Assessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston

Dong, Lin; Jiang, Hongchao; Li, Wenjing; Qiu, Bing; Wang, Hao; Qiu, Waishan

doi:10.1016/j.landurbplan.2023.104756

Cited by 52 publications

(30 citation statements)

References 111 publications

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“…The classification of environmental perception and environmental elements was primarily based on the part of speech of the words, while the categorization of factors mainly stems from the study of literature related to jogging. Environmental perception factors involve abstract spatial sensations, predominantly represented by adjectives, including safety [47,48], vibrancy [28], cleanliness [49], slope [25], width [15,50], accessibility [14,19], connectivity [28], air quality [22,51], scenic beauty [14,18], greenery [23,24], lighting [52,53], soundscape [28], and temperature [20,54]. Environmental elements factors primarily include concrete spatial entities, represented mostly by nouns, including vehicles [51,53], pedestrians [14,32], traffic infrastructure [28,55], landscape [14], green space [24,50], waterfront space [26,32], service facilities [14,15], lighting facilities [52,53], pavement [48,56], culture [57], buildings [15,58], and vertical elements [25].…”

Section: Text Analysismentioning

confidence: 99%

“…Recent studies have explored the relationship between the built environment and jogging [14][15][16][17][18]. Some environmental factors related to jogging have already been studied, such as accessibility [19], temperature [20], air quality [21,22], greenery [23,24], and slope [25].…”

Section: Introductionmentioning

confidence: 99%

“…Some environmental factors related to jogging have already been studied, such as accessibility [19], temperature [20], air quality [21,22], greenery [23,24], and slope [25]. However, these studies often concentrate on a single type of land use environment, such as streets [15], parks [26], or greenways [27], with limited analyses comparing different land use types. Given that different lands are managed by various sectors, this study can significantly enhance collaboration among different sectors [28] to promote the efficient and integrated use of land.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Differences in Jogging Support across Various Land Use Types in Urban Built-Up Areas Using User-Recommended Routes

Tan,

Jiang,

Guo

et al. 2024

Buildings

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Land use types other than specialized athletic fields provide a variety of jogging environments, addressing the shortage of urban fitness facilities and promoting urban health as well as sustainability. Currently, there is limited research comparing the differences in jogging support among various land use types, which can assist decision-makers in setting priorities and targeted strategies for urban renewal, especially in urban built-up areas with limited land resources. Initially, spatial information, statistical data, and recommendation reason text were extracted from recommended routes in mobile fitness apps and categorized into six land use types. Subsequently, spatial potential was measured through descriptive statistics, buffer area analysis, spatial autocorrelation analysis, and line density analysis. Environmental preferences were gauged by text analysis using jieba word segmentation and grouped word frequency calculation. Finally, the measurement results of different land uses were compared, including scale differences, spatial differentiation, environmental perception, and environmental elements. The research found that streets, residential areas, campuses, parks, and greenways possess significant potential to support jogging, particularly streets. These types of land use exhibit varying spatial potentials and attractions in environmental preferences. Targeted recommendations have been proposed to support the renewal of urban built-up areas and research in related fields.

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Section: Text Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparing Differences in Jogging Support across Various Land Use Types in Urban Built-Up Areas Using User-Recommended Routes

Tan,

Jiang,

Guo

et al. 2024

Buildings

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“…Among them, a significant subset (280+ papers) has extensively utilized Street View Imagery (SVI) data [9] and artificial intelligence (AI) models, including machine learning (ML), deep learning (DL), and computer vision (CV) for urban-scale visual auditing [10][11][12]. Emerging studies [13][14][15][16] have indicated that street scene qualities significantly affect human behaviors, including running [17], walking [18], mental health [19][20][21], leisure activities [22], job and housing decisions [23,24], crime [25], and carbon emissions [26].…”

Section: Introduction 1public Space and Safety Perceptionmentioning

confidence: 99%

Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI

Liu,

Li,

Ren

et al. 2024

J. Imaging

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A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to the lack of night-time urban appearance data, prior studies based on street view imagery (SVI) rarely addressed the perceived night-time safety issue, which can generate important implications for crime prevention. This study hypothesizes that night-time SVI can be effectively generated from widely existing daytime SVIs using generative AI (GenAI). To test the hypothesis, this study first collects pairwise day-and-night SVIs across four cities diverged in urban landscapes to construct a comprehensive day-and-night SVI dataset. It then trains and validates a day-to-night (D2N) model with fine-tuned brightness adjustment, effectively transforming daytime SVIs to nighttime ones for distinct urban forms tailored for urban scene perception studies. Our findings indicate that: (1) the performance of D2N transformation varies significantly by urban-scape variations related to urban density; (2) the proportion of building and sky views are important determinants of transformation accuracy; (3) within prevailed models, CycleGAN maintains the consistency of D2N scene conversion, but requires abundant data. Pix2Pix achieves considerable accuracy when pairwise day–and–night-night SVIs are available and are sensitive to data quality. StableDiffusion yields high-quality images with expensive training costs. Therefore, CycleGAN is most effective in balancing the accuracy, data requirement, and cost. This study contributes to urban scene studies by constructing a first-of-its-kind D2N dataset consisting of pairwise day-and-night SVIs across various urban forms. The D2N generator will provide a cornerstone for future urban studies that heavily utilize SVIs to audit urban environments.

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“…Built environment characteristics influence pedestrians' decision making through pedestrians' perception of the walking environment [15][16][17][18]. Pedestrians perceive the walking environment through various factors, including attractiveness, safety, and security [19,20].…”

Section: Introductionmentioning

confidence: 99%

Influence of the Built Environment on Pedestrians’ Route Choice in Leisure Walking

Ge,

He,

Shang

2023

IJGI

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Exploring the relationship between leisure walking and the built environment will provide an improvement in human health and well-being. It is, therefore, necessary to explore the most relevant scale for leisure walking and how the association between the built environment and leisure walking varies across scales. Three hundred volunteers were recruited to wear GPS loggers, and a total dataset of 268 tracks from 105 individuals was collected. The shortest possible routes between starting and ending points were generated and compared to the actual routes using the paired T-test. An improved grid-based buffer approach was proposed, and statistics for the grid cells intersecting the paths were calculated. Grid cells were calculated for six scales: 50 m, 100 m, 200 m, 500 m, 800 m, and 1600 m. The results showed that the actual paths were on average 24.97% longer than the shortest path. The mean, standard deviation, and minimum and maximum values of the built environment variables were all significantly associated with leisure walking. The most relevant spatial scale was found to be the 100 m scale. Overall, the smaller the scale, the more significant the association. Participants showed a preference for moderately compact urban forms, diverse options for destinations, and greener landscapes in leisure walking route choice.

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Assessing impacts of objective features and subjective perceptions of street environment on running amount: A case study of Boston

Cited by 52 publications

References 111 publications

Comparing Differences in Jogging Support across Various Land Use Types in Urban Built-Up Areas Using User-Recommended Routes

Comparing Differences in Jogging Support across Various Land Use Types in Urban Built-Up Areas Using User-Recommended Routes

Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI

Influence of the Built Environment on Pedestrians’ Route Choice in Leisure Walking

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