Built environment – activity patterns model

The first arrow (i) of the model in figure 3-1 characterizes the behavioral component of our framework, linking the built environment to activity patterns. Figure 3-2. displays a more detailed model of this relationship. In this conceptualization, the density and mix of land uses such as residences, commerce, shopping, and restaurants, determine where an individual will undertake activities throughout the day, given a pre-determined activity pattern. This represents a

ii iv v v iii i

Built

Environment

Activity Patterns

(transportation and

physical activity)

Exposures

(air pollution and

traffic crashes)

Healthy

Behavior

Adverse

Health Effects

Net Health Effects

Air

Pollution

Vehicular

simplification in that in actuality the availability of, and destination to, different locations may influence the activity pattern itself. That withstanding, the relationship holds that one is more likely to choose a location that is closer to the origin of the trip or to the home location compared to other locations, and a location that has a high amount of the sought good or service compared to other locations. In other words there is an inverse relationship between distance to a location and probability of choosing that location, and a direct relationship between the density of the entity representing the purpose of the trip (such as number of employees for employment, or number of shops for shopping trips) and the choice of location.

Figure 3-2 Built environment – activity patterns model

An other conceptual approach to link the built environment to activity patterns could be in the vein of activity based models which account for opportunities and constraints afforded by the physical infrastructure to predict simultaneously which activities are undertaken, in what order, where, at what time, and with what travel mode. These models are developed using local samples and applying principles of utility maximization or decision heuristics. A review of such models can be found for example in Veldhuisen et al. (2000).

ACTIVITY PATTERNS

BUILT ENVIRONMENT

Land use factors:

- Mix of land uses (residences,

commerce, shops, etc)

- Density of uses (residential,

employment, etc)

- Parks

Transportation and design factors:

- Route directness (cul-de-sac, grid

street pattern, etc.)

- Sidewalks

- Trails, paths

- Ease of street crossing

Location of activities

(work, shopping, etc.)

Mode choice

- motorized

- non-motorized

Physical activity behavior

- Recreation

In terms of mode choice, theoretical and empirical research holds that the more direct the route is, the more sidewalks, trails and paths there are, and the safer the street crossings are, the more likely one is to walk or bike rather than used motorized vehicles. Again, this is a reduction of the complexity of the phenomena, and these elements have not consistently been found to be as significant as other features of the built environment. However, all indicators of the built

environment are simplifications of the reality, with certain factors masking or acting as a surrogate for others – for instance a gridded street pattern with sidewalks may in fact denote a pre-world war II traditional neighborhood, implying other determinants of behavior such as interesting diverse architecture and tree coverage. The choice of indicators for a quantitative application will necessarily depend not only on their common presence in non-motorized travel behavior research, but also on their relative simplicity for data collection and policy

implementation.

In addition to travel-related physical activity behavior (‘active travel’), the built

environment may also increase leisure time physical activity (‘active recreation’). The presence of opportunities for outdoor leisure activity such as parks, trails, paths, and also neighborhood streets with sidewalks, increase the likelihood of recreational physical activity.

In both cases of active leisure or active travel, the literature reveals much more abundant evidence of a “static” (cross-sectional) effect of the built environment on behavior than on the potential changes in individual behavior following neighborhood transformations. However, since in both fields the little longitudinal or quasi-experimental type of studies do tend to indicate a potential change in physical activity bheavior, this assumption is adopted in this research. The uncertainty associated with this step, however, must be noted.

Socio-demographics and other personal factors are also shown to influence activity patterns and mode choice. Hence the chosen approach must match local population composition to characteristics of individuals in the activity database (or other source of activity data) such as gender, age, or income, to moderate; these factors may then be used as moderators of the built

environment effect on travel choices. An important consideration for the particular interest in non-motorized transportation is the health status of individuals. Indeed, it is possible that individuals with compromised health would be less likely to walk or bike to go places. Conversely, active travel behavior is likely to be more beneficial to individuals with certain health ailments, such obesity, diabetes, and cardio-vascular conditions. Therefore including such circumstances in the model would be extremely relevant for the scope of this work. However, the availability of data linking health status, activity patterns, travel mode choice, and the built environment effect on activities is poor to inexistent. Moreover, predicting changes in behaviors as a result of changes in the built environment is a difficult proposition to begin with, and it becomes prohibitively uncertain when considering specific populations such as sedentary, diabetic, or obese people.