Page 79 - JCAU-7-1

Journal of Chinese
Architecture and Urbanism Machine-simulated scoring of child-friendly streets

psychological well-being (Li, 2016), particularly in low- method. Finally, it discusses general conclusions and
income neighborhoods (Hou et al., 2022). recommendations for further research.
Urban planning and design guidelines for child-friendly 2. Child-friendly streets and emerging
streets emphasize the necessity for safe and healthy mobility research methods
options, ensuring secure infrastructure, equitable access
to city services, and promoting well-being for children 2.1. Child-friendly streets
and their caregivers (Nikku & Pokhrel, 2013; Sapsağlam Streets serve as crucial conduits for children’s travel and
& Eryılmaz, 2024). Key elements include continuous and play a key role in facilitating child-friendly mobility. Child-
accessible pedestrian infrastructure (Peng, 2020; Rini, friendly streets not only provide safe and comfortable
2019), safe cycling and transit facilities (Kingston et al., environments for children’s journeys but also contribute
2007; Smith et al., 2022), green spaces (Vidal & Castro positively to their physical development, mental well-being,
Seixas, 2022; Yuniastuti & Hasibuan, 2019), and adequate and social interactions (Cheng et al., 2024). By examining
natural daylight (Ding et al., 2023; Jahromi, 2020). In how children navigate urban areas, researchers can gain
addition, a visually stimulating environment can stimulate valuable insights into how streets influence children’s safety,
the development of cognitive skills in children (Berk, 2015; development, and independence (McMillan, 2005). Brown
Bornstein, 1985), especially during their peak growth et al. (2019) emphasize that prioritizing active travel and
and learning periods (Read et al., 1999). For comfort and separating motor vehicles from streets and public spaces
convenience, streets should be well-connected, providing a can enhance children’s safety.
diverse array of pedestrian pathways (Burden et al., 1999),
reliable public transport (Bertolini, 2020), clear signage Street design guidelines are widely available across
and schedules (Bain et al., 2012), climate-appropriate regions and countries; however, they often prioritize
shade and shelter (Wheeler et al., 2019), and facilities such traffic over the needs and travel patterns of children
as restrooms and drinking fountains (NYC, 2010). (Harirchian et al., 2018). Despite regional variations,
children encounter various travel-related risks, such as
Urban scholars have long been assessing urban street road crossings, nighttime travel, and unaccompanied use
environments, particularly in understanding how the of public transportation (Shaw et al., 2015), which typically
physical environment and its visual impact shape observers’ require adult supervision or a companion (Mehtap,
experiences (Ewing & Handy, 2009; Lynch, 1964; Nasar, 2016). Johansson (2006) found that children’s outdoor
1990; Sanoff, 2016; Zhang et al., 2018). These studies have activities are influenced by individual factors, such as
primarily relied on traditional data collection methods parents’ perception of the quality of traffic environments,
such as interviews and questionnaires (Montello et al., sidewalks, and bike lanes. Due to their young age, children
2017; Raimbault et al., 2003; Sholihah & Heath, 2016), often travel with others (McDonald, 2006), such as their
which require substantial manpower, costs, and time. caregivers. Villanueva et al. (2013) posited that street
Such methods limit research scalability, often constraining accessibility and safety are associated with children’s ability
studies to small geographic areas (Zhang et al., 2018). to travel independently.
Previous street studies have predominantly focused on
adults’ experiential aspects, with minimal attention given 2.2. Emerging research methods using computer
to the comfort and safety of children, such as their ability vision
to travel alone. Torres (2020) has argued that many cities Visual observations provide one of the most intuitive
prioritize traffic and parking in street planning, reducing ways for urban residents, including children, to perceive
the walkability and sociability of street environments and their surroundings (Ulrich, 1979). The availability of
adversely affecting local communities.
online data resources, such as Google Street View, has
This study explores the application of machine learning introduced new opportunities for analyzing urban
predictive models in assessing the comfort and safety perceptions through geo-tagged imagery. Street-level
of children on urban streets. A street view recognition image processing now allows for the extraction of spatial
approach was to identify potential barriers to children’s features and quantifiable data, employing machine
safe and free movement and to highlight locations for learning protocols to process extensive datasets rapidly
improvement. The rest of this article is divided into four and assess city blocks on a large scale (He & Li, 2021).
sections. First, it introduces the background of using Map service providers such as Google Maps facilitate
machine learning methods for street analysis. Second, it automated workflows connected to their Application
presents the case study project and technical workflow. Programming Interfaces (APIs), enabling researchers to
Third, it evaluates the outcomes of the deep-learning systematically gather imagery across thousands of cities

Volume 7 Issue 1 (2025) 2 https://doi.org/10.36922/jcau.3578