Research and practice method
A Spatial Agent-Based Model for the Simulation of Adults' Daily Walking Within a City

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Abstract

Environmental effects on walking behavior have received attention in recent years because of the potential for policy interventions to increase population levels of walking. Most epidemiologic studies describe associations of walking behavior with environmental features. These analyses ignore the dynamic processes that shape walking behaviors. A spatial agent-based model (ABM) was developed to simulate people's walking behaviors within a city. Each individual was assigned properties such as age, SES, walking ability, attitude toward walking and a home location. Individuals perform different activities on a regular basis such as traveling for work, for basic needs, and for leisure. Whether an individual walks and the amount she or he walks is a function of distance to different activities and her/his walking ability and attitude toward walking. An individual's attitude toward walking evolves over time as a function of past experiences, walking of others along the walking route, limits on distances walked per day, and attitudes toward walking of the other individuals within her/his social network. The model was calibrated and used to examine the contributions of land use and safety to socioeconomic differences in walking. With further refinement and validation, ABMs may help to better understand the determinants of walking and identify the most promising interventions to increase walking.

Introduction

Environmental effects on walking have received increasing attention as a strategy to increase population levels of physical activity.1, 2, 3 Built environment characteristics found to be associated with walking include density of residents, land use mix, features of street design, and aesthetics.1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 Greater safety, less violence, and greater social support for walking have also been found to be positively associated with walking.16, 17, 18, 19

The majority of existing research has applied statistical models to observational data to estimate the associations of environmental characteristics with walking after controls for confounding variables. A limitation of this approach is the inability to completely account for the selection of people into neighborhoods.20, 21 Another important limitation is that statistical models are unable to fully capture the dynamic set of relationships (including feedback loops and dynamic interactions among individuals, among environments, and between individuals and environments) through which environmental and personal attributes interact to shape walking behavior. Because population levels of walking emerge from the functioning of a system with various interacting components, the identification of the possible effects of a given policy or intervention requires understanding of the functioning of the system as a whole.

Agent-based models (ABMs) have received increasing attention as tools to investigate how dynamic processes shape the distribution of health outcomes, including the ways in which physical and social environments contribute to health-related behaviors.22, 23 An ABM is a computational model that can be used to simulate the actions and interactions of agents as well as the dynamic interactions between agents and their environments in order to gain understanding of the functioning of the system.24, 25 These models can be used to investigate the impact of policy alternatives in the presence of nonlinear relationships and feedbacks. ABMs have been used to investigate the transmission of infectious diseases, the determinants of drinking and drug use, and the effects of healthy food availability on diet.22, 26, 27, 28 Although ABMs have been used to study pedestrian movement29, 30, 31, 32 and there have been calls for greater use in the study of environmental effects on walking,33, 34 applications in public health are still scarce.

An exploratory spatial ABM was developed to simulate people's walking behavior within a hypothetic city. The model was calibrated against existing population data. The model was then used to investigate how the spatial patterning of built and social environments (specifically land use and safety as illustrative examples) contributes to social inequalities in walking in the context of residential segregation by SES.

Section snippets

Model Development

The model was developed in Java and Repast. It is a time-discrete model with each time step being 1 day. For parsimony, the model includes adults on working days only (no weekends), seasonal variations and weather are ignored, a public transportation network is not included, and each individual is assumed to have a car.

The model represents a city of 64 km2, comparable in size to the city of Ann Arbor MI. It is an 800×800 grid space, where each cell of size 10 m×10 m can be occupied by a

Model Assessment and Results

Table 2 shows similarities between NHTS data and model predictions for the specific distributional characteristics used in the calibration. As expected from the calibration and the assumption in the model, for both NHTS data and the calibrated model, 35%–40% of people do not walk, most people (>60%) walk no more than three times a week, and only 10%–20% walk more than seven times a week. The distribution of the distances of walking trips is highly skewed for both NHTS and model predictions:

Discussion

By incorporating feedback mechanisms that allowed individuals to alter their walking behaviors in response to their social networks, their own previous experiences, and the prevalence of walking they encountered, this relatively simple model was able to generate patterns of walking behaviors that have been observed in empirical studies. The model allowed for feedbacks over time from both built and social environment features. In addition, walking for one purpose had effects on walking for other

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