A. Páez School of Geography and Earth Sciences

McMaster University

Chaire Mobilité 4e colloque annuel

AN INVESTIGATION OF THE ATTRIBUTES

OF WALKABLE ENVIRONMENTS

FROM THE PERSPECTIVE OF SENIORS

IN MONTREAL

Motivation

Physical inactivity is a key modifiable risk factor for many health conditions. (Warburton et al. 2006)

Participation in physical activity during leisure time has declined over time.

Active travel can complement other forms of physical activity and contribute to healthy lifestyles. (e.g. Frank

et al., 2010; Lee and Buchner, 2008; Pucher et al., 2010)

“Physical activity by stealth” (Brockman and Fox, 2011)

Motivation

For seniors walking is a suitable physical activity that places the right amount of stress in the joints.

More beneficial, even, than other types of physical activity.

Motivation

Physical activity behavior is influenced by factors along different domains. Demographic and biological.

Psychological.

Cognitive and emotional.

Social and cultural.

Behavioral attributes and skills.

Physical environment.

Physical activity characteristics.

Motivation

Physical (built) environment is perceived as a tractable policy variable. Macro-scale (urban form attributes)

Meso-scale (neighborhood attributes)

Micro-scale (street segment-pedestrian level attributes)

Research Question

What are the attributes of walkable environments at the street level segment? From the perspective of seniors.

In Montreal Island.

Approach

Walkability / Walking

Investigate walking behavior based on socio-economic, demographic, and macro- and meso-scale attributes of built environment.

Approach

Inspect variations between predicted and observed walking.

Conjecture: If NOT random, other attributes, e.g., micro-scale features.

Conduct walkability audits in selected sited (visual inspections of built environment)

Methods

Joint discrete-continuous model Discrete: mode choice (car, transit, walking)

Continuous: trip length.

Use results of mode choice model to estimate the probability of walking -> walking behavior.

Methods

Walking Predicted = Yes Predicted = No

Observed = Yes Under-predicted: more walkable?

Observed = No Over-predicted: less walkable?

Methods

Cluster analysis to identify sub-regions where walking is more or less common than the model predicts.

Using spatial scan statistic.

Selection of sites for audits

Results

Attributes of built environments associated with more walking Items at segment’s intersection (Crossing

areas marked, four way intersections)

Sidewalk and amenities (completeness, connectivity, slope, lights)

Land uses (land use mixture, mix of vertical built environment, gathering places: coffee shops)

Examples: more walkable

Examples: more walkable

Examples: more walkable

Examples: less walkable

Examples: less walkable

Concluding remarks

Building a knowledge base regarding streetscapes and walkability/walking

Results are intuitive

Potential for low cost-high impact interventions

Acknowledgements

Md Moniruzzaman, Ph.D. Candidate, McMaster U.

Canadian Institutes for Health Research (Emerging Team Grant, PI Heather McKay)

Prof. Zachary Patterson (Concordia U.)

Technical Committee on OD Survey