Our Walkability Algorithm             


The two concepts of walkability and breast cancer were separated to dig PROJECT STEPS deeper into the particularities of each that would contribute to this research. Walking was broken down into two purposes: walking for leisure and walking for commuting or walking.

Studies show that the most physically active adults in walkable neighborhoods walk to commute compared to those living in lesser walkable neighborhoods (University of British Columbia, 2010). Furthermore, parks and open spaces encourage this form of walking greatly (University of British Columbia, 2010). Another study shows that measures of walkability must consider street connectivity, residential density and access to public modes of transportation (Carr et al, 2010). In turn, greater residential density and street connectivity support an increased use of public transportation (University of British Columbia, 2010). Studies show that a greater density of street intersections and a smaller average block length encourages walking as a form of commuting as well (Carr et al, 2010). Based on these articles, the following variables were used to calculate our walkability:

  • Land use mix: A neighbourhood with mixed land use encourages walking as a form of commuting.
  • Population density: Greater residential density supports higher use of public transportation and therefore results in more walking.
  • Road connectivity: Smaller block lengths, greater street connectivity result in increased walkability scores.
  • Distance to transit stop and parks: Closer distance to public transit stop and parks induce more walking activity.
  • Slope: People are more willing to walk on a flat street.

Once the variables were chosen, a multi-criteria evaluation (MCE) was chosen as the best GIS analytic tool to calculate a walkability score.

  • An MCE is a fundamental step of the rational decision-making process especially in cases when multiple variables must be evaluated together (Klinkenberg, 2007).

In this MCE each variable was standardized, given a weight according to its relational importance, and finally, all variable weights were combined to generate a final result. The weights were generated based on responses from client surveys and group discussion. The final result of our algorithm is a walkability score of Metro Vancouver Area with 1 representing a more walkable area and 5 representing a less walkable area.