309 Journal of Physical Activity and Health, 2013, 10, 309-322 © 2013 Human Kinetics, Inc. Zhu, McAuley, and Chodzko-Zajko are with the Dept of Kinesiology and Community Health, University of Illinois at Urbana-Champaign. Nedovic-Budic is with the Dept of Geog- raphy, Planning, and Environmental Policy, University College Dublin, Belield, Ireland. Olshansky is with the Dept of Urban and Regional Planning, University of Illinois at Urbana–Cham- paign. Marti is with ARTIS, LLC, Salt Lake City, Utah. Gao is with the Dept of Kinesiology, Boise State University, Boise, ID. Park is with the Dept of Physical Education, Springield College, Springield, MA. Agent-Based Modeling of Physical Activity Behavior and Environmental Correlations: An Introduction and Illustration Weimo Zhu, Zorica Nedovic-Budic, Robert B. Olshansky, Jed Marti, Yong Gao, Youngsik Park, Edward McAuley, and Wojciech Chodzko-Zajko Purpose: To introduce Agent-Based Model (ABM) to physical activity (PA) research and, using data from a study of neighborhood walkability and walking behavior, to illustrate parameters for an ABM of walking behavior. Method: The concept, brief history, mechanism, major components, key steps, advantages, and limitations of ABM were irst introduced. For illustration, 10 participants (age in years: mean = 68, SD = 8) were recruited from a walkable and a nonwalkable neighborhood. They wore AMP 331 triaxial accelerometers and GeoLogger GPA tracking devices for 21 days. Data were analyzed using conventional statistics and high- resolution geographic image analysis, which focused on a) path length, b) path duration, c) number of GPS reporting points, and d) interaction between distances and time. Results: Average steps by subjects ranged from 1810–10,453 steps per day (mean = 6899, SD = 3823). No statistical difference in walking behavior was found between neighborhoods (Walkable = 6710 ± 2781, Nonwalkable = 7096 ± 4674). Three environment parameters (ie, sidewalk, crosswalk, and path) were identiied for future ABM simulation. Conclusion: ABM should provide a better understanding of PA behavior’s interaction with the environment, as illustrated using a real-life example. PA ield should take advantage of ABM in future research. Keywords: GPS, mapping, environment, statistical modeling The impact of the environment, especially built environment, on physical activity (PA) participation has been well documented. 1–4 While measuring and tracking individual PA participants, and their interactions with the environment is possible using a combination of PA, global positioning system (GPS), and Geographic Information System (GIS) measures, modeling the environmental factors or correlates and their impact using traditional statistical methods is still a challenge. There are several reasons for this: 1. It is very dificult to correlate travel-related PA with the environment because these activities extend over both time and space. In the end, PA must be assigned to individual subjects, not to speciic locations. For example, a heavily used pedestrian bridge could be considered successful if it facilitated more walking; it would be less successful if it simply replaced an already heavily used crosswalk without having much effect on overall walking activity levels. The bridge is important only as a correlate of activity 2. It is dificult to quantitatively assess policy inter- vention strategies based on discovered correlates. For example, suppose that a survey reveals that both crossing major roads and lack of sidewalks are important inhibitors of pedestrians. In a typi- cal urban setting with hundreds of dangerous road crossings and miles of thoroughfares with no or poor sidewalks, the question becomes which speciic projects will produce the greatest yield in terms of increased pedestrian use. Commonly used correla- tional statistical methods are not appropriate because of the cluster nature of the data (ie, participants from a neighborhood are not independent of each other). As a result, Type I errors in statistical analysis are often heightened 5 3. The statistical methods that can take clustered data into consideration in the data analysis (eg, the hierarchical linear model, HLM) 5 assume that PA participants are limited in macro units (eg, neighbor- hood) being studied. This assumption is often not true: a person who lives in walkable neighborhood in the suburb may walk very little if he/she spends most of their time in the city or a place where there are no sidewalks or it is not safe to walk Official Journal of ISPAH www.JPAH-Journal.com ORIGINAL RESEARCH