Jogging, historically marginalized in the realms of urban and transportation strategy since it is not about commuting, is garnering appreciation for its health-related merits. The growing public focus on health underscores the urgent need for planning and infrastructure to support outdoor physical activities, yet current evaluations of urban environments' friendliness toward such activities are insufficient. This investigation unveils a runnability evaluation framework predicated on accessible geospatial big data. Initial steps involved delineating potential metrics from the built environment, pedestrian perceptions, and the natural setting, as informed by literature. This was followed by constructing a backward stepwise regression analysis, utilizing jogging frequency as the response variable against the identified metrics as predictors. The ensuing model retained certain variables, which were then deemed valid metrics, and their regression coefficients were appropriated as weights to compute a runnability index for individual street segments. This framework was applied in Guangzhou, affirming the model's objectivity and validity. The introduced framework furnishes researchers and urban planners with an objective and reproducible tool for the evaluation of runnability and possesses the versatility for an extension to assess walkability and bikeability. This study encourages the attention and support of jogging activities.

中文翻译：

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打造慢跑友好型城市：利用大数据评估城市街道的运行性能


慢跑在历史上在城市和交通战略领域被边缘化，因为它与通勤无关，但因其与健康相关的优点而受到赞赏。公众对健康的日益关注凸显了对规划和基础设施的迫切需求，以支持户外体育活动，但目前对城市环境对此类活动的友好程度的评估并不充分。这项调查揭示了一个基于可访问地理空间大数据的可运行性评估框架。最初的步骤包括根据文献从建筑环境、行人感知和自然环境中描绘潜在指标。然后构建一个向后逐步回归分析，利用慢跑频率作为响应变量，针对已识别的指标作为预测因子。随后的模型保留了某些变量，这些变量随后被视为有效指标，并且它们的回归系数被用作权重，以计算各个街段的运行性能指数。该框架在广州得到应用，肯定了该模型的客观性和有效性。引入的框架为研究人员和城市规划者提供了一个客观且可重复的工具，用于评估可运行性，并具有扩展以评估步行性和可骑行性的多功能性。本研究鼓励关注和支持慢跑活动。