Exploring nonlinear effects of built environment on jogging behavior using random forest

Liu, Yong; Li, Y. M.; Yang, Wei; Hu, Jie

doi:10.1016/j.apgeog.2023.102990

Cited by 25 publications

(14 citation statements)

References 59 publications

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“…In addition to the traffic environment, green space, pavement, safety, and air quality are considered important by both experts and the public. This is consistent with existing research [21,27,56].…”

Section: Public Attention On Environmental Factors: An Overviewsupporting

confidence: 93%

“…In recent years, the impact of the street environment on jogging has increasingly gained attention. Studies have explored both linear [2,20] and nonlinear [21][22][23] correlations between various environmental factors and jogging behavior, including but not limited to traffic facilities [2,24], natural exposure [2,25], accessibility [12], and climate [26]. However, many environmental factors used in research are pre-selected based on expert experience and previous literature [27,28], potentially overlooking or neglecting factors that joggers focus on.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Street Environmental Factors That Attract Public Attention from the Jogger’s Perspective: A Multiscale Spatial Exploration

Zhong,

Guo,

Zhang

et al. 2024

Buildings

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Improving street jogging environments can promote urban health and sustainability, especially within urban built-up areas. Public attention to the environment during jogging has not been sufficiently studied, and the recommended route data from the fitness app offers an opportunity for multiscale spatial exploration. Initially, by analyzing the recommendation reason text, the study identified 22 environmental factors and proposed the Environmental Factor Public Attention Intensity (EFPAI) for quantification, with subsequent comparisons to expert opinions. Further, by analyzing five types of data from the recommended routes, the relationships between EFPAI and jogging frequency, route shape, spatial distribution, and seasonal variations were uncovered. The study revealed: (1) discrepancies between expert opinions and public attention, with experts more likely to overlook factors such as slope, visual quality, width, and acoustic quality; (2) the public tends to notice factors like pedestrians, green spaces, waterfront spaces, landscapes, culture, sports facilities, and accessibility on routes with high jogging frequency; (3) EFPAI for traffic infrastructure, waterfront spaces, landscapes, residential areas, and campuses is associated with route shape; (4) EFPAI of certain environmental factors is influenced by urban geographical spatial factors, and substantial differences exist between different cities. Specific recommendations were provided from three different scales—site scale, street block scale, and urban scale—to guide the resolution of issues in planning and decision-making processes for urban renewal.

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Section: Public Attention On Environmental Factors: An Overviewsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Identifying Street Environmental Factors That Attract Public Attention from the Jogger’s Perspective: A Multiscale Spatial Exploration

Zhong,

Guo,

Zhang

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Greenery, scenic beauty, accessibility, lighting, cleanliness, vehicles, green space, landscapes, and waterfront spaces garner less attention. These environmental factors, while not as prominently regarded as the previous category, still hold significant influence, reflecting findings from existing studies [14,16,19,24,52,56,62]. Most of these factors exhibited variations in performance across different land use types and need to be considered separately in the process of environmental improvement for each land use type.…”

Section: Environmental Preferencementioning

confidence: 66%

“…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%

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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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The artificial intelligence advantage: Supercharging exploratory data analysis

Oettl,

Oeding,

Feldt

et al. 2024

Knee surg. sports traumatol. arthrosc.

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Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data‐driven decisions. Tree‐based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real‐world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data‐driven decision‐making across various scientific domains.Level of Evidence: Level V ‐ Expert opinion.

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Exploring nonlinear effects of built environment on jogging behavior using random forest

Cited by 25 publications

References 59 publications

Identifying Street Environmental Factors That Attract Public Attention from the Jogger’s Perspective: A Multiscale Spatial Exploration

Identifying Street Environmental Factors That Attract Public Attention from the Jogger’s Perspective: A Multiscale Spatial Exploration

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

The artificial intelligence advantage: Supercharging exploratory data analysis

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