Extending Vancouver's Public Bike Share system: a GIS analysis
FACTOR SELECTION
Of the list of items influencing the choice to cycle provided in the introduction and based on Winters et al. (2011), Vandenbulcke et al. (2011) and Rietveld and Daniel (2004), only some were viable to include in this analysis. A smaller selection of factors was then chosen based on their ability to represent these items. The list of factors used in the multi-criteria analysis are provided in the figure below, along with the associated motivators/deterrents (as discussed in the introduction) or, for the social factors, the rationale for their inclusion. Factors discussed in the introduction that were not included in the analysis are provided below, along with an explanation for their not being included.
Included Factors
Factors chosen for inclusion in analysis in brown. Justification for their inclusion in yellow.
Factors Not Included
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Factors related to weather:
Weather is not considered in this analysis because it is relatively consistent across the city in terms of space and is highly variable temporally.
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Immeasurable, personal factors:
Cycling determinants such as the need to carry heavy or bulky items or wanting to make the trip in daylight are highly variable from person to person and are not possible to incorporate into the GIS. The same holds true for trip distance, since this will vary from trip to trip, and also to some degree for speed of commute, since this depends much on physical fitness of the individual.
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Whether there is secure bike storage at one’s destination:
This factor is not applicable to this analysis since the PBS serves as bike storage itself.
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Factors for which data was not available:
Although it is possible to map factors such as the degree to which a certain route is illuminated after dark or the bike paths that have separated lanes, this data could not be found. Health level, though highly correlated with cycling, is difficult to measure and therefore there is a lack of data.
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Gender, income and education:
Gender was excluded in this analysis because of a presumed consistency across the city. Income and education level are not included in this analysis not because they are irrelevant to the further implementation of a PBS, but because there is a complicated relationship between income, education and rates of cycling. Personal income is typically positively correlated with one’s education level. Yet in Belgium lower income (and thus lower education) is associated with cycling (Vandenbulcke et al., 2011), whereas in some U.S. cities higher education (and thus higher income) is associated with cycling (Xing, Handy and Buehler, 2010). Since the relationship between the two for Vancouver was unknown, both factors were left out.