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

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.