To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults
Introduction
The aging of the population, as a result of rising life expectancy, a decreasing fertility rate, and the aging of baby boomers (born between 1946 and 1964), is profoundly shaping a wide variety of regions around the world (e.g., Japan and Hong Kong). The absolute number and percentage of older people worldwide are ballooning. In 2019, there were 700 million older adults (defined as people aged ≥65) worldwide. By 2050, this number will nearly double to 1.3 billion. Additionally, the percentage of older people was 9.1% in 2019 and is predicted to climb to 15.9% in 2050 (United Nations, 2019). Similarly, Hong Kong is facing the issue of population aging and dramatic and unprecedented changes in its demographic profile. Second only to Japan (28.4% in 2020, topping the global list) in the Asian context, Hong Kong has a high percentage of older people (18.2% in 2020). The percentage of its older people is predicted to grow to 34.7% in 2050 (United Nations, 2019).
Older people's mobility has been widely documented to be closely related to their participation in activities, social integration, physical and psychological health, and life satisfaction (Alsnih and Hensher, 2003; Chen et al., 2021, Metz, 2000). Their mobility is a determinant of quality of life and subjective wellbeing (Banister and Bowling, 2004). For older adults, an insufficient level of mobility leads to difficulty in participating/engaging in social activities and interacting with the wide community and, consequently, low morale, depression, and loneliness (Wong et al., 2018).
Walking is a form of active mobility (low-intensity physical activity) for residents and a travel mode with considerable economic, environmental, social, safety, and health advantages (Frank et al., 2007; Heath et al., 2012; Sælensminde, 2004; Wong et al., 2021). Walking has thus received much attention from governments, non-government organizations, and researchers. It has been advocated and promoted universally. As Cervero and Kockelman (1997) state, a paramount transportation objective is diverting travel demand from motorized to active modes such as walking. Moreover, walking is a critical travel mode for older adults, who generally have limited access to cars (Hu et al., 2013; Yang, 2018), in many locations (e.g., Chinese cities) (Cheng et al., 2019b, Liu et al., 2021b; van Wee and Handy, 2016). This is particularly true for Hong Kong. Owing to its mixed land use, pedestrian-friendly urban design, and high walkability, Hong Kongers have a predilection for and habit of walking, as evidenced by Hong Kongers taking the highest number of walking steps all over the world (Althoff et al., 2017).
Urban greenery may facilitate physical activity (due in part to humankind's biophilia nature) and alter people's walking behavior (Lu et al., 2018). Traditionally, its evaluation heavily relies on in-person assessments, field observations, aerial photography, and remote sensing imagery. However, these methods have limitations, such as high labor intensity, a restriction to small areas, a restriction to a bird's eye (overhead) view, and the inability to represent the human scale (Kang et al., 2020). Fortunately, owing to the availability of street-view imagery data and the rapid development of urban analytics, streetscape greenery has garnered increasing attention from researchers (especially those in the public health field) in recent years. We can now efficiently and accurately estimate streetscape greenery from street-view imagery (Kang et al., 2020).
Existing literature has extensively emphasized the effects of socio-demographic characteristics and built-environment attributes (e.g., population density and street connectivity) on the travel behavior of older adults. However, few studies have evaluated the role of streetscape greenery (Yang et al., 2020; Yang et al., 2019; Zang et al., 2020). More importantly, most, though not all, of these studies have assumed a pre-determined (often linear) relationship between travel behavior and its contributory factors. Nevertheless, the connection between the built environment and the travel behavior of older people may be non-linear (Ding et al., 2019, Liu et al., 2021a; van Wee and Handy, 2016). This non-linearity can be explained by the peer effect (or collective socialization) and travel utility (Cheng et al., 2020a; Galster, 2018; Mokhtarian and Salomon, 2001). In recent years, pioneering research has focused on the issue of non-linearity (Cheng et al., 2020a; Ding et al., 2018b; Ding et al., 2018c; Zhang et al., 2020). However, to our best knowledge, only one study (Cheng et al., 2020a) concentrates on the non-linearity of the connection between the built environment and the travel behavior of older people. No studies on the effect of streetscape greenery on travel behavior have considered the issue of non-linearity. As ignoring a genuinely non-linear effect may result in misunderstandings and erroneous practical implications (Cheng et al., 2020a), delving deeper into the non-linearity issue is crucial.
To address the above issues, we use data from the Hong Kong Travel Characteristic Survey (TCS) 2011 and Google Street View (GSV) imagery (detailed in Section 3) to assess the walking propensity (or propensity of walk trip-making, propensity to walk) of older people and streetscape greenery, respectively. A random forest model is adopted to evaluate the non-linear effects of streetscape greenery on walking propensity. A binary logistic regression model is generated to make a comparison with the random forest model. Notably, socio-demographic and built-environment attributes (measured by TCS 2011 data or geo-data) are controlled. The contributions of this study are (1) the examination of the connection between streetscape greenery and older people's walking propensity; (2) the scrutinization of the non-linear and threshold effects of streetscape greenery on travel behavior for the first time; and (3) the assessment of the non-linear and threshold effects of built-environment attributes on older people's walking behavior.
The remainder of this paper is organized as follows. The ensuing section reviews the literature on the contributory factors of the walking behavior of older adults. Sections 3 introduces the data and methodology of random forest modeling. Section 4 presents the results of random forest modeling and compares them with the outcomes of logit modeling. Section 5 winds up the paper, discusses theoretical and practical implications, and summarizes research limitations.
Section snippets
Literature review
A voluminous body of the existing literature has confirmed the effects of the built environment on the walking behavior of older people. We conducted electronic searches in the database of Web of Science and retrieved pertinent articles published between 2006 and 2020. We first screened the papers by title and then by abstracts. We considered their relevance to this study and manually chose papers for detailed reviews.
Table 1 summarizes selected studies on the link between the built environment
TCS 2011 data
TCS 2011 is a wide-ranging and comprehensive travel survey that was conducted by the Transport Department of the Hong Kong government during September 2011 and January 2012. The TCS is conducted roughly every ten years. It includes three main surveys, namely household interview survey (for collecting the trip information of Hong Kong residents), stated preference survey (for identifying the contributory factors of transport mode choices for users), and hotel/guesthouse tourists survey (for
Relative importance of predictor variables
Table 3 presents the random forest results and shows the relative importance of predictor variables. Fig. 3 graphically describes the relative importance. The variable of primary interest, namely Streetscape greenery, has the second-highest relative importance (12.82%), exceeded only by Age (16.65%). This outcome supports the critical role of streetscape greenery in determining the walking propensity of older adults. Furthermore, the other “5Ds” built-environment variables closely follow
Conclusions and discussions
The aging of the population has become a popular phenomenon in many cities worldwide (Jing et al., 2021), such as Hong Kong. Given that walking is a prevalent travel mode for older adults and has multiple health and wellbeing benefits, determining the correlates of the walking behavior of older adults is of paramount importance. Furthermore, as an easily perceived built-environment attribute, streetscape greenery has seldom been investigated in the travel behavior literature of older adults but
Acknowledgments
This research was supported by the Fundamental Research Funds for the Central Universities of China (No. 2682021CX097), Education and Scientific Research Grant of Sichuan Province (No. SCJG20A110), and Talent Cultivation Quality and Teaching Reform Project of Ideological and Political Theory Course of Chengdu University of Technology in 2020 (20800-2020SZ009). The authors are grateful to the reviewers for their constructive comments.
References (66)
- et al.
The mobility and accessibility expectations of seniors in an aging population
Transp. Res. A Policy Pract.
(2003) - et al.
Quality of life for the elderly: the transport dimension
Transp. Policy
(2004) - et al.
Implementing on-site construction waste recycling in Hong Kong: barriers and facilitators
Sci. Total Environ.
(2020) - et al.
Age and retirement status differences in associations between the built environment and active travel behaviour
J. Transp. Health
(2016) - et al.
Travel demand and the 3Ds: density, diversity, and design
Transp. Res. Part D: Transp. Environ.
(1997) - et al.
Measuring accessibility to health care services for older bus passengers: a finer spatial resolution
J. Transp. Geogr.
(2021) - et al.
Applying a random forest method approach to model travel mode choice behavior
Travel Behav. Soc.
(2019) - et al.
Active travel for active ageing in China: the role of built environment
J. Transp. Geogr.
(2019) - et al.
Examining non-linear built environment effects on elderly’s walking: a random forest approach
Transp. Res. Part D: Transp. Environ.
(2020) - et al.
Synergistic effects of the built environment and commuting programs on commute mode choice
Transp. Res. A Policy Pract.
(2018)
Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo
Transp. Res. A Policy Pract.
Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: a machine learning approach
Accid. Anal. Prev.
How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds
J. Transp. Geogr.
Stepping towards causation: do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity?
Soc. Sci. Med.
Do differences in built environments explain age differences in transport walking across neighbourhoods?
J. Transp. Health
Evidence-based intervention in physical activity: lessons from around the world
Lancet
Polycentric urban form and non-work travel in Singapore: a focus on seniors
Transp. Res. Part D: Transp. Environ.
The associations between older adults’ daily travel pattern and objective and perceived built environment: a study of three neighbourhoods in Singapore
Transp. Policy
Understanding the travel behavior of elderly people in the developing country: a case study of Changchun, China
Procedia Soc. Behav. Sci.
Mobility of the elderly in densely populated neighbourhoods in Singapore
Sustain. Cities Soc.
Impact of physical and social environments on the walking behaviour of Hong Kong’s older adults
J. Transp. Health
Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach
J. Transp. Geogr.
Heterogeneity in physical activity participation of older adults: a latent class analysis
J. Transp. Geogr.
The effect of street-level greenery on walking behavior: evidence from Hong Kong
Soc. Sci. Med.
A dose–response effect between built environment characteristics and transport walking for youths
J. Transp. Health
Mobility of older people and their quality of life
Transp. Policy
How derived is the demand for travel? Some conceptual and measurement considerations
Transp. Res. A Policy Pract.
An investigation of the attributes of walkable environments from the perspective of seniors in Montreal
J. Transp. Geogr.
Mode use and trip length of seniors in Montreal
J. Transp. Geogr.
Cost–benefit analyses of walking and cycling track networks taking into account insecurity, health effects and external costs of motorized traffic
Transp. Res. A Policy Pract.
Exploring the non-linear associations between spatial attributes and walking distance to transit
J. Transp. Geogr.
Relationship between the physical environment and physical activity in older adults: a systematic review
Health Place
Key research themes on urban space, scale, and sustainable urban mobility
Int. J. Sustain. Transp.
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