Assessing spatiotemporal bikeability using multi-source geospatial big data: A case study of Xiamen, China

Shaoqing Dai, Wufan Zhao*, Yanwen Wang, Xiao Huang, Zhidong Chen, Jinghan Lei, Alfred Stein, Peng Jia

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

4 Citations (Scopus)
23 Downloads (Pure)


This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing open-source data, advanced deep neural networks, and GIS spatial analysis, the framework eliminates subjective evaluations and is more efficient and comprehensive than prior methods. The experimental results on Xiamen, China, demonstrate the effectiveness of the framework in identifying areas for improvement and enhancing cycling mobility. The proposed framework provides a structured approach for evaluating bikeability in different geographical contexts, making reproducing bikeability indices easier and more comprehensive to policymakers, transportation planners, and environmental decision-makers.

Original languageEnglish
Article number103539
JournalInternational Journal of Applied Earth Observation and Geoinformation
Publication statusPublished - Dec 2023


  • Bike-sharing
  • Bikeability
  • Built environment
  • Multi-source data
  • Spatialtemporal


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