Investigating the association between the built environment and active travel of young adults using location based technology

Physical inactivity is the second leading modifiable risk of chronic disease. Habitual inactivity prevents young adults from developing healthy patterns of physical activity during the important transition from adolescence to adulthood. Consistent levels of inactivity thus pose a critical, life-long health risk. One promising approach to promoting physical activity is to embed physical activity in daily life by promoting active travel as ways to commute and recreate. A growing body of evidence indicates that certain environmental characteristics promote active lifestyles in general or promote specific kinds of physical activity such as walking or biking. However, studies have also reported inconsistent and sometimes contradictory results of the influence of environment on active travel. These inconsistencies and contradictions stem partly from the challenges of collecting valid data regarding the environmental features and of the locations where people actually travel. The inconsistent results may also stem from the discrepancy between the measured environment and the perceived environment. These challenges prevent us from understanding the strength of the association between environmental features and travel behaviors, and thereby limit the potential to use research results to guide evidenced-based urban design and planning. This dissertation explores and reduces the research gaps regarding these sets of challenges. First, to better measure an individual’s travel behavior in a convenient and cost-effective manner, I tested the applicability of using a smartphone application that I developed to simultaneously collect location, time, and accelerometer data, and developed a method to automatically classify these data into different travel modes. Second, to overcome the Uncertain Geographic Context Problem (UGCP), I measured the built environmental features at the places where active travel occurred. Then, I modeled the active travel behaviors based on the environmental features using mixed logistic regression. Third, to complement the statistical models, and to reveal people’s own perspectives about the characteristics of the environment that support active travel, I examined geo-tagged photo narrative that the participants provided. These photo narratives reveal information that grows directly from the users of the environment about the specific features of the built environment. Results from the smartphone data classification demonstrated that smartphone devices are capable of capturing data that reveal how, where, and when people travel. The classification system used in this study achieved more than 80% accuracy in the detection of the type of travel mode people took. Results from the statistical analysis of the relationship between environment and travel behavior showed that greenness was consistently and positively associated with more recreational active travel than vehicle travel in both cities. Destinations in general showed a positive relationship with utilitarian active travel behavior. Crime did not show a significant relationship with different modes of active travel. I also found that a variety of design features such as aesthetics, functionality, destination, and safety were associated with an active lifestyle. In this study, I employed interdisciplinary methods from geography, public health, and urban planning. I attempted to integrate realms of urban planning and public health by using innovative technologies such as GIS and smartphone sensors to examine travel behaviors. This project also reached to the scale of each individual and probed their concerns on environmental characteristics to promote active travel behavior. I hope the results of this study will help to design urban environment with more active living features. I also hope this study will contribute to combat the obesity and physical inactivity problems that plague cities and make cities more livable and people healthier.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Landscape Architecture

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Zhou, Xiaolu

Contributors dc:contributor

• Sullivan, William C.
• Feng, Chen-Chieh
• Deal, Brian M.
• Zhu, Weimo

• Physical Activity
• Geographic Information System (GIS)
• Travel Behavior
• Singapore
• Chicago

Statement dc:rights

• Copyright 2014 Xiaolu Zhou

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/50453

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/50453