travel behavior11,15,16. They commonly used the most influential built environment theoretical system, the “D” (density, diversity, design, distance to transit, destination accessibility, demographic, demand management) theory11,15. Corresponding to the “D” theory, in 1996, the London Planning Advisory Committee proposed the “5C” theory—connected, convenient, comfortable, convivial, and conspicuous17.

Although many scientific measurement systems and empirical evidence are available, two main research gaps remain. (1) Only a few studies have systematically selected all-around CBEs according to specific campus characteristics by combining GIS-measured gross qualities (diversity, street connectivity, etc.) and street environmental features to investigate the correlation between CBEs and walking activities. (2) Pedestrian volume (PV) is a significant indicator of walking activity; few studies have disentangled its relationship with on-campus built environments and presented campus planning and design strategies based on walking activity promotion. Because Chinese university students have no source of income and need to rely on their parents to pay tuition fees and living expenses, in order to reduce the cost of accommodation for university students, the Chinese government has provided subsidies to provide cheaper dormitories18. Meanwhile, for ease of management and to ensure the safety of students, almost all Chinese university campuses adopt a mandatory accommodation policy, requiring students to live in dormitories within the campus. This policy provides a great deal of autonomy for the students to make decisions by themselves to do social and recreational activities, with minimal control from university administrators and their parents19. Although students often study and live on campus, they can still free to travel and socialize off-campus. Moreover, there is no significant difference between weekdays and weekends in Chinese college students' lifestyle behavior, they still need to attend classes or study on weekends sometimes, except that they have more time for extracurricular activities (sports, shopping, etc.) on weekends. Since students' daily living, learning, and walking activities occur mainly on campus, it is crucial to disentangle CBE correlates of walking to propose campus planning and design strategies to improve campus walkability and promote PV. Although many developed countries' universities provide students with various accommodation options, many students still choose to live in dormitories or apartments within the campus, and the CBE features could still influence the daily traveling or activities of these students. Therefore, the results of this study can still provide references for other regional campus environment interventions to promote students’ walking activity on campus. Due to the limited resources, this study takes Chinese campuses as research samples and focuses on investigating relationships between CBE features and walking activity. Specifically, this study first aims to propose a research framework for studying the association between CBEs and walking activity and comprehensively select and optimize urban built environment features and methods based on the differences between campus and urban environments. We use eight campuses in Tianjin as an example to verify the proposed framework and the technical methods to provide certain generalizability for application in other regional campuses. The macro- (facility accessibility, land use, street connectivity, and spatial configuration) and micro-level environmental elements (design qualities and streetscape features) were combined as measured CBE features (Fig. 1). Second, it intends to understand better the extent to which various CBEs and architectural design elements are associated with PV to present pedestrian-oriented and green healthy campus planning and design strategies.Figure 1 The illustration of this study’s selected variables.

This study is organized as follows. Section "Literature review" introduces the literature on built environments, walking activities, and CBE studies. Section "Methods" presents the study area, data collection, and data analysis methods. Section "Result" discusses the results of the regression analysis. The findings of the study are conferred in Sect. "Discussion". Finally, Sect. "Conclusion" concludes the study.

Literature review

Built environment and walking activity

Academia has systematically combed and clarified the correlation between the built environment and different types of walking activities16. Considering that PV is the dependent variable in this study, this section summarizes PV-related studies from three main aspects: (1) study area and settings, (2) main research domains, and (3) measured built environments, data analysis methods, and the relationships between built environments and PV. Detailed information on these topics is provided in the subsequent paragraphs.Research on PV in English is mainly concentrated in developed countries1,20,21. The relevant theoretical systems were earlier proposed and applied in various cities of specific countries (New York, Salt Lake City, Minneapolis, etc.)20,21–24. Moreover, in cities such as Seoul (South Korea), the government collects urban street PV data, forms comprehensive open data, and provides data support for quantitative research. Therefore, scholars can easily disentangle the association between built environment features and PV1,25,26.

PV-related research domains can be divided into two directions. First, it proposes a statistical model to predict pedestrian demand and PV. It primarily analyzes traffic planning, engineering, and road safety21,23,27. The model can quantitatively examine pedestrian travel safety, evaluate the commercial vitality of streets, predict the PV in specific areas, and provide a powerful tool for designing an active pedestrian-oriented traveling environment. Second, it explores the impact of the built environment variables on PV and then strategizes building a healthy city based on promoting walking activities1,22. This direction primarily focuses on urban planning and design and evaluates the spatial form, space quality, and micro-perspective street environmental elements of the built environment. The first direction is primarily based on PV prediction, supplemented by measuring the built environment. The second direction focuses on measuring the built environment complemented by PV analysis21.

Most studies measured the built environment elements from multi-level dimensions, such as typical “D” variables measured by GIS and its related plug-ins and streetscape features measured by field audits. Some studies used percepted quality attributes based on the urban design quality (UDQ) theory20,22,24,28. Research primarily used a 100–400 m buffer zone to measure GIS-based environmental elements. Negative binomial and multiple linear regression models were the primary statistical analysis methods. Considering the influence of spatial autocorrelation between street PVs in regression outcomes, only a few studies have used spatial regression models to eliminate the spatial autocorrelation of model residuals22,24,28. Existing studies have found that residential density, transit accessibility, different types of land use proportion, street connectivity, spatial configurations, design qualities, streetscape features, and thermal comfort and weather factors are closely associated with PV1,21,24–26.

Nevertheless, such research has a few limitations. Some studies only considered PV of less than 100 street segments or observation points as research samples. Moreover, the regression results may have lower statistical prediction power and cannot accurately reflect the association between the built environment and PV22,29,30. Although most studies include similar built environment variables owing to differences in geographic settings, built environment elements significantly associated with PV often differ in the dimension and direction of their relationships. Therefore, the generalizability of the outcomes derived from existing studies should be verified when applied to other regions. Much research has focused on commercial and residential areas and urban public spaces, whereas few empirical studies have concentrated on universities, educational parks, and industrial regions.

Campus walkability and CBE studies

Many studies have systematically analyzed the campus walkability and walking activity domains. Researchers integrated special CBEs to develop walkability assessment tools. These tools can be categorized into three types. (1) Investigating students’ subjective perceptions of campus walking environments using questionnaires and interviews. Harun et al. explored respondents’ evaluations of streetscape features affecting campus walkability through questionnaires and used factor analysis to extract four categories—comfort, connectivity, safety, and accessibility—significantly affecting walkability31. Ramakreshnan et al. used a questionnaire to disclose students’ walking motivation, CBEs associated with walkability, and the relative importance of the studied CBE factors with socio-demographic characteristics in a tropical campus32. (2) Applying GIS and field audits to measure campus walkability objectively. Mu and Lao optimized the Walk Score measurement system by combining students’ daily travel characteristics to propose a GIS-based evaluation of campus walkability tool. Further, they tested the reasonableness of the new method through an empirical study33. Horacek et al. applied a field audit tool to assess campus path walkability/bikeability and investigated their relationship with BMI and other mediators34. (3) Incorporating objective and subjective methods to propose complicated and comprehensive campus walkability measurement systems. King et al. introduced an objective and perceived campus walkability assessment method comprising 24 key factors35, Zhang and Mu developed a synthetic quantitative method combining a walking preference survey and objective measurements calculated from GIS and audits8. These assessment studies strengthened their applicability in campus-related research. By combining campus walkability measurement outcomes, researchers have optimized the relevant urban built environment theoretical systems and methods to measure CBE and explore its impact on different fields’ issues. It mainly includes three aspects: (1) the travel behavior and modes of campus employees and students, (2) students’ college life satisfaction and academic performance, and (3) physical activity and students’ health status.

The first domain has mainly explored the influence of CBE from the factors of destination diversity, density of service facilities, street connectivity, and other features on the choice of travel mode, travel time, travel frequency, housing choice, and other travel requirements36,37. Vale et al. found that students living in residential areas with dense service and a good walking environment preferred to walk or ride on campus36. Bopp et al. found that students’ personal characteristics, campus environmental factors, and psychological and cognitive factors significantly affected the number of walking and cycling trips9. The second domain investigated the impact of campus form, planning structure, street network density, and green spaces on students’ retention and graduation rates, academic performance, and students’ subjective evaluation of college life4,6,7. Hajrasouliha proposed a campus score evaluation system to measure the campus environment quality and found that campus scores and freshmen retention and graduation rates are significantly correlated6. Hipp et al. found that high-quality green spaces positively impacted students’ quality of life7. The main objective of the third domain was to analyze the influence of CBE on students’ walking behavior, physical activity, obesity, BMI, and other physical health conditions. Empirical studies were mainly conducted on the location of the campus, campus scale, the difference between on- and off-campus built environments, and other factors to explore their impact on behavioral activities and health issues5,38,39. Peachey and Baller investigated students’ subjective perceptions of CBE. They found that the self-perceived quality of CBE and the physical activity intensity of students on campus were better than those outside the campus38. Reed and Ainsworth explored the relationship between students’ subjective perceptions of pedestrian walkways, campus safety, and walking activities. They found students’ perceptions of CBE, gender, and moderate physical activity are significantly correlated40. Researchers have also explored the effects of proximity to sports facilities on students’ walking activities. Reed found that proximity to sports facilities positively influences students’ physical activity and stimulates their willingness to exercise41.

In general, existing studies have remarkably achieved the CBE measurement, and many empirical findings have proven that specific built environment elements and complex indicators significantly impact students’ physical activity and college life. Despite the relatively mature research framework and theoretical system of the urban built environment, CBE research has yet to form a systematic or standard theoretical research model, particularly regarding the impact of CBE on student walking activities. Moreover, significant differences exist in the selected categories of environmental elements in different empirical studies. Compared to urban research, the selected elements in the CBE study often involve only a few aspects (accessibility, street design, etc.) and a limited systematic basis for selecting widely referred elements.

Methods

Study area

University campuses in Tianjin were considered as the study areas. Many university campuses are often built in high-education parks or university towns in Chinese cities. Tianjin has rich educational resources and diverse campus categories. Higher education parks and university towns in Tianjin can serve as specific areas for reflecting the current problems of Chinese university campuses. These can be used as representative regions for studying CBE and walking activities. Accordingly, this study selected eight comprehensive typical campuses from these areas as research objects (Fig. 2). Table 1 provides detailed information about the eight campuses. There are similarities and differences between the eight campuses. The Weijinlu campus of Tianjin University (WCTU) and the Balitai campus of Nankai University (BCNU) were built in the 1950s and are located in the downtown area. These campuses are planned on a cluster mode, with a balanced distribution of facilities within the clusters and different types of commercial facilities located on some of the streets around the campuses. The other six campuses were constructed and opened around 2010. They are all located in suburban areas and contain both axial and clustered planning layouts, with on-campus facilities distributed based on axes and clusters. Three campuses, Tianjin Polytechnic University (TPU), Tianjin Normal University (TNU), and Tianjin University of Technology (TUT), are located adjacent to one of the big high-education parks in Tianjin, while Beiyangyuan campus of Tianjin University (BCTU), Jinnan campus of Nankai University (JCNU), and Hebei University of Technology (HUT) are situated in other high-education parks. Only a few streets with commercial facilities surround these six campuses. While the number of facilities around these six campuses differs from that of the urban campus, this difference has little influence on on-campus walking activities as students primarily use on-campus facilities to meet their daily requirements. Notably, it can be seen from Table 2 that although there are differences in planning patterns and locations among the eight campuses, the number of facilities and distribution patterns within the campuses are less different due to students' similar learning and living needs. The total number of facilities on each campus is similar to each other. The average number of eight campuses is 39. Specifically, the number of facilities, such as libraries, administrative buildings, etc., is consistent among the campuses, which ensures that the analysis of facility-related indicators is reasonable. Moreover, the chosen campuses have various functions that can reflect the distinctive characteristics of Chinese campuses.Figure 2 The location of the eight campuses.

Table 1 The basic information of the eight case campuses.

University campus	Land area/10000m2	Population	Number of selected streets	Number of questionnaires	Male (female)	Undergraduate	Postgraduate	Ph.D. candidate	
WCTU	136	19,000	151	153	50.9% (49.1%)	34.5%	34.5%	31%	
BCNU	121	19,000	121	98	55.3% (44.7%)	33.3%	45.8%	20.9%	
BCTU	244	20,000	152	137	61.3% (39.7%)	44.5%	33.5%	22%	
JCNU	246	14,000	93	62	48.8% (51.2%)	37.6%	40.7%	21.7%	
HUT	204	30,000	62	66	55.5% (43.5%)	65.8%	33.2%	–	
TPU	195	25,000	118	63	54.2% (45.8%)	57.2%	42.8%	–	
TNU	233	34,000	90	59	48.5% (51.5%)	60.8%	39.2%	–	
TUT	156	29,000	105	57	52.9% (47.1%)	58.9%	41.1%	–	

Table 2 The number of facilities in each of the eight campuses during the research period.

Number of facilities	WCTU	BCNU	BCTU	JCNU	HUT	TPU	TNU	TUT	
Canteens and restaurants	6	5	7	7	5	6	6	6	
Public teaching buildings	5	3	5	3	3	3	3	5	
Stores	4	5	5	3	4	3	4	4	
Libraries	1	1	1	1	1	1	1	1	
Squares and green spaces	11	13	16	15	14	12	12	13	
Outdoor stadiums	3	3	3	5	4	4	4	3	
Coffee shops	2	3	3	4	2	2	2	2	
Banks and post offices	2	2	2	2	4	3	3	2	
Administrative buildings	1	1	1	1	1	1	1	1	
Barbershops	1	2	2	2	2	1	1	1	
Total number of all facilities	36	38	45	43	40	36	37	38	

Because streets can reflect pedestrian activity and environmental vitality, quality, and streetscape features well, they can be considered the research object for studying the CBE and PV correlation. Therefore, data were collected from 892 street segments selected from the eight campuses (Table 1, Fig. 3). The principle of street selection consists of two aspects: (1) the chosen samples should contain streets comprehensively and cover different land-use zones and functional groups; (2) streets should have buildings or streetscape features so that pedestrian activity occurs theoretically. Owing to variations in building distribution and landscaping on different campuses, the number of streets selected sometimes does not match the campus population density and size. For example, although WCTU and BCNU have small campus scales and population densities, they have high building densities and diverse land uses, especially WCTU, where more street samples are available for research. However, despite having a high population density and campus size, HUT has a relatively low building density, large green landscapes, and undeveloped land areas; students mainly study and live in specific areas. Meanwhile, some sites were under construction. Therefore, the sample of streets available for research was small, and we selected 62 street segments. The environmental characteristics of the central parts of the other campuses were relatively consistent, and the number of street segments selected for each campus was controlled at approximately 100. We eliminated street segments that did not meet the principles. The selected street segments’ walking activities and environmental characteristics could represent typical Chinese campus street environments and walking behaviors.Figure 3 The location of the selected streets.

The CBE variables

Since this study aims to propose a conceptual framework to study the association of CBEs with PV, we systematically compared widely applied environmental elements in urban built environments and walkability research16,42 and optimized the corresponding technical methods by combining the special environmental characteristics of campuses and the daily travel patterns of students. We test the reasonableness of the framework and technical methods by taking eight campuses in Tianjin as an example so that we can apply the validated framework and methods to measure campuses in other regions. Accordingly, we selected built environment elements from both the macro- and micro-perspectives. For the macro-level elements, we chose the Walk Score to calculate facility accessibility, land-use mix, street connectivity, and spatial configuration. Furthermore, we selected environmental design qualities and streetscape features as the micro-level elements. Based on the differences between campuses and cities regarding spatial form and travel modes, we optimized the measurement method according to local conditions. The Chinese campus’s closed boundary, compulsory housing policy, and the patterned “dormitories-facilities (stores, canteens, etc.)-dormitories” have addressed students’ daily lives and their housing, learning, and living needs within the campus. Therefore, for a larger buffer zone, the urban environment outside the campus (street connectivity, land use, etc.) was included in the measured indicators, which otherwise would provide inaccurate results that do not truly reflect the impact of the campus environment on student walking behavior. The scale of the street network of Chinese campuses is small; scholars often choose short buffer zones of 100 m43 and 200 m44 to explore the effects of small-scale, narrow-block-built environments on walking activities. Accordingly, this study selected three buffer ranges of 100 m, 200 m, and 300 m. Pearson correlation analysis between objective CBEs and PV in each buffer revealed that the overall correlation of the 100 m buffer was the weakest, while that of the 300 m buffer was slightly higher than that of the 200 m. Although the 300 m buffer had a high correlation, this scale range would extensively overlap with off-campus urban environments, resulting in erroneous measurements. Therefore, we used a buffer zone with a radius of 200 m actual sidewalk network distance to calculate the associated objective physical environment features.

Similar to the main approaches adopted in the literature and based on the independent variables selected for this study30,45, we measured all elements opening onto/facing a specific street as reflected in the following two categories. For macro-level CBEs, the elements represented destination accessibility and land use attributes. Specifically, we calculated destination accessibility based on the distance to on-street facilities. The land use entropy and proportions in the selected land use attributes were calculated based on all land areas surrounding the street. For micro-level CBEs, we counted the number of buildings, street furniture, public arts, and other physical street features. Moreover, we evaluated the proportion of street walls associated with the façades of buildings facing the studied street.

Macro-level environmental features

Campus walk score

Walk Score is an index to measure facility accessibility. It is calculated using the gravity model and consists of three parts: facility weight, walking-time attenuation coefficient, and street network connectivity46. The Walk Score first attenuates the weight of the corresponding facilities by measuring the shortest distance from the evaluation point to different types of facilities and obtains the initial value. Second, the final score was obtained by adjusting the initial values using the intersection density and block length. The Walk Score is calculated based on an entire age group, however, college students’ facility usage characteristics are specific, and applying the Walk Score methodology directly to campuses can provide inaccurate results. Therefore, we optimized the Walk Score measurement system based on the following three aspects: students’ weekly usage of service facilities, their willingness to walk to various facilities, and campus-specific street network characteristics.

Accordingly, we adopted Zhang et al.’s method to develop the questionnaire from two main aspects to obtain facility weight and walking times: (1) the weekly usage frequencies of students walking to different types of facilities and (2) walking times to various facilities47. Furthermore, we obtained students’ ages, sex, grades, and other personal information. Through randomized interviews in the early stages, we selected dozens of facilities that the students could use daily. Considering that university hospitals have a very low usage frequency by students and non-public service facilities such as office laboratory buildings serve each faculty member independently, we excluded these facilities from the study. Simultaneously, because HUT and TNU do not have stadiums and student activity centers, we excluded these two facility types and developed a questionnaire based on the characteristics of the 11 types of facilities. We asked respondents the number of times they used each type of facility weekly. According to the decay principle of the Walk Score46, the weight of a facility does not decay within 5 min (300 m) of walking. It decreases by 12% after 5–20 min. When the walking time increased to more than 30 min (2300 m), the attenuation coefficient exceeded 1, and the Walk Score decreased to 0. Based on this principle, we defined (1) walking comfort time: when students’ walking willingness did not decrease during this time, (2) walking tolerance time: from comfort time to this time, students’ walking willingness decreased as walking time increased; and (3) walking resistance time: after this time was exceeded, students no longer walked to the facilities.

We developed the web-based questionnaire by “Questionnaire Star” (http://www.sojump.com/), a professional online questionnaire platform providing the fast, easy-to-use, and low-cost online design of questionnaires, data collection, and other functional services. We randomly selected students living on each campus to fill out the questionnaire during the working day. Each participant took an average of 10 min to complete the survey. After questionnaire completion, the respondent received a gift or red packet worth 5 yuan. The closed campus environment and regular study and lifestyles in China made daily travel and facility usage characteristics of college students very similar. Therefore, although the resource limitations and sample size were relatively small, they still represented the daily facility needs of college students in the study area. All survey data were collected between April and May 2019. Table 1 displays partial questionnaire information.

This study was reviewed and approved by the Research Ethics Committee of each university. All surveys were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all respondents when they completed the questionnaire. The questionnaires received were unmarked. Owing to the similarity between the daily travel patterns and facility requirements of Chinese university students, only slight differences existed in facility usage characteristics among the eight campuses. To facilitate the calculation, we summarized the data collected from the eight campuses and used their average values to calculate the weights of the 11 types of facilities and walking times. We determined each type of facility weight based on the proportion of its usage frequency to the total facility usage frequency. For instance, the weight of the stores was determined by (4.31/33.17) × 100 = 10.51. Moreover, we obtained three types of walking time related to different facilities. Table 3 shows the final facility weight and its related walking times. For instance, the comfortable walking time of stores was 6 min, meaning that walking willingness remained the same within this time. The walking tolerance time was 17 min, which means that walking intention dropped as the time increased from 6 to 17 min. Outside this interval, the walking willingness continued to decrease with decreasing gradients. However, after exceeding 24 min, students no longer walked to the stores.Table 3 The facility weight and three types of walking time.

Facility type	Usage frequency	Weight	Comfortable walking time	Tolerant walking time	Resistant walking time	
Canteens and restaurants	21	51.19	6	17	24	
Public teaching buildings	5.06	12.34	6	17	24	
Stores	4.31	10.51	6	17	24	
Libraries	2.03	4.95	7	18	24	
Squares and green spaces	2	4.87	7	18	24	
Bus stops	1.81	4.41	6	17	24	
Outdoor stadiums	1.4	3.41	8	18	24	
Coffee shops	1.32	3.22	7	18	24	
Banks and post offices	0.98	2.39	7	18	24	
Administrative buildings	0.86	2.1	6	17	24	
Barbershops	0.25	0.61	7	18	24	

Based on the Walk Score algorithm and the method of Zhang et al., we obtained the original score of the midpoint of the street segment46–48). The Walk Score method considered pedestrian friendliness, comprising intersection density and block length in the algorithm to obtain more accurate results. This indicated that areas with poor pedestrian friendliness attenuated the initial score by a certain percentage46. The Walk Score also stated that the attenuation range for intersection density and block length is 0–5%, and the maximum attenuation amount for a location is 10% of the initial value. Specifically, for intersection density per square mile, > 200 implies no penalty, whereas for < 60, the penalty is a maximum of 5%. Regarding block length, < 120 m means no penalty, while > 195 m implies a maximum of 5% penalty46. Therefore, this principle was applied to reduce the original scores. The campus Walk Score was calculated as follows:Campus Walk Score=∑i=1nwi×g(dij)×1-ID×1-BL,

where wi is the facility weight of a specific facility type, n is the category of facilities, dij is the shortest network distance from an evaluation location to the facility i, g(dij) is the attenuation coefficient of the walking time based on this distance, and ID and BL are the attenuation coefficients of the intersection density and block length, respectively. Finally, the accessibility index of the overall Walk Score was established.

Land use

Consistent with the land use classifications in urban studies1,24,43, we divided the campus land into facility, residential, office, park, and squares. However, considering the campus unique land use characteristics, the land for facilities can be divided into two types: one includes public teaching buildings and libraries,the other contains stores, canteens, and other public facilities; residential land includes dormitories, faculty apartments, and residential areas; office land contains office and training buildings, college buildings, and other buildings; and park and square land includes parks, squares, and water bodies. Three indicators were established to reflect campus land use attributes: land use entropy, facility land ratio (ratio of the facility land to the buffer zone area), and residential land ratio (ratio of the residential land to the buffer zone area). The entropy index is calculated as follows:Entropy=-∑i=1nPilnPilnn,

where n represents the five land types, and Pi represents the area proportion of land i in the total land.

Street connectivity

This study combined sidewalks with park paths and other pedestrian systems and used actual pedestrian paths as the measurement network. Because campus Walk Score was attenuated based on intersection density and block length, we eliminated these two variables and selected two indicators to evaluate the street connectivity attributes—the 4-way intersection ratio and pedestrian route diversity (PRD) (the ratio of the actual distance from the origin to the destination to the Euclidean distance; we used the Spatial Analyst Tools function in GIS to calculate the actual distance and Euclidean distance). When calculating the PRD in urban environments, large public service facilities, such as commercial complexes or cultural and political centers, are often selected as the main destinations. Considering that the typical landmark buildings of Chinese campuses are libraries, administrative buildings, student activity centers, and stadiums, this study selected these four types of facilities to calculate the PRD and considered the average value as the final PRD. The PRD index is calculated as follows:PRD=averageDirdDigd,

where Dird is the actual distance, Digd is the Euclidean distance.

Spatial configuration

As essential spatial elements, spatial configuration attributes can influence walking choices and activity intensity1,49. This study used the spatial design network analysis (sDNA) plug-in in QGIS 3.12 to measure the spatial structure attributes. sDNA can be used to analyze any kind of spatial network and explore its relationships with various phenomena (walking activity, health, community cohesion, etc.) in different domains (transport planning, urban design, architecture, etc.)50. Its calculation is simple and can directly rely on the street and road network files of GIS. It measures the spatial structure of the network through the integral analysis function. Based on the classification standard of sDNA elements by Kang and Cooper et al.1,49, we finally chose four indices as the campus spatial configuration indicators: closeness, calculated as the number of network links divided by the Euclidean distance between the origin and all reachable destinations within each radius,The closeness is calculated as follows:Closeness=∑y∈Rxp(y)dM(x,y).

Rx is the set of links starting from link x within a given network radius. p(y) is the weight of node y within the search radius R; dM (x, y) is the shortest Euclidean distance from node x to node y. Betweenness assesses all possible trips passing through a network link. It is calculated as follows:Betweenness=∑y∈N∑y∈RxpzODy,z,x.

OD(y, z, x): the geodesic endpoints are y and z, not x, when it is measured. Severance (the diversion ratio), calculated as the mean value of the shortest network distance divided by the Euclidean distance over all links; The severance is calculated as follows:Severance=∑y∈RxdM(x,y)CFD(x,y)p(y)∑y∈Rxp(y).

CFD (x, y) is the crow flight distance between the centers of x and y. Efficiency measures the navigability of the connected links covering space or distance in local areas. It is the distance from the origin to the point where the convex hull has its greatest radius. In other words, it is the largest distance to any point within the network radius and, as such, represents the single route accessible from the origin that can cover the most distance1,49.

Micro-level environmental features

Ewing et al. indicated that the UDQ is the perceived quality of the environment, consisting of physical streetscape features. The streetscape features individually do not comprehensively reflect the experience of walking on the streets, and the UDQ has a higher cumulative effect than the sum of individual streetscape features20. The UDQ establishes a link between streetscape features and walking activities. Combined with the components of streetscape features, it determines the environmental attractiveness of walking. Additionally, because the UDQ is composed of streetscape features, multicollinearity problems arise if the two are analyzed simultaneously in the model. To solve this problem, scholars have separately explored the relationship between UDQ, streetscape features, and PV20,24,51. Therefore, this study selected design qualities and streetscape features as the micro-level environmental features.

Environmental design quality

We applied the UDQ measurement tool to measure the campus subjective perceived space environmental qualities. Ewing et al. proposed this measurement system comprising five design qualities—imageability, enclosure, human scale, transparency, and complexity20. Each quality consists of different streetscape features. We measured the constituent features of each quality through field observations, and Table 4 expounds on the methods used to measure each feature. This system is primarily applied to commercial streets or streets with commercial facilities. Although a university campus is not a pure regional environment dominated by commercial land, it is often equipped with commercial facilities of different business types and streets in the living area. Therefore, university campuses also have a strong applicability for this system. We optimized this system according to the campus’s unique environmental characteristics. We followed the formulas in Table 4 to calculate different qualities20. Finally, we added the overall design quality summed by the five classified qualities and constructed six quality indices.Table 4 Urban design qualities, their physical features, coefficients and P-values (Source: Ewing and Clemente20).

Urban design quality	Significant physical features	Method of evaluation	Coefficient	p-value	
Imageability	Proportion of historic buildings	Assess the proportion of historic buildings on both sides of the street	0.97	 < 0.001	
Courtyards/plazas/parks (#)	Count the number of courtyards, squares, and parks on both sides of the street	0.414	 < 0.001	
Outdoor dining (yes/no)	Records with outdoor dining chairs are 1; no records are 0	0.644	 < 0.001	
Buildings with non-rectangular shapes (#)	Count the number of buildings with non-rectangular shapes on both sides of the street	0.0795	0.036	
Noise level (rating)	Evaluate the street noise level; 1 = very quiet; 2 = quiet; 3 = normal; 4 = noisy; 5 = very noisy	 − 0.183	0.045	
Major landscape features (#)	Count the number of landscape features on both sides of the street	0.722	0.049	
Buildings with identifiers (#)	Count the number of buildings with identifiers on both sides of the street	0.111	0.083	
Enclosure	Proportion street wall1—your side	Assess the proportion of your side street walls to the total length of the block	0.716	 < 0.001	
Proportion street wall—opposite side	Assess the proportion of opposite side street walls to the total length of the block	0.94	0.002	
Proportion sky2 across	Evaluate the proportion of the sky visible in the frame across the street	 − 2.193	0.021	
Long sight lines (#)	Record the number of long sight lines that can be seen in the field of view to the front, left, and right of travel, with values from 0 to 3	 − 0.308	0.035	
Proportion sky ahead	Evaluate the proportion of the sky visible in the frame ahead of the street	 − 1.418	0.055	
Human scale	Long sight lines (#)	Record the number of long sight lines that can be seen in the field of view to the front, left, and right of travel, with values from 0 to 3	 − 0.744	 < 0.001	
All street furniture and other street items (#)	Count the number of all street furniture and other street items on both sides of the street	0.0364	 < 0.001	
Proportion of first-floor with windows	Assess the proportion of glass interfaces in the first-floor façade of the buildings	1.099	 < 0.001	
Building height—your side	Calculate the average height of all buildings along your walking side	 − 0.00304	0.033	
Small planters (#)	Count the number of small planters on both sides of the street	0.0496	0.047	
Transparency	Proportion of first-floor with windows	Assess the proportion of glass interfaces in the first-floor façade of the buildings	1.219	0.002	
Proportion of active uses	Assess the proportion of street buildings with active uses along your walking side	0.533	0.004	
Proportion of street wall—your side	Assess the proportion of your side street walls to the total length of the block	0.666	0.011	
Complexity	Buildings (#)	Count the number of buildings on both sides of the street	0.051	0.008	
Dominant building colors (#)	Count the number of dominant building colors on both sides of the street	0.177	0.031	
Accent building colors (#)	Count the number of accent building colors on both sides of the street	0.108	0.043	
Outdoor dining (yes/no)	Records with outdoor dining chairs are 1; no records are 0	0.367	0.045	
Public art (#)	Count the number of public art on both sides of the street	0.272	0.066	
1. We evaluated the proportion of street walls on the street based on the campus street network and buildings' GIS and CAD files. 2. Form a simple frame with the fingers, place it in front of your face, and slowly move the frame until your eyes can see the scenes outside the frame through it. Evaluate the percentage of the sky inside the frame (use decimal increments of 0.10)20. 3. Because the dependent variable in this study is PV, which is included in the original element of imageability and complexity, to avoid the multicollinearity problem that arises during regression analysis, we calculated the two design qualities without considering PV20,24.

Streetscape features

Because the UDQ system contains a relatively all-round list of streetscape features that were selected based on the systematic discussion and evaluation of the expert group20, we considered the streetscape features in the UDQ system as the main framework. Table 4 displays the measurement of each feature in detail. We further combined the sidewalk length, sidewalk quality (0 = absent, 1 = poor, 2 = medium, 3 = good), number of lanes, condition of street trees (0 = no or few street trees, 1 = more or continuously arranged street trees), and number of street monitors on both sides of the street, which are commonly used in the walking activity study to supplement the UDQ elements and construct a measurement index of micro streetscape features52. Because only 6.73% of the 892 streets contained historical buildings, these street segments were not included in the measurement system. Concurrently, only 3.8% and 0.9% of the streets had small planters and outdoor dining tables, respectively; therefore, these indicators were excluded from the system. In addition to the newly added and eliminated elements, we counted the number of buildings, building identifiers, non-rectangular buildings, basic building colors, accent building colors, street furniture, public art, major landscape features, courtyards and parks, and long sight lines. We further evaluated noise level, building height, and the respective percentage of street walls, sky, first-floor windows, and active uses. Finally, we set 21 elements as the streetscape features and measured them according to the method described by Ewing et al.20. Data were collected between March and October 2019. The investigations were canceled during bad weather (rain or strong winds).

PV

This study applied the method of Ewing et al. to calculate the PV of campus streets20. This method has been widely used in studies of UDQ systems20,24,51. Specifically, we walked through the measured street segments and recorded the pedestrians we encountered performing walking activities. The recorded walking activities included sitting, standing, walking, running, and riding, excluding people sitting in outdoor dining seats on the streets. Since college students walk on weekdays in various modes, including purposeful walking, such as going to teaching buildings for classes, libraries, etc., and recreational walking, it is more likely to reflect the impact of CBEs on walking activity. Meanwhile, weekday walking behaviors can also reflect weekend walking modes, which are mainly for leisure and recreation. Moreover, the data in this study were collected through field observations, and due to limited resources, we measured weekday walking activities on the street. To avoid the impact of a single accidental statistical result on the accuracy of the measured data, this study selected different weekdays (from Monday to Friday) and times for the four data statistics. Finally, the average of the four values was considered the PV value of the measured street (Fig. 4). Data collections were conducted between March and October 2019. Unlike urban travel time, the peak time for campus walking is when students leave class at noon and go to the canteens and dormitories. Therefore, to avoid the impact of this specific walking flow on the measured data, the statistical time of this study was from 9 a.m. to 12 a.m. and from 1 p.m. to 5 p.m. Counts were canceled during bad weather (rain or strong winds).Figure 4 Distribution map of PV in the streets of eight campuses.

Data analysis

PV is the dependent variable in this study, with integrated and non-negative values, and does not conform to a positive distribution. Because the dependent variable must be a non-negative integer, applying a multivariate linear regression model for data analysis may predict negative outcomes and obtain inaccurate and biased estimates. Accordingly, applying models specific to count outcomes is more reasonable. Poisson and negative binomial regression models were applied to count outcomes53. Poisson regression is suitable for data that equals the mean and standard deviation of dependent variables, while negative binomial regression contains an extra parameter. Therefore, the latter is a better alternative to Poisson regression for over-dispersed data with a standard deviation higher than the mean20. Hilbe indicated that the condition that the mean is equal to the variance is difficult to satisfy in actual data and that the overdispersion of the data is the norm,therefore, negative binomial regression is an extension of the Poisson regression model54. Through empirical comparisons, scholars have found that negative binomial regression has better predictive and model performance capabilities than ordinary least-squares regression55. Most empirical studies using PV as the dependent variable consider negative binomial regression for analysis20,24,28. Similar to this study’s data structure, two teams applied negative binomial regression models to analyze the data20,24. Specifically, the mean PV in this study was larger than the standard deviation, and we used the dispersion test to find overdispersion in the Poisson models. Consequently, we chose the negative binomial regression for the subsequent regression analysis. In the negative binomial model, for individual i, the probability of Yi = yi is assumed to be determined by a distribution with parameter λ.PYi=yixi)=e-λλyiyi!yi=0,1,2⋯.

We used Maximum likelihood estimation to estimate α and β in the models exhibited in the following models:lnλ=β0+β1×x1+β1×x1+⋯+βj×xi.

Because environmental design quality consists of streetscape features, two main types of models were constructed to avoid collinearity between elements: (1) macro-level environmental elements and micro-level design qualities and (2) macro-level environmental elements and micro-level streetscape features. Accordingly, the two equations of the models for predicting PV values are as follows:1 PV=eβ0+βwasc×Xwasc+βlaus×Xlaus+βstco×Xstco+βspco×Xspco+βdequ×Xdequ

2 PV=eβ0+βwasc×Xwasc+βlaus×Xlaus+βstco×Xstco+βspco×Xspco+βdequ×Xstfe,

where PV = estimated PV, Xwasc = campus overall Walk Score, Xlaus = Land use elements, Xstco = Street connectivity elements, Xspco = Spatial configuration elements, Xdequ = Overall design quality and classified design qualities, Xstfe = Streetscape features, βwasc, βlaus, βstco, βspco, βdequ, βstfe = coefficients for associated explanatory variables, and β0 = constant derived from the model.

Considering that the overall design quality comprised 5 classified qualities, we built two models (Model 1 and Model 2) for the first type and one (Model 3) for the second type (Table 3). We further analyzed the correlations between the independent variables and excluded variables with correlation coefficients greater than 0.6, ensuring the rationality of the data structure. Although the correlation between the human scale and enclosure was higher than 0.6 (r = 0.653), both were considered essential variables and were retained for further analysis. Finally, we took 36 environmental variables into analysis (Table 5).Table 5 The compositions of the three models.

Environmental variables	Model 1	Model 2	Model 3	
Macro-level elements		Overall Walk Score	√	√	√	
4 Land use features	√	√	√	
2 Street connectivity features	√	√	√	
3 Spatial configuration features	√	√	√	
Micro-level elements	Micro-level design qualities	Overall design quality	√			
5 Classified design qualities		√		
Micro-level streetscape features	21 Streetscape features			√	
√ represents the variables contained in the regression models.

Owing to the spatial proximity between the measured street segments, the PV of a specific street is affected by the PV of adjacent streets, which leads to spatial autocorrelation in the regression model. Therefore, testing and eliminating spatial autocorrelation is crucial to improve the accuracy of the regression outcomes. By calculating the fitted models’ Moran’s I using ArcGIS 10.1, we found that the P values of the six indices were all notable, which means that all six models had spatial autocorrelation. Studies have proven that the spatial filtering function can effectively eliminate spatial autocorrelation22,24,28. The advantage of this method is that the significance of the independent variables in the model is corrected, but the coefficient is not affected.

Therefore, we first imported the .shp files of streets with the geospatial coordinate system into GeoDa 1.16 to construct a spatial weight matrix. Subsequently, we imported the .shp files and their corresponding matrix GAL files into the R 4.1.0 language. Further, we used the ME (Moran Eigenvectors) function in the spatialreg package to remove the spatial autocorrelation of the regression models and obtain a certain number of spatial filtering eigenvectors. Finally, we checked Moran’s I of the residues and found no spatial autocorrelation in the built models.

Result

The descriptive statistics of the CBE variables and PV are presented in Table 6. We first tested the collinearity problem and found that the variance inflation factor (VIF) of each element in the three models was less than 3, indicating no collinearity between the variables in the fitted models. Subsequently, we removed spatial autocorrelation in the fitted models to obtain accurate regression results. The results are shown in Tables 7 and 8.Table 6 Descriptive statistics (n = 892).

Category	Variable	Max	Min	Mean	SD	
Dependent variable	PV	33.00	0.00	4.42	4.03	
Macro-level environmental elements	Campus Walk Score	
 Overall Walk Score	99.38	53.05	89.37	7.66	
Land use	
 Entropy	1	0.14	0.66	0.14	
 Teaching buildings and libraries’ land ratio	0.68	0	0.07	0.12	
 Stores and restaurants’ land ratio	0.62	0	0.10	0.11	
 Residential land ratio	0.89	0.00	0.20	0.19	
Street connectivity	
 4-way intersection ratio	0.81	0.00	0.35	0.15	
 PRD	3.15	1.02	1.39	0.23	
Spatial configuration	
 Betweenness	197.07	0.01	19.67	23.63	
 Severence	2.22	1.00	1.30	0.17	
 Efficiency	321.05	32.26	197.42	36.62	
Micro-level environmental elements	Micro-level design qualities	
 Overall design quality	19.83	8.35	14.03	1.86	
 Imageability	5.93	1.62	2.67	0.53	
 Enclosure	3.09	0.29	2.32	0.99	
 Human scale	3.15	1.13	2.73	0.56	
 Transparency	3.77	1.71	2.94	0.33	
 Complexity	5.51	2.70	3.52	0.31	
Micro-level streetscape features	
 Buildings with non-rectangular shapes	3.00	0.00	0.91	0.82	
 Building height	60.00	0.00	16.11	8.60	
 Buildings	15.00	0.00	2.01	1.32	
 Basic building colors	8.00	0.00	1.46	0.89	
 Accent building colors	10.00	0.00	2.35	1.33	
 Sidewalk length	283.90	35.06	97.65	35.08	
 Sidewalk quality	3.00	0.00	2.13	1.05	
 Number of lanes	6.00	0.00	1.46	0.87	
 Noise level	5.00	1.00	2.87	0.85	
 Street furniture	31.00	0.00	6.83	3.83	
 Street monitor	7.00	0.00	0.86	1.23	
 Street trees	1.00	0.00	0.77	0.33	
 Public art	3.00	0.00	0.27	0.59	
 Major landscape features	3.00	0.00	0.76	0.78	
 Courtyards, plazas, parks	2.00	0.00	0.12	0.33	
  % street wall (your side)	1.00	0.00	0.65	0.28	
  % street wall (opposite side)	1.00	0.00	0.32	0.39	
 % sky (your side)	0.50	0.10	0.24	0.10	
Long sight lines	2.00	0.00	0.59	0.63	
 % first-floor windows	0.90	0.00	0.34	0.19	
 % of active uses	1.00	0.00	0.75	1.23	
Significant values are in bold.

Table 7 Associations between macro-level environmental elements and micro-level design qualities and PV.

	Model 1	Model 2	
Coef	Std	P	Coef	Std	P	
Constant	 − 5.579***	0.496	 < 0.001	 − 5.323***	0.470	 < 0.001	
Macro − level environmental elements	
 Campus Walk Score	
  Overall Walk Score	0.057***	0.005	 < 0.001	0.045***	0.004	 < 0.001	
 Land use	
  Entropy	1.192***	0.172	 < 0.001	0.543***	0.157	 < 0.001	
  Teaching buildings and libraries’ land ratio	0.842***	0.202	 < 0.001	0.823***	0.189	 < 0.001	
  Stores and restaurants’ land ratio	1.144***	0.209	 < 0.001	0.904***	0.203	 < 0.001	
  Residential land ratio	0.235*	0.139	0.042	0.236	0.144	0.102	
 Street connectivity	
  4 − way intersection ratio	0.111	0.165	0.502	0.359*	0.153	0.019	
  PRD	 − 0.411***	0.106	 < 0.001	 − 0.441***	0.100	 < 0.001	
 Spatial configuration	
  Betweenness	 − 0.003**	0.001	0.008	 − 0.002*	0.001	0.012	
  Severence	 − 0.347**	0.125	0.006	 − 0.426***	0.119	 < 0.001	
  Efficiency	0.002**	0.001	0.004	0.001*	0.001	0.021	
Micro − level design qualities	
 Overall design quality	0.104***	0.013	 < 0.001				
  Imageability				0.257***	0.040	 < 0.001	
  Enclosure				 − 0.107***	0.030	 < 0.001	
  Human scale				0.234***	0.052	 < 0.001	
  Transparency				0.159**	0.055	0.004	
  Complexity				0.394***	0.059	 < 0.001	
 Spatial filtering eigenvector	
  Fit (ME)	1.530*	0.682	0.025	 − 1.394*	0.547	0.011	
  Fit (ME)	 − 1.264*	0.551	0.022	 − 1.486**	0.553	0.007	
  Fit (ME)	 − 1.794**	0.556	0.001	 − 1.437*	0.609	0.018	
  Fit (ME)	1.299*	0.604	0.032	0.437	0.664	0.510	
N	892	892	
2 × log − likelihood(df)	 − 3841.83 (855)	 − 3802.77 (855)	
AIC	3918	3879	
***p < 001, **p < 01, *p < 0.05.

Significant values are in bold.

Table 8 Associations between macro-level environmental elements and micro-level streetscape features and PV.

	Model 3	
Coef	Std	P	
Constant	 − 5.932***	0.454	 < 0.001	
Macro − level environmental elements	
 Campus Walk Score	
  Overall Walk Score	0.056***	0.004	 < 0.001	
 Land use	
  Entropy	0.164a	0.179	0.091	
  Teaching buildings and libraries’ land ratio	0.333*	0.179	0.041	
  Stores and restaurants’ land ratio	0.617**	0.199	0.002	
  Residential land ratio	0.467***	0.126	 < 0.001	
 Street connectivity	
  4 − way intersection ratio	0.130	0.144	0.368	
  PRD	 − 0.224*	0.099	0.024	
 Spatial configuration	
  Betweenness	0.001	0.001	0.251	
  Severence	0.087	0.107	0.416	
  Efficiency	 − 0.001	0.001	0.454	
Micro − level streetscape features	
 Buildings with nonrectangular shapes	0.090***	0.024	 < 0.001	
 Building height	0.005a	0.002	0.056	
 Buildings	0.056**	0.015	 < 0.001	
 Accent building colors	0.024	0.016	0.137	
 Basic building colors	 − 0.066**	0.023	0.004	
 Sidewalk length	0.002***	0.001	 < 0.001	
 Sidewalk quality	0.126***	0.023	 < 0.001	
 Number of lanes	0.022	0.023	0.322	
 Noise level	0.240***	0.025	 < 0.001	
 Street furniture	0.034***	0.005	 < 0.001	
 Street monitor	 − 0.010	0.018	0.552	
 Street trees	0.109*	0.045	0.016	
 Public art	0.077**	0.029	0.009	
 Major landscape features	0.054*	0.026	0.043	
 Courtyards, plazas, parks	0.083	0.056	0.135	
  % street wall (your side)	 − 0.030	0.083	0.716	
  % street wall (opposite side)	0.051	0.059	0.390	
  % sky (your side)	0.162	0.251	0.520	
 Long sight lines	0.032	0.038	0.398	
  % first − floor windows	0.341**	0.117	0.004	
  % of active uses	0.182*	0.093	0.049	
 Spatial filtering eigenvector	
  Fit (ME)	 − 1.723**	0.577	0.003	
  Fit (ME)	1.602*	0.679	0.018	
  Fit (ME)	1.318*	0.585	0.024	
  Fit (ME)	0.911a	0.517	0.078	
  Fit (ME)	 − 0.869a	0.506	0.086	
N	892	
2 × log − likelihood(df)	 − 3517.54 (845)	
AIC	3614	
***p < 001, **p < 01, *p < 0.05, aP < 0.1. Significant values are in bold.

For the results of the macro-level environmental elements, first, the overall Walk Score was significantly related to PV in the two types of models. Second, the significance of land-use factors strongly associated with PV in the first model type was higher than that in the second type. The proportions of teaching buildings and libraries’ land, stores and canteens’ land, and residential land were significantly correlated with PV in the two types of models. The entropy was significantly related to PV at P < 0.001 in Models 1 and 2. In contrast, the entropy correlated with PV at P < 0.1 in the second model type. Third, only the PRD was significantly associated with the PV in all models, but the correlation was negative. Because the PRD measures the detour from the origin to the destination, larger values indicate longer detours and less efficient walking, thus reducing the walking intensity on the street. Data analysis validates this explanation. Finally, the spatial configuration of betweenness and efficiency was significantly and positively correlated with PV, while the severance was significantly and negatively linked to PV. However, these significant relationships were only reflected in the first model type.

For the results of the micro-level design qualities and streetscape features, the overall and classified environmental design quality significantly correlated with PV, indicating the prominence of street quality elements in the relationship between CBEs and walking activities. Specifically, only enclosures and PV were negatively correlated, indicating that overly closed campus streets cannot promote street walking activities. Among the 21 streetscape features in Model 3, only the number of basic building colors was negatively associated with the PV. The significance is at P < 0.1. Twelve features, namely number of buildings, non-rectangular buildings, accent building colors, sidewalk length, sidewalk quality, noise level, street furniture, street trees, public art, landscape features, the proportion of first-floor windows, and the proportion of active uses, were significantly correlated with PV at P < 0.05. Comparatively, building height was only related to PV at P < 0.1.

Discussion

This study optimized the urban built environment measurement system with the unique campus environment based on an interdisciplinary perspective. The study selected comprehensive CBE features from a multi-dimensional perspective incorporating the macro- and micro-level environmental variables. Eight Chinese campuses in Tianjin were chosen to systematically explore the correlation of CBE features with PV. We disclosed and refined the categories of features significantly correlated with PV using several negative binomial regression models. Based on the study findings, several major issues are discussed as follows.

First, we discussed the macro-level environmental features significantly correlated with PV in the two types of models. We found that destination accessibility of the overall campus Walk Score was positively related to PV. Additionally, the land use features of facility land ratio, residential land ratio, and street connectivity attributes of the PRD were closely associated with PV. It means that the campus land use pattern improves by mixing residential and commercial land uses, increasing the allocation of facilities closely related to students’ lives, such as canteens, restaurants, and stores, providing direct pedestrian paths between facilities as far as possible, and offering various route options to increase the connectivity and efficiency of the campus walking network. These findings are similar to other urban research results. Park et al. realized that the Walk Score can significantly promote PV24. Duncan and Twardzik also found that the Walk Score is vital for improving walking activities56,57. Lee et al. observed that the proportion of facility land promotes PV58. Although we found that residential land proportion and PRD are strongly correlated with PV, these two factors were not significant in existing urban research. Because students travel to and from the dormitory more frequently every day, and the layout modes of the library, administrative building, public teaching building, and other service facilities are relatively fixed and unified, the two indicators related to the distribution of facilities and PV are significantly correlated. However, urban land is a relatively complex factor, and commercial facilities and residential buildings are often mixed. Residents often leave home for work during working days, resulting in weak walking activity intensity in the streets around the residential area during daytime working hours. Therefore, it is difficult for such factors to form a relatively consistent result on the impact of PV.

Second, we found that six environmental design qualities were closely associated with PV, while enclosure was only negatively linked to walking activity. This finding has both similarities and differences with those of previous studies. Current studies based on the UDQ theory have found that although different qualities are significantly correlated with PV, the significance is mainly reflected in transparency20,22,24,28. Existing urban research has yet to find a significant relationship between the human scale and PV. However, the present study found that the human scale is significantly associated with PV at the highest level of P < 0.001. Unlike other current studies20,22,24,28, this study found that all design qualities were significantly correlated with campus PV. These differences show that campuses have better applicability to the UDQ theory.

Third, we found that non-rectangular buildings, street amenities, the proportion of first-floor windows, the proportion of active uses, and other features were positively correlated with PV24,51. In contrast, basic building colors were negatively correlated with PV. These findings are consistent with the results of a few current studies. Two studies indicated that the proportion of windows on the first-floor of a building and the proportion of active uses were positively associated with PV24,51. Ewing et al. and Park et al. observed that the number of street furniture items positively correlated with PV24,51. Simultaneously, Park et al. found that basic building colors significantly inhibited PV24. Sung et al. and Kang have observed that street furniture significantly promoted PV in Seoul1,59. The present study found that the number of buildings and noise level were closely related to PV, unlike existing studies that indicated a weak or no correlation with walking24,51. This difference is due to the high density of buildings on both sides of the streets, especially in the street environment in the city center. Regardless of the PV value, the spectrum of building density is low with a high number of buildings on the street; no significant correlation was observed between PV and the number of buildings on the street. Campus buildings, including libraries, canteens, and public teaching buildings, are low in density and are primarily pedestrian destinations on the street. Therefore, the density of campus buildings increased street pedestrian activity. However, the noise level is remarkably related to PV because most Chinese campuses have closed boundaries, and students’ travel modes are mainly on foot. Compared to the noise generated by the immense urban motor vehicle traffic and horn honking, the sound and noise sources on the campus’s internal environment are mainly from pedestrian walking or recreational activities on the streets and plazas. Because campus noise is much less than that in urban environments, the noise level on the streets is often positively correlated with the intensity of street walking activities. Additionally, street plazas can exclude cars and allow activities occurring in the plaza to attract passerby students to watch, play, or stimulate their willingness to participate, promoting the frequency and duration of street walking. Consequently, the campus noise level was positively correlated to PV.

Finally, from the regression results, active intervention strategies of campus planning and design to promote walking activities and walkability can be presented for campus form optimization from a macro-level perspective, and space quality improvement and streetscape feature construction from a micro-level perspective. The design strategy is based on CBEs that are significantly related to the PV (as shown in Table 9). Notably, the chosen macro-level environmental correlates of walking were significant in the two types of models. The following points are considered for campus form optimization. (1) Campus walk score: to improve facility accessibility. Specifically, the service facilities should be distributed in a multi-center mode, emphasizing the equal allocation of frequently used facilities such as stores and canteens on campus and decentralizing the facility layout according to the functional groups to reduce the resource wastage and walking distances caused by the centralized distribution of facilities; (2) Land use: to increase the proportion of facility and residential land. Particularly, it promotes campus land-use mixed patterns with a mixed layout of residential and commercial functions. Moreover, the campus’s surrounding areas should be densely connected to commercial land and streets with various facilities. (3) Street connectivity: to decrease walking detour distance. For instance, direct pedestrian routes should be provided between facilities wherever possible. Various route options should also be provided to enhance campus permeability and avoid continuous long block scales, ensuring that students can walk short distances to different services and facilities without decreasing their willingness to walk.Table 9 Distilled CBE features significantly correlated with PV.

CBE variables	CBE features significantly correlated with PV in both types of models	
Macro-level environmental elements	
 Campus Walk Score	
  Overall Walk Score	X	
 Land use	
  Entropy		
  Teaching buildings and libraries’ land ratio	X	
  Stores and restaurants’ land ratio	X	
  Residential land ratio	X	
 Street connectivity	
 4-way intersection ratio		
 PRD	X	
 Spatial configuration	
  Betweenness		
  Severence		
  Efficiency		
Micro-level design features	
 Overall design quality	X	
 Imageability	X	
 Enclosure	X	
 Human scale	X	
 Transparency	X	
 Complexity	X	
Micro-level streetscape features	
 Buildings with nonrectangular shapes	X	
 Building height		
 Buildings	X	
 Accent building colors	X	
 Basic building colors		
 Sidewalk length	X	
 Sidewalk quality	X	
 Number of lanes		
 Noise level	X	
 Street furniture	X	
 Street monitor		
 Street trees	X	
 Public art	X	
 Major landscape features	X	
 Courtyards, plazas, parks		
  % street wall (your side)		
  % street wall (opposite side)		
  % sky (your side)		
 Long sight lines		
  % first-floor windows	X	
  % of active uses	X	
X represents CBE features correlated with PV in the two types of models at P < 0.05.

The design quality can be improved by increasing the campus's imageability, human scale, transparency, and complexity while reducing street enclosures to create a walking-friendly travel environment for students. According to the components of different qualities (excluding the features eliminated in this study), imageability consists of the number of squares, buildings with identifiers, non-rectangular-shaped buildings, and major landscape features20. The last two categories of elements were significantly associated with the PV in this study; therefore, increasing these elements may promote imageability. Enclosure is negatively affected by sky proportion and long sight lines but positively influenced by street walls20. We should increase the major landscapes closely associated with PV to reduce street walls and increase sky proportions. The human scale consisted of the positive factors of street furniture, proportion of first-floor windows, and small planters20. This study found that the first two categories of elements were significantly correlated with PV. Therefore, adding street furniture and the proportion of first-floor windows should be focused to enhance the human scale. Transparency comprises the proportions of first-floor windows, active uses, and street walls (your side)20. We found that the first two categories of elements were significantly associated with PV. Accordingly, these two elements should be promoted to enhance transparency. Complexity comprises the number of buildings, building colors, and public art20. We found that the three features correlated with PV; hence, adding these physical features can promote complexity.

Notably, planners and decision-makers should diversify campus land use, increase the number of campus buildings, and balance the placement of public teaching buildings, stores, and other service facilities with public plazas and landscaped greenspaces on campus. Therefore, the accessibility of the campus facilities could be improved, and students can conveniently engage in physical or recreational activities while completing their university’s educational learning requirements. Streets should be equipped with various pieces of furniture, such as lamps, benches, trash cans, etc., and street trees and public artworks should also be increased. These micro-level streetscape features can provide pedestrians with an interesting and pleasurable walking experience and give students better access to the natural environment, thereby reducing their anxiety and depression symptoms and promoting their mental well-being. Moreover, the configuration of street-level physical features, for example, human-scaled tables and chairs and pleasantly scaled plazas and green spaces, can provide a suitable place and environment for students to engage in physical and social activities on the street, thus increasing their communications with friends and classmates and promote their social coherence and sense of campus belonging. By increasing the proportion of first-floor windows in buildings and their utilization rates, the visual connection between building occupants and walkers on the street is strengthened, and the frequency of use of the building is increased, thus stimulating more social and communicative activities.

Conclusion

To improve campus walkability and promote student walking activities, this study presents a conceptual framework for examining the association between CBEs and walking activity. Considering the campus environments' specificities and college students' traveling patterns, it comprehensively selects frequently used built environment variables by optimizing the Walk Score, UDQ measurement system, and other features, simultaneously modifying the measurement scale and boundary. We took eight campuses in Tianjin as an example to verify the conceptual framework and the technical approach through regression models' analysis. We found that most CBE features closely correlated with PV. Specifically, campus Walk Score, design qualities, and street amenity features are most significantly correlated with PV, whereas street connectivity, spatial configuration, and architectural design features are weakly associated with PV. This result illustrated that the optimized Walk Score and UDQ measurement system have high application in campus environments, and the conceptual framework's rationality was validated overall. Researchers can apply and optimize this study's framework and technical methods to measure campus environments in other regions' campuses. Moreover, the proposed framework can provide a platform by adding other environmental elements and comprehensively exploring the relationship between CBE features and different walking activities, health outcomes, and other parameters for constructing a sustainable and healthy campus. Meanwhile, based on these significant CBE correlates of walking, campus planning and renewal strategies for walking activity promotion are proposed. Campus planners can apply these design strategies to plan new campuses or update the established campus to create a pedestrian-friendly traveling environment that promotes students' walking activity and health conditions.

This study had the following limitations: (1) While campuses in Tianjin were chosen as the research sample, the campuses located in hot or cold climates and mountainous areas differ because of their natural and living environments, whether the derived outcomes apply to campuses in other regions and the linkage of CBE with walking activities is consistent needs further verification. (2) Although the PV of the selected streets was counted four times to avoid the data inaccuracy caused by a single occasional collection, the method and accuracy of measuring the PV need to be further improved. Due to limited resources, this study only considered weekday street walking activities. Although weekday walking activities could appropriately reflect the relationship between CBEs and PV on campus, future studies need to measure and analyze the correlation of CBEs with PV on weekends to discuss the different outcomes derived from weekends and weekdays. (3) Owing to the use of cross-sectional data, this study mainly explored the association between CBEs and PV and did not extensively analyze the influence mechanism of CBE on PV. Therefore, subsequent studies should combine longitudinal panel data to explore the CBE influence mechanism in depth.

Acknowledgements

This research was funded by Shandong Provincial Natural Science Foundation, China; grant number ZR2023QE328.

Author contributions

Zhehao Zhang: Conceptualization, Methodology, Writing-Original draft preparation; Tianyi Sun: Data curation, Software, investigation; Thomas Fisher: Validation, Writing-Reviewing and Editing; Haiming Wang: Visualization, Supervision, Writing-Reviewing and Editing.

Data availability

The datasets analyzed during the current study are not publicly available because they contain non-public data but are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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