Understanding urban leisure walking behavior: Correlations between neighborhood features and fitness tracking data

Özgün Balaban*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review


Leisure walking (including running) is the easiest way to combat the lack of physical activity of urban residents. Hence, understanding what drives people to take a leisurely walk is vital for the design and planning of healthy and vibrant neighborhoods. In this chapter, we investigate and identify which (urban) features can define the spatial distribution of leisure walk amounts, using a collection of more than 40,000 leisure walks collected from fitness tracking apps in Singapore. First, we conducted a spatial analysis of leisure walk data using a grid-based network of Singapore and examined its features, including land use mix, street typology, greenness, and the presence of facilities, such as bus stops and traffic lights, as well as the perception of urban qualities. The latter has been determined by analyzing millions of Google Street View (GSV) images along Singapore's street network, categorizing their content using automated deep learning algorithms. The findings can help planners and designers understand which features promote more leisure walks, and thus inform the design of healthier, more resident-friendly environments that promote walking and stimulate vibrancy.

Original languageEnglish
Title of host publicationArtificial Intelligence in Urban Planning and Design
Subtitle of host publicationTechnologies, Implementation, and Impacts
Number of pages17
ISBN (Electronic)9780128239414
ISBN (Print)9780128239421
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Fitness tracking apps
  • Informed urban planning
  • Leisure walks
  • Neural networks
  • Spatial regression


Dive into the research topics of 'Understanding urban leisure walking behavior: Correlations between neighborhood features and fitness tracking data'. Together they form a unique fingerprint.

Cite this