Geography Reference
In-Depth Information
5.4.1
Cross-Sectional Designs Examining the “Association”
Relationship
Cross-sectional designs provide continuous empirical studies to quantify the relation
between the built environment and walking behavior by comparing different neigh-
borhood types. The examination of the relationship usually runs in the following
kinds of regression analysis. (1) Objective measures regress on walking behavior:
most studies in transportation are based on this method, especially with the aid
of GIS. (2) Subjective measures regress on walking behavior: most studies in
public health adopt this method. (3) “Objective and subjective” measures regress
on walking behavior: First, both objective and subjective variables are employed
to run the regression, respectively. The two measures are then combined to check
if the predictability can be improved. This method identifies whether the objective
measure, subjective measure or their combination explains the walking behavior
more effectively. (4) “Spatial distortions” and walking behavior: Analyses explore
the “mismatch” between the objective measures and their subjective counterparts
and the effect of this mismatch on walking behavior outcomes (Gebel et al.
2009
).
The regression results usually show that the built environment has a significant
effect on individuals' walking behavior. However, in reality, it is only statistically
significant rather than remarkable and the correlation coefficient is relatively
small. In addition, although the cross-sectional design is effective in controlling
potentially confounding effects (e.g. socio-economic or socio-demographic), it
poses challenges in identifying the causal mechanisms involved.
5.4.2
Longitudinal Designs Examining the “Causality”
Relationship
Several longitudinal design studies have also been conducted. Krizek (
2003
)uses
longitudinal household travel data in Seattle to examine the relationship between
changes in neighborhood forms and household travel behavior. The results show
that in controlling for changes in lifestyle, relocating households to neighborhoods
with more accessibility could effectively reduce vehicle miles traveled, but it
has no significant effect on trip generation or mode splitting (Krizek
2003
). Cao
et al. (
2007
) examine the relationship between the residential environment and
non-work travel frequencies by automobile, transit and walk/bicycle modes in
Northern California. Their study uses quasi-longitudinal data from 547 movers and
assumes the movers' residential preferences and travel attitudes remain constant
(Cao et al.
2007
). Through a structural equation model, they detect more promising
neighborhood characteristic effects than those found in previous studies after
controlling for “self-selection.” However, the reliability of the movers' memories
leaves room for argument.
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