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

Minglu Qin, Haibin Xu and Jiantuan Huang ()
Additional contact information
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

Sustainability, 2024, vol. 16, issue 5, 1-14

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.

Keywords: streetscape; land surface temperature; cycling behavior; GeoDetector (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/5/1990/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/5/1990/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:5:p:1990-:d:1347768

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().