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The ever-growing development of sensor technology offers new opportunities to understand Environmental Stress Theory (Lazarus and Cohen, 1977). Through employing personalised environmental monitors, researchers have been able to measure individuals’ exposures to various kinds of environmental stressors: noise (Ueberham and Schlink, 2018), temperature (Ojha et al., 2019), light (Kanjo et al., 2018), air pollutants (Donaire-Gonzalez et al., 2019), wind and solar radiation (Shimazaki and Katsuta, 2019). The stressors in the current literature focus more on the physical environment, but little is known about the health effects of the built environment (Benita and Tunçer, 2019). However, built environment factors are thought to be closely related to physical activity and mental health; examples of such factors are greenery (Gubbels et al., 2016), walkability (Van Cauwenberg et al., 2016) and crowd density (Engelniederhammer et al., 2019). However, previous studies have not demonstrated how to objectively measure the association between the built environment and human emotions and health.
To assess the impact of the built environment on health, a priority is to measure the influence of street-level urban features. Previous studies have researched specific urban features. For example, urban trees that can provide shade (Nasution and Zahrah, 2014, Wolf et al., 2020) and sitting facilities that can encourage people to stay in public space (Gehl, 2013). Aside from that, other studies on the built environment have emphasised the quality of the built environment and type of amenities as important contributors to health promotion, investigating features such as walking paths, the presence or absence of nearby water and trees, lawns, birdlife, lighting, benches, facilities, playgrounds, and the type of surrounding roads and traffic (Holman et al.,1996; Giles-Corti et al., 2005).
Urban imagery is a widely used tool to represent the characteristics of the built environment. The history of using imagery in this context can be tracked to the last century (Lynch, 1960; Whyte, 1980), with Ewing and Handy (2009) being the first to utilise quantitative analysis of imagery, adopting video clips of streetscapes to measure urban design qualities (e.g., enclosure, human scale, and complexity). Furthermore, Thwaites et al. (2005) summarised urban features (e.g., sky exposure, façade continuity, visual complexity) from sequential photographs of streets taken at 25-metre intervals, laying a foundation for contemporary research on urban characteristics/qualities.
Recently, street imagery from Google Street View (GSV) enabled the consistent objective measuring of urban features on a city-scale (Li et al., 2015, Yin and Wang, 2016), and is becoming an effective tool to assess the built environment (Anguelov et al., 2010, Goel et al., 2018, Helbich et al., 2019). For example, previous studies have employed GSV to investigate pedestrian-level greenery (Hua et al., 2022), traffic safety (Cai et al., 2022), and sky and building view factors (Gong et al., 2018). GSV merits attention in the research on health and the built environment (Rzotkiewicz et al., 2018), but—with regard to health studies—some argue it still cannot take the place of in-person observations due to inconsistencies and inadequate resolution (Clews et al., 2016). Both researchers from the domains of public health and urban planning advocate for the personal level measurement of human responses to urban metrics (Bell et al., 2014, Chrisinger and King, 2018). As a solution to this issue, novel methods, such as tracking individuals by personalised sensors in urban environments needed to be explored.