Analyze the usage of urban greenways through social media images and computer vision

Yang Song, Huan Ning, Xinyue Ye, Divya Chandana, Shaohua Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Urban greenway is an emerging form of urban landscape offering multifaceted benefits to public health, economy, and ecology. However, the usage and user experiences of greenways are often challenging to measure because it is costly to survey such large areas. Based on the online postings from Instagram in 2017, this paper used Computer Vision (CV) technology to analyze and compare how the general public uses two typical greenway parks, The High Line in New York City and the Atlanta Beltline in Atlanta. Face and object detection analysis were conducted to infer user composition, activities, and key experiences. We presented the temporal patterns of Instagram postings as well as the group gatherings, smiling, and representative objects detected from photos. Our results have shown high user engagement levels for both parks while teens are significantly underrepresented. The High Line had more group activities and was more active during weekdays than the Atlanta Beltline. Stronger sense of escape and physical activities can be found in Atlanta Beltline. In summary, social media images like Instagram can provide strong empirical evidence for urban greenway usage when combined with artificial intelligence technologies, which can support the future practice of landscape architecture and urban design.

Original languageEnglish (US)
JournalEnvironment and Planning B: Urban Analytics and City Science
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Architecture
  • Geography, Planning and Development
  • Urban Studies
  • Nature and Landscape Conservation
  • Management, Monitoring, Policy and Law

Keywords

  • computer vision
  • park usage
  • place quality
  • social media
  • Urban greenway

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