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Investigating the Impact of Streetscape and Land Surface Temperature on Cycling Behavior

Author

Listed:
  • Minglu Qin

    (College of Physical Education and Health Science, Guangxi Minzu University, Nanning 530008, China)

  • Haibin Xu

    (School of Recreational Sports and Tourism, Beijing Sport University, Beijing 100084, China)

  • Jiantuan Huang

    (College of Physical Education and Health Science, Guangxi Minzu University, Nanning 530008, China)

Abstract

Cycling is a flexible way of traveling that can promote the development of urban public transportation. Previous studies on the influence of cycling have focused more on the cyclists themselves, ignoring the influences of the features of natural environments, such as streetscapes and land surface temperatures (LSTs), on cycling behavior. Therefore, in this study, street view image data and Landsat 8 imagery were utilized to extract streetscape and LST features; in particular, a framework was established for a single-indicator analysis and a multiple-indicator interaction analysis based on the random forest model with GeoDetector. The model was used to explore the effects of streetscapes and surface temperatures on cycling behavior. The results of this study for the main urban area of Beijing show that (1) high-density buildings and high population activity exacerbated the heat island effect at the city center and certain areas in the east, with the highest LST reaching 46.93 °C. In contrast, the greenery and water bodies in the northwestern and northeastern areas reduced the LST, resulting in a minimum temperature of 11.61 °C. (2) The optimal analysis scale was a 100 m buffer pair, and the regression fitting accuracy reached 0.83, confirming the notable influences of streetscape and LST characteristics on cycling behavior. (3) The random forest (RF) model results show that the importance of LST features and vegetation and sky conditions exceeded 0.07, and a reasonable sky openness and open building ventilation became the first choices for promoting cycling behavior. (4) According to the GeoDetector model, the LST features alone exhibited an importance of more than 0.375 for cycling behavior, while interactions with streetscapes greatly reduced the negative effect of LST on cycling behavior. The interaction between walls and plants reached 0.392, while the interaction between multiple environmental factors and greenery and favorable ventilation counteracted the negative impact of high-temperature heat waves on the residents’ choice of bicycles.

Suggested Citation

  • Minglu Qin & Haibin Xu & Jiantuan Huang, 2024. "Investigating the Impact of Streetscape and Land Surface Temperature on Cycling Behavior," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1990-:d:1347768
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    References listed on IDEAS

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